Congyu Liao

All Publications

• Data- and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies. IEEE reviews in biomedical engineering Huang, J., Wu, Y., Wang, F., Fang, Y., Nan, Y., Alkan, C., Abraham, D., Liao, C., Xu, L., Gao, Z., Wu, W., Zhu, L., Chen, Z., Lally, P., Bangerter, N., Setsompop, K., Guo, Y., Rueckert, D., Wang, G., Yang, G. 2024; PP

  Abstract

  Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.

  View details for DOI 10.1109/RBME.2024.3485022

  View details for PubMedID 39437302

• Spherical echo-planar time-resolved imaging (sEPTI) for rapid 3D quantitative T 2 * and susceptibility imaging. Magnetic resonance in medicine Wang, N., Liao, C., Cao, X., Nishimura, M., Brackenier, Y. W., Yurt, M., Gao, M., Abraham, D., Alkan, C., Iyer, S. S., Zhou, Z., Jeong, H., Kerr, A., Haldar, J. P., Setsompop, K. 2024

  Abstract

  To develop a 3D spherical EPTI (sEPTI) acquisition and a comprehensive reconstruction pipeline for rapid high-quality whole-brain submillimeter T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification.For the sEPTI acquisition, spherical k-space coverage is utilized with variable echo-spacing and maximum kx ramp-sampling to improve efficiency and signal incoherency compared to existing EPTI approaches. For reconstruction, an iterative rank-shrinking B0 estimation and odd-even high-order phase correction algorithms were incorporated into the reconstruction to better mitigate artifacts from field imperfections. A physics-informed unrolled network was utilized to boost the SNR, where 1-mm and 0.75-mm isotropic whole-brain imaging were performed in 45 and 90 s at 3 T, respectively. These protocols were validated through simulations, phantom, and in vivo experiments. Ten healthy subjects were recruited to provide sufficient data for the unrolled network. The entire pipeline was validated on additional five healthy subjects where different EPTI sampling approaches were compared. Two additional pediatric patients with epilepsy were recruited to demonstrate the generalizability of the unrolled reconstruction.sEPTI achieved 1.4 × $$ \times $$ faster imaging with improved image quality and quantitative map precision compared to existing EPTI approaches. The B0 update and the phase correction provide improved reconstruction performance with lower artifacts. The unrolled network boosted the SNR, achieving high-quality T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification with single average data. High-quality reconstruction was also obtained in the pediatric patients using this network.sEPTI achieved whole-brain distortion-free multi-echo imaging and T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification at 0.75 mm in 90 s which has the potential to be useful for wide clinical applications.

  View details for DOI 10.1002/mrm.30255

  View details for PubMedID 39250435

• AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI. IEEE transactions on medical imaging Alkan, C., Mardani, M., Liao, C., Li, Z., Vasanawala, S. S., Pauly, J. M. 2024; PP

  Abstract

  Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R = 5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

  View details for DOI 10.1109/TMI.2024.3443292

  View details for PubMedID 39146168

• Artificial intelligence for neuro MRI acquisition: a review. Magma (New York, N.Y.) Yang, H., Wang, G., Li, Z., Li, H., Zheng, J., Hu, Y., Cao, X., Liao, C., Ye, H., Tian, Q. 2024

  Abstract

  OBJECT: To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts.MATERIALS AND METHODS: A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.RESULTS: The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency.DISCUSSION: The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

  View details for DOI 10.1007/s10334-024-01182-7

  View details for PubMedID 38922525

• High-Fidelity Intravoxel Incoherent Motion Parameter Mapping Using Locally Low-Rank and Subspace Modeling. NeuroImage Finkelstein, A. J., Liao, C., Cao, X., Mani, M., Schifitto, G., Zhong, J. 2024: 120601

  Abstract

  Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods.To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise.Our proposed method reduced noise contamination in simulations, resulting in a 60 %, 58.9 %, and 83.9 % reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p=0.0210), which wasn't observed with the conventional reconstruction.The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

  View details for DOI 10.1016/j.neuroimage.2024.120601

  View details for PubMedID 38588832

• Uncovering microstructural architecture from histology. bioRxiv : the preprint server for biology Georgiadis, M., Auf der Heiden, F., Abbasi, H., Ettema, L., Nirschl, J., Taghavi, H. M., Wakatsuki, M., Liu, A., Ho, W. H., Carlson, M., Doukas, M., Koppes, S. A., Keereweer, S., Sobel, R. A., Setsompop, K., Liao, C., Amunts, K., Axer, M., Zeineh, M., Menzel, M. 2024

  Abstract

  Microstructural tissue organization underlies the complex connectivity of the brain and controls properties of connective, muscle, and epithelial tissue. However, discerning microstructural architecture with high resolution for large fields of view remains prohibitive. We address this challenge with computational scattered light imaging (ComSLI), which exploits the anisotropic light scattering of aligned structures. Using a rotating lightsource and a high-resolution camera, ComSLI determines fiber architecture with micrometer resolution from histological sections across preparation and staining protocols. We show complex fiber architecture in brain and non-brain sections, including histological paraffin-embedded sections with various stains, and demonstrate its applicability on animal and human tissue, including disease cases with altered microstructure. ComSLI opens new avenues for investigating fiber architecture in new and archived sections across organisms, tissues, and diseases.One Sentence Summary: We uncover microstructural architecture of new or archived human and animal histological sections in health and disease.

  View details for DOI 10.1101/2024.03.26.586745

  View details for PubMedID 38585744

• Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI. Magnetic resonance in medicine Liu, Q., Ning, L., Shaik, I. A., Liao, C., Gagoski, B., Bilgic, B., Grissom, W., Nielsen, J. F., Zaitsev, M., Rathi, Y. 2024

  Abstract

  To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform.We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD).Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences.The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

  View details for DOI 10.1002/mrm.30062

  View details for PubMedID 38469671

• The Potential of Phase Constraints for Non-Fourier Radiofrequency-Encoded MRI. IEEE transactions on computational imaging Liu, Y., Liao, C., Setsompop, K., Haldar, J. P. 2024; 10: 223-232

  Abstract

  In modern magnetic resonance imaging, it is common to use phase constraints to reduce sampling requirements along Fourier-encoded spatial dimensions. In this work, we investigate whether phase constraints might also be beneficial to reduce sampling requirements along spatial dimensions that are measured using non-Fourier encoding techniques, with direct relevance to approaches that use tailored spatially-selective radiofrequency (RF) pulses to perform spatial encoding along the slice dimension in a 3D imaging experiment. In the first part of the paper, we use the Cramér-Rao lower bound to examine the potential estimation theoretic benefits of using phase constraints. The results suggest that phase constraints can be used to improve experimental efficiency and enable acceleration, but only if the RF encoding matrix is complex-valued and appropriately designed. In the second part of the paper, we use simulations of RF-encoded data to test the benefits of phase constraints combined with optimized RF-encodings, and find that the theoretical benefits are indeed borne out empirically. These results provide new insights into the potential benefits of phase constraints for RF-encoded data, and provide a solid theoretical foundation for future practical explorations.

  View details for DOI 10.1109/tci.2024.3361372

  View details for PubMedID 39280790

  View details for PubMedCentralID PMC11394734

• Rapid and accurate navigators for motion and B0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators. Magnetic resonance in medicine Brackenier, Y., Wang, N., Liao, C., Cao, X., Schauman, S., Yurt, M., Cordero-Grande, L., Malik, S. J., Kerr, A., Hajnal, J. V., Setsompop, K. 2024

  Abstract

  To develop a framework that jointly estimates rigid motion and polarizing magnetic field (B0 ) perturbations ( δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ ) for brain MRI using a single navigator of a few milliseconds in duration, and to additionally allow for navigator acquisition at arbitrary timings within any type of sequence to obtain high-temporal resolution estimates.Methods exist that match navigator data to a low-resolution single-contrast image (scout) to estimate either motion or δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . In this work, called QUEEN (QUantitatively Enhanced parameter Estimation from Navigators), we propose combined motion and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimation from a fast, tailored trajectory with arbitrary-contrast navigator data. To this end, the concept of a quantitative scout (Q-Scout) acquisition is proposed from which contrast-matched scout data is predicted for each navigator. Finally, navigator trajectories, contrast-matched scout, and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ are integrated into a motion-informed parallel-imaging framework.Simulations and in vivo experiments show the need to model δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ to obtain accurate motion parameters estimated in the presence of strong δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Simulations confirm that tailored navigator trajectories are needed to robustly estimate both motion and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Furthermore, experiments show that a contrast-matched scout is needed for parameter estimation from multicontrast navigator data. A retrospective, in vivo reconstruction experiment shows improved image quality when using the proposed Q-Scout and QUEEN estimation.We developed a framework to jointly estimate rigid motion parameters and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ from navigators. Combing a contrast-matched scout with the proposed trajectory allows for navigator deployment in almost any sequence and/or timing, which allows for higher temporal-resolution motion and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimates.

  View details for DOI 10.1002/mrm.29976

  View details for PubMedID 38173304

• Polynomial Preconditioners for Regularized Linear Inverse Problems. SIAM Journal on Imaging Sciences Iyer, S. S., Ong, F., Cao, X., Liao, C., Daniel, L., Tamir, J. I., Setsompop, K. 2024; 17:1 : 116-146

  View details for DOI 10.1137/22M1530355

• High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting. Magnetic resonance in medicine Liao, C., Cao, X., Iyer, S. S., Schauman, S., Zhou, Z., Yan, X., Chen, Q., Li, Z., Wang, N., Gong, T., Wu, Z., He, H., Zhong, J., Yang, Y., Kerr, A., Grill-Spector, K., Setsompop, K. 2023

  Abstract

  This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1 , T2 , and proton-density (PD), all within a clinically feasible scan time.We developed 3D visualization of short transverse relaxation time component (ViSTa)-MRF, which combined ViSTa technique with MR fingerprinting (MRF), to achieve high-fidelity whole-brain MWF and T1 /T2 /PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling spiral-projection acquisition and joint spatial-temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF mapping was achieved without a need for multicompartment fitting that could introduce bias and/or noise from additional assumptions or priors.The in vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi-parametric mapping with high SNR and good quality. The in vivo results of 1 mm- and 0.66 mm-isotropic resolution datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30× slower with lower SNR. Furthermore, we applied the proposed method to enable 5-min whole-brain 1 mm-iso assessment of MWF and T1 /T2 /PD mappings for infant brain development and for post-mortem brain samples.In this work, we have developed a 3D ViSTa-MRF technique that enables the acquisition of whole-brain MWF, quantitative T1 , T2 , and PD maps at 1 and 0.66 mm isotropic resolution in 5 and 15 min, respectively. This advancement allows for quantitative investigations of myelination changes in the brain.

  View details for DOI 10.1002/mrm.29990

  View details for PubMedID 38156945

• Next-generation MRI scanner designed for ultra-high-resolution human brain imaging at 7 Tesla. Nature methods Feinberg, D. A., Beckett, A. J., Vu, A. T., Stockmann, J., Huber, L., Ma, S., Ahn, S., Setsompop, K., Cao, X., Park, S., Liu, C., Wald, L. L., Polimeni, J. R., Mareyam, A., Gruber, B., Stirnberg, R., Liao, C., Yacoub, E., Davids, M., Bell, P., Rummert, E., Koehler, M., Potthast, A., Gonzalez-Insua, I., Stocker, S., Gunamony, S., Dietz, P. 2023

  Abstract

  To increase granularity in human neuroimaging science, we designed and built a next-generation 7 Tesla magnetic resonance imaging scanner to reach ultra-high resolution by implementing several advances in hardware. To improve spatial encoding and increase the image signal-to-noise ratio, we developed a head-only asymmetric gradient coil (200 mT m-1, 900 T m-1s-1) with an additional third layer of windings. We integrated a 128-channel receiver system with 64- and 96-channel receiver coil arrays to boost signal in the cerebral cortex while reducing g-factor noise to enable higher accelerations. A 16-channel transmit system reduced power deposition and improved image uniformity. The scanner routinely performs functional imaging studies at 0.35-0.45 mm isotropic spatial resolution to reveal cortical layer functional activity, achieves high angular resolution in diffusion imaging and reduces acquisition time for both functional and structural imaging.

  View details for DOI 10.1038/s41592-023-02068-7

  View details for PubMedID 38012321

  View details for PubMedCentralID 2492463

• DTI-MR fingerprinting for rapid high-resolution whole-brain T1 , T2 , proton density, ADC, and fractional anisotropy mapping. Magnetic resonance in medicine Cao, X., Liao, C., Zhou, Z., Zhong, Z., Li, Z., Dai, E., Iyer, S. S., Hannum, A. J., Yurt, M., Schauman, S., Chen, Q., Wang, N., Wei, J., Yan, Y., He, H., Skare, S., Zhong, J., Kerr, A., Setsompop, K. 2023

  Abstract

  This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1 , T2 , proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time.To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design.The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence.The proposed method can achieve whole-brain T1 , T2 , diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.

  View details for DOI 10.1002/mrm.29916

  View details for PubMedID 37936313

• High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. NeuroImage Liao, C., Yarach, U., Cao, X., Iyer, S. S., Wang, N., Kim, T. H., Tian, Q., Bilgic, B., Kerr, A. B., Setsompop, K. 2023: 120168

  Abstract

  To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution.We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720μm and 500μm isotropic resolution in-vivo diffusion MRI.Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720μm and 500μm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches.The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500μm-isotropic-resolution compared to standard multi-shot EPI.

  View details for DOI 10.1016/j.neuroimage.2023.120168

  View details for PubMedID 37187364

• Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. bioRxiv : the preprint server for biology Iyer, S. S., Schauman, S. S., Sandino, C. M., Yurt, M., Cao, X., Liao, C., Ruengchaijatuporn, N., Chatnuntawech, I., Tong, E., Setsompop, K. 2023

  Abstract

  Introduction: Spatio-temporal MRI methods enable whole-brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice. Deep learning (DL) is a promising approach to accelerate reconstruction, but can be computationally intensive to train and deploy due to the large dimensionality of spatio-temporal MRI. DL methods also need large training data sets and can produce results that don't match the acquired data if data consistency is not enforced. The aim of this project is to reduce reconstruction time using DL whilst simultaneously limiting the risk of deep learning induced hallucinations, all with modest hardware requirements.Methods: Deep Learning Initialized Compressed Sensing (Deli-CS) is proposed to reduce the reconstruction time of iterative reconstructions by "kick-starting" the iterative reconstruction with a DL generated starting point. The proposed framework is applied to volumetric multi-axis spiral projection MRF that achieves whole-brain T1 and T2 mapping at 1-mm isotropic resolution for a 2-minute acquisition. First, the traditional reconstruction is optimized from over two hours to less than 40 minutes while using more than 90% less RAM and only 4.7 GB GPU memory, by using a memory-efficient GPU implementation. The Deli-CS framework is then implemented and evaluated against the above reconstruction.Results: Deli-CS achieves comparable reconstruction quality with 50% fewer iterations bringing the full reconstruction time to 20 minutes.Conclusion: Deli-CS reduces the reconstruction time of subspace reconstruction of volumetric spatio-temporal acquisitions by providing a warm start to the iterative reconstruction algorithm.

  View details for DOI 10.1101/2023.03.28.534431

  View details for PubMedID 37034586

• 3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution T 2 * mapping. Magnetic resonance in medicine Chen, Z., Liao, C., Cao, X., Poser, B. A., Xu, Z., Lo, W. C., Wen, M., Cho, J., Tian, Q., Wang, Y., Feng, Y., Xia, L., Chen, W., Liu, F., Bilgic, B. 2023

  Abstract

  This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping.3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction.Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. For T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis.The proposed technique enables rapid 3D distortion-free high-resolution imaging and T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping in 47 s at 1.1 × 1.1 × 1.0 mm3 resolution.

  View details for DOI 10.1002/mrm.29578

  View details for PubMedID 36705076

• Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping. Human brain mapping Zhou, Z., Li, Q., Liao, C., Cao, X., Liang, H., Chen, Q., Pu, R., Ye, H., Tong, Q., He, H., Zhong, J. 2023

  Abstract

  Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.

  View details for DOI 10.1002/hbm.26203

  View details for PubMedID 36629336

• Synergistic Hankel Structured Low-Rank Approximation With Total Variation Regularization for Complex Magnetic Anomaly Detection IEEE Transactions on Instrumentation and Measurement Liu, H., Zhang, X., Liao, C., Dong, H., Liu, Z., Hu, X. 2023; 72

  View details for DOI 10.1109/TIM.2023.3264036

• SLfRank: Shinnar-Le-Roux Pulse Design with Reduced Energy and Accurate Phase Profiles using Rank Factorization. IEEE transactions on medical imaging Ong, F., Zhong, Z., Liao, C., Lustig, M., Vasanawala, S. S., Pauly, J. M. 2022; PP

  Abstract

  The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (by as much as 26%) and more accurate phase profiles. Concretely, the SLR algorithm consists of two steps: (1) an invertible transform between frequency selective pulses and polynomial pairs that represent Cayley-Klein (CK) parameters and (2) the design of the CK polynomial pair to match the desired magnetization profiles. Because the CK polynomial pair is bi-linearly coupled, the original algorithm sequentially solves for each polynomial instead of jointly. This results in sub-optimal pulses. Instead, we leverage a convex relaxation technique, commonly used for low rank matrix recovery, to address the bilinearity. Our numerical experiments show that the resulting pulses are almost always globally optimal in practice. For slice excitation, the proposed algorithm results in more accurate linear phase profiles. And in general the improved pulses have lower energy than the original SLR pulses.

  View details for DOI 10.1109/TMI.2022.3231782

  View details for PubMedID 37015710

• Detecting Silent Acute Microinfarcts in Cerebral Small Vessel Disease Using Submillimeter Diffusion-Weighted Magnetic Resonance Imaging: Preliminary Results. Stroke Misquitta, K., Daou, M., Conklin, J., Liao, C., Setsompop, K., Poublanc, J., Shirzadi, Z., MacIntosh, B. J., Tomlinson, G., Cohn, M., Aviv, R. I., Silver, F. L., Mandell, D. M. 2022: 101161STROKEAHA122039723

  View details for DOI 10.1161/STROKEAHA.122.039723

  View details for PubMedID 35695007

• Highly accelerated EPI with wave encoding and multi-shot simultaneous multislice imaging. Magnetic resonance in medicine Cho, J., Liao, C., Tian, Q., Zhang, Z., Xu, J., Lo, W. C., Poser, B. A., Stenger, V. A., Stockmann, J., Setsompop, K., Bilgic, B. 2022

  Abstract

  To introduce wave-encoded acquisition and reconstruction techniques for highly accelerated EPI with reduced g-factor penalty and image artifacts.Wave-EPI involves application of sinusoidal gradients during the EPI readout, which spreads the aliasing in all spatial directions, thereby taking better advantage of 3D coil sensitivity profiles. The amount of voxel spreading that can be achieved by the wave gradients during the short EPI readout period is constrained by the slew rate of the gradient coils and peripheral nerve stimulation monitor. We propose to use a "half-cycle" sinusoidal gradient to increase the amount of voxel spreading that can be achieved while respecting the slew and stimulation constraints. Extending wave-EPI to multi-shot acquisition minimizes geometric distortion and voxel blurring at high in-plane resolutions, while structured low-rank regularization mitigates shot-to-shot phase variations. To address gradient imperfections, we propose to use different point spread functions for the k-space lines with positive and negative polarities, which are calibrated with a FLEET-based reference scan.Wave-EPI enabled whole-brain single-shot gradient-echo (GE) and multi-shot spin-echo (SE) EPI acquisitions at high acceleration factors at 3T and was combined with g-Slider encoding to boost the SNR level in 1 mm isotropic diffusion imaging. Relative to blipped-CAIPI, wave-EPI reduced average and maximum g-factors by up to 1.21- and 1.37-fold at Rin  × Rsms  = 3 × 3, respectively.Wave-EPI allows highly accelerated single- and multi-shot EPI with reduced g-factor and artifacts and may facilitate clinical and neuroscientific applications of EPI by improving the spatial and temporal resolution in functional and diffusion imaging.

  View details for DOI 10.1002/mrm.29291

  View details for PubMedID 35678236

• Blip up-down acquisition for spin- and gradient-echo imaging (BUDA-SAGE) with self-supervised denoising enables efficient T2 , T2 *, para- and dia-magnetic susceptibility mapping. Magnetic resonance in medicine Zhang, Z., Cho, J., Wang, L., Liao, C., Shin, H. G., Cao, X., Lee, J., Xu, J., Zhang, T., Ye, H., Setsompop, K., Liu, H., Bilgic, B. 2022

  Abstract

  To rapidly obtain high resolution T2 , T2 *, and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity.We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient EPI sequence for quantitative mapping. The acquisition includes multiple T2 *-, T2 '-, and T2 -weighted contrasts. We alternate the phase-encoding polarities across the interleaved shots in this multi-shot navigator-free acquisition. A field map estimated from interim reconstructions was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to eliminate distortion. A self-supervised neural network (NN), MR-Self2Self (MR-S2S), was used to perform denoising to boost SNR. Using Slider encoding allowed us to reach 1 mm isotropic resolution by performing super-resolution reconstruction on volumes acquired with 2 mm slice thickness. Quantitative T2 (=1/R2 ) and T2 * (=1/R2 *) maps were obtained using Bloch dictionary matching on the reconstructed echoes. QSM was estimated using nonlinear dipole inversion on the gradient echoes. Starting from the estimated R2 /R2 * maps, R2 ' information was derived and used in source separation QSM reconstruction, which provided additional para- and dia-magnetic susceptibility maps.In vivo results demonstrate the ability of BUDA-SAGE to provide whole-brain, distortion-free, high-resolution, multi-contrast images and quantitative T2 /T2 * maps, as well as yielding para- and dia-magnetic susceptibility maps. Estimated quantitative maps showed comparable values to conventional mapping methods in phantom and in vivo measurements.BUDA-SAGE acquisition with self-supervised denoising and Slider encoding enables rapid, distortion-free, whole-brain T2 /T2 * mapping at 1 mm isotropic resolution under 90 s.

  View details for DOI 10.1002/mrm.29219

  View details for PubMedID 35436357

• Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. Magnetic resonance in medicine Cao, X., Liao, C., Iyer, S. S., Wang, Z., Zhou, Z., Dai, E., Liberman, G., Dong, Z., Gong, T., He, H., Zhong, J., Bilgic, B., Setsompop, K. 2022

  Abstract

  PURPOSE: To improve image quality and accelerate the acquisition of 3D MR fingerprinting (MRF).METHODS: Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low-rank constraint and a modified spiral-projection spatiotemporal encoding scheme called tiny golden-angle shuffling were implemented for rapid whole-brain high-resolution quantitative mapping. Reconstruction parameters such as the locally low-rank regularization parameter and the subspace rank were tuned using retrospective in vivo data and simulated examinations. B0 inhomogeneity correction using multifrequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect.RESULTS: The proposed MRF acquisition and reconstruction framework yields high-quality 1-mm isotropic whole-brain quantitative maps in 2min at better quality compared with 6-min acquisitions of prior approaches. The proposed method was validated to not induce bias in T1 and T2 mapping. High-quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4min using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures.CONCLUSIONS: The proposed tiny golden-angle shuffling, MRF with optimized spiral-projection trajectory and subspace reconstruction enables high-resolution quantitative mapping in ultrafast acquisition time.

  View details for DOI 10.1002/mrm.29194

  View details for PubMedID 35199877

• Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps. Magnetic resonance in medicine Yu, F. F., Huang, S. Y., Kumar, A., Witzel, T., Liao, C., Duval, T., Cohen-Adad, J., Bilgic, B. 2021

  Abstract

  PURPOSE: A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast parametric maps. We sought to reduce the acquisition time for QSM and macromolecular tissue volume by acquiring both contrasts simultaneously by leveraging their redundancies. The joint virtual coil concept with GRAPPA (JVC-GRAPPA) was applied to reduce acquisition time further.METHODS: Three adult volunteers were imaged on a 3 Tesla scanner using a multi-echo 3D GRE sequence acquired at 3 head orientations. Macromolecular tissue volume, QSM, R 2 , T1 , and proton density maps were reconstructed. The same sequence (GRAPPA R = 4) was performed in subject 1 with a single head orientation for comparison. Fully sampled data was acquired in subject 2, from which retrospective undersampling was performed (R = 6 GRAPPA and R = 9 JVC-GRAPPA). Prospective undersampling was performed in subject 3 (R = 6 GRAPPA and R = 9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes.RESULTS: Subject 1's multi-orientation and single-orientation macromolecular tissue volume maps were not significantly different based on RMSE. For subject 2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject 3. Using QSM, R 2 , and macromolecular tissue volume, the contributions of myelin and iron content to susceptibility were estimated.CONCLUSION: We have developed a novel strategy to simultaneously acquire data for the reconstruction of 5 intrinsically coregistered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 min using JVC-GRAPPA.

  View details for DOI 10.1002/mrm.28995

  View details for PubMedID 34480768

• Quantitative T1 and T2 mapping by magnetic resonance fingerprinting (MRF) of the placenta before and after maternal hyperoxia. Placenta Stout, J. N., Liao, C., Gagoski, B., Turk, E. A., Feldman, H. A., Bibbo, C., Barth, W. H., Shainker, S. A., Wald, L. L., Grant, P. E., Adalsteinsson, E. 2021; 114: 124-132

  Abstract

  INTRODUCTION: MR relaxometry has been used to assess placental exchange function, but methods to date are not sufficiently fast to be robust to placental motion. Magnetic resonance fingerprinting (MRF) permits rapid, voxel-wise, intrinsically co-registered T1 and T2 mapping. After characterizing measurement error, we scanned pregnant women during air and oxygen breathing to demonstrate MRF's ability to detect placental oxygenation changes.METHODS: The accuracy of FISP-based, sliding-window reconstructed MRF was tested on phantoms. MRF scans in 9-s breath holds were acquired at 3T in 31 pregnant women during air and oxygen breathing. A mixed effects model was used to test for changes in placenta relaxation times between physiological states, to assess the dependency on gestational age (GA), and the impact of placental motion.RESULTS: MRF estimates of known phantom relaxation times resulted in mean absolute errors for T1 of 92ms (4.8%), but T2 was less accurate at 16ms (13.6%). During normoxia, placental T1=1825±141ms (avg±standard deviation) and T2=60±16ms (gestational age range 24.3-36.7, median 32.6 weeks). In the statistical model, placental T2 rose and T1 remained contant after hyperoxia, and no GA dependency was observed for T1 or T2.DISCUSSION: Well-characterized, motion-robust MRF was used to acquire T1 and T2 maps of the placenta. Changes with hyperoxia are consistent with a net increase in oxygen saturation. Toward the goal of whole-placenta quantitative oxygenation imaging over time, we aim to implement 3D MRF with integrated motion correction to improve T2 accuracy.

  View details for DOI 10.1016/j.placenta.2021.08.058

  View details for PubMedID 34537569

• Efficient T2 mapping with blip-up/down EPI and gSlider-SMS (T2 -BUDA-gSlider). Magnetic resonance in medicine Cao, X., Wang, K., Liao, C., Zhang, Z., Srinivasan Iyer, S., Chen, Z., Lo, W., Liu, H., He, H., Setsompop, K., Zhong, J., Bilgic, B. 2021

  Abstract

  PURPOSE: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity.METHODS: A T2 blip-up/down EPI acquisition with generalized slice-dithered enhanced resolution (T2 -BUDA-gSlider) is proposed. A RF-encoded multi-slab spin-echo (SE) EPI acquisition with multiple TEs was developed to obtain high SNR efficiency with reduced TR. This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and used for T2 quantification.RESULTS: In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated.CONCLUSION: BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at ~1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications.

  View details for DOI 10.1002/mrm.28872

  View details for PubMedID 34046924

• A multi-inversion multi-echo spin and gradient echo echo planar imaging sequence with low image distortion for rapid quantitative parameter mapping and synthetic image contrasts MAGNETIC RESONANCE IN MEDICINE Manhard, M., Stockmann, J., Liao, C., Park, D., Han, S., Fair, M., van den Boomen, M., Polimeni, J., Bilgic, B., Setsompop, K. 2021

  Abstract

  Brain imaging exams typically take 10-20 min and involve multiple sequential acquisitions. A low-distortion whole-brain echo planar imaging (EPI)-based approach was developed to efficiently encode multiple contrasts in one acquisition, allowing for calculation of quantitative parameter maps and synthetic contrast-weighted images.Inversion prepared spin- and gradient-echo EPI was developed with slice-order shuffling across measurements for efficient acquisition with T1 , T2 , and T 2 ∗ weighting. A dictionary-matching approach was used to fit the images to quantitative parameter maps, which in turn were used to create synthetic weighted images with typical clinical contrasts. Dynamic slice-optimized multi-coil shimming with a B0 shim array was used to reduce B0 inhomogeneity and, therefore, image distortion by >50%. Multi-shot EPI was also implemented to minimize distortion and blurring while enabling high in-plane resolution. A low-rank reconstruction approach was used to mitigate errors from shot-to-shot phase variation.The slice-optimized shimming approach was combined with in-plane parallel-imaging acceleration of 4× to enable single-shot EPI with more than eight-fold distortion reduction. The proposed sequence efficiently obtained 40 contrasts across the whole-brain in just over 1 min at 1.2 × 1.2 × 3 mm resolution. The multi-shot variant of the sequence achieved higher in-plane resolution of 1 × 1 × 4 mm with good image quality in 4 min. Derived quantitative maps showed comparable values to conventional mapping methods.The approach allows fast whole-brain imaging with quantitative parameter maps and synthetic weighted contrasts. The slice-optimized multi-coil shimming and multi-shot reconstruction approaches result in minimal EPI distortion, giving the sequence the potential to be used in rapid screening applications.

  View details for DOI 10.1002/mrm.28761

  View details for Web of Science ID 000632464500001

  View details for PubMedID 33764563

• SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces. Magnetic resonance in medicine Ramos-Llordén, G. n., Vegas-Sánchez-Ferrero, G. n., Liao, C. n., Westin, C. F., Setsompop, K. n., Rathi, Y. n. 2021

  Abstract

  To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages nonlinear redundancy in the data to boost the SNR while preserving signal information.We exploit nonlinear redundancy of the dMRI data by means of kernel principal component analysis (KPCA), a nonlinear generalization of PCA to reproducing kernel Hilbert spaces. By mapping the signal to a high-dimensional space, a higher level of redundant information is exploited, thereby enabling better denoising than linear PCA. We implement KPCA with a Gaussian kernel, with parameters automatically selected from knowledge of the noise statistics, and validate it on realistic Monte Carlo simulations as well as with in vivo human brain submillimeter and low-resolution dMRI data. We also demonstrate KPCA denoising on multi-coil dMRI data.SNR improvements up to 2.7 × were obtained in real in vivo datasets denoised with KPCA, in comparison to SNR gains of up to 1.8 × using a linear PCA denoising technique called Marchenko-Pastur PCA (MPPCA). Compared to gold-standard dataset references created from averaged data, we showed that lower normalized root mean squared error was achieved with KPCA compared to MPPCA. Statistical analysis of residuals shows that anatomical information is preserved and only noise is removed. Improvements in the estimation of diffusion model parameters such as fractional anisotropy, mean diffusivity, and fiber orientation distribution functions were also demonstrated.Nonlinear redundancy of the dMRI signal can be exploited with KPCA, which allows superior noise reduction/SNR improvements than the MPPCA method, without loss of signal information.

  View details for DOI 10.1002/mrm.28752

  View details for PubMedID 33834546

• In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution. Scientific data Wang, F. n., Dong, Z. n., Tian, Q. n., Liao, C. n., Fan, Q. n., Hoge, W. S., Keil, B. n., Polimeni, J. R., Wald, L. L., Huang, S. Y., Setsompop, K. n. 2021; 8 (1): 122

  Abstract

  We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.

  View details for DOI 10.1038/s41597-021-00904-z

  View details for PubMedID 33927203

• Distortion-free, high-isotropic-resolution diffusion MRI with gSlider BUDA-EPI and multicoil dynamic B0 shimming. Magnetic resonance in medicine Liao, C. n., Bilgic, B. n., Tian, Q. n., Stockmann, J. P., Cao, X. n., Fan, Q. n., Iyer, S. S., Wang, F. n., Ngamsombat, C. n., Lo, W. C., Manhard, M. K., Huang, S. Y., Wald, L. L., Setsompop, K. n. 2021

  Abstract

  We combine SNR-efficient acquisition and model-based reconstruction strategies with newly available hardware instrumentation to achieve distortion-free in vivo diffusion MRI of the brain at submillimeter-isotropic resolution with high fidelity and sensitivity on a clinical 3T scanner.We propose blip-up/down acquisition (BUDA) for multishot EPI using interleaved blip-up/blip-down phase encoding and incorporate B0 forward-modeling into structured low-rank reconstruction to enable distortion-free and navigator-free diffusion MRI. We further combine BUDA-EPI with an SNR-efficient simultaneous multislab acquisition (generalized slice-dithered enhanced resolution ["gSlider"]), to achieve high-isotropic-resolution diffusion MRI. To validate gSlider BUDA-EPI, whole-brain diffusion data at 860-μm and 780-μm data sets were acquired. Finally, to improve the conditioning and minimize noise penalty in BUDA reconstruction at very high resolutions where B0 inhomogeneity can have a detrimental effect, the level of B0 inhomogeneity was reduced by incorporating slab-by-slab dynamic shimming with a 32-channel AC/DC coil into the acquisition. Whole-brain 600-μm diffusion data were then acquired with this combined approach of gSlider BUDA-EPI with dynamic shimming.The results of 860-μm and 780-μm datasets show high geometry fidelity with gSlider BUDA-EPI. With dynamic shimming, the BUDA reconstruction's noise penalty was further alleviated. This enables whole-brain 600-μm isotropic resolution diffusion imaging with high image quality.The gSlider BUDA-EPI method enables high-quality, distortion-free diffusion imaging across the whole brain at submillimeter resolution, where the use of multicoil dynamic B0 shimming further improves reconstruction performance, which can be particularly useful at very high resolutions.

  View details for DOI 10.1002/mrm.28748

  View details for PubMedID 33748985

• Robust autocalibrated structured low-rank EPI ghost correction. Magnetic resonance in medicine Lobos, R. A., Hoge, W. S., Javed, A., Liao, C., Setsompop, K., Nayak, K. S., Haldar, J. P. 2020

  Abstract

  PURPOSE: We propose and evaluate a new structured low-rank method for echo-planar imaging (EPI) ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method can be used to suppress EPI ghosts arising from the differences between different readout gradient polarities and/or the differences between different shots. It does not require conventional EPI navigator signals, and is robust to imperfect autocalibration data.METHODS: Autocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable high-quality ghost correction. This method works well when the autocalibration data are pristine, but performance degrades substantially when the autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated LORAKS in two ways. First, it does not completely trust the information from autocalibration data, and instead considers the autocalibration and EPI data simultaneously when estimating low-rank matrix structure. Second, it uses complementary information from the autocalibration data to improve EPI reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS is evaluated using simulations and in vivo data, including comparisons to state-of-the-art methods.RESULTS: RAC-LORAKS is demonstrated to have good ghost elimination performance compared to state-of-the-art methods in several complicated EPI acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging).CONCLUSIONS: RAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data.

  View details for DOI 10.1002/mrm.28638

  View details for PubMedID 33332652

• Diffusion-PEPTIDE: Distortion- and blurring-free diffusion imaging with self-navigated motion-correction and relaxometry capabilities. Magnetic resonance in medicine Fair, M. J., Liao, C., Manhard, M. K., Setsompop, K. 2020

  Abstract

  PURPOSE: To implement the time-resolved relaxometry PEPTIDE technique into a diffusion acquisition to provide self-navigated, distortion- and blurring-free diffusion imaging that is robust to motion, while simultaneously providing T2 and T 2 mapping.THEORY AND METHODS: The PEPTIDE readout was implemented into a spin-echo diffusion acquisition, enabling reconstruction of a time-series of T2 - and T 2 -weighted images, free from conventional echo planar imaging (EPI) distortion and blurring, for each diffusion-encoding. Robustness of PEPTIDE to motion and shot-to-shot phase variation was examined through a deliberate motion-corrupted diffusion experiment. Two diffusion-relaxometry in vivo brain protocols were also examined: (1)1 * 1 * 3 mm3 across 32 diffusion directions in 20 min, (2)1.5 * 1.5 * 3.0 mm3 across 6 diffusion-weighted images in 3.4 min. T2 , T 2 , and diffusion parameter maps were calculated from these data. As initial exploration of the rich diffusion-relaxometry data content for use in multi-compartment modeling, PEPTIDE data were acquired of a gadolinium-doped asparagus phantom. These datasets contained two compartments with different relaxation parameters and different diffusion orientation properties, and T2 relaxation variations across these diffusion directions were explored.RESULTS: Diffusion-PEPTIDE showed the capability to provide high quality diffusion images and T2 and T 2 maps from both protocols. The reconstructions were distortion-free, avoided potential resolution losses exceeding 100% in equivalent EPI acquisitions, and showed tolerance to nearly 30° of rotational motion. Expected variation in T2 values as a function of diffusion direction was observed in the two-compartment asparagus phantom (P < .01), demonstrating potential to explore diffusion-PEPTIDE data for multi-compartment modeling.CONCLUSIONS: Diffusion-PEPTIDE provides highly robust diffusion and relaxometry data and offers potential for future applications in diffusion-relaxometry multi-compartment modeling.

  View details for DOI 10.1002/mrm.28579

  View details for PubMedID 33314281

• DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning. NeuroImage Tian, Q., Bilgic, B., Fan, Q., Liao, C., Ngamsombat, C., Hu, Y., Witzel, T., Setsompop, K., Polimeni, J. R., Huang, S. Y. 2020: 117017

  Abstract

  Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input b=0 image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output image volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b=0 images and 21 DWIs for the primary eigenvector derived from DTI and two b=0 images and 26-30 DWIs for various scalar metrics derived from DTI, achieving 3.3-4.6* acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 b=0 images and 90 DWIs measures around 1-1.5 mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.

  View details for DOI 10.1016/j.neuroimage.2020.117017

  View details for PubMedID 32504817

• High-fidelity, accelerated whole-brain submillimeter in vivo diffusion MRI using gSlider-spherical ridgelets (gSlider-SR) MAGNETIC RESONANCE IN MEDICINE Ramos-Llorden, G., Ning, L., Liao, C., Mukhometzianov, R., Michailovich, O., Setsompop, K., Rathi, Y. 2020; 84 (4): 1781–95

  Abstract

  To develop an accelerated, robust, and accurate diffusion MRI acquisition and reconstruction technique for submillimeter whole human brain in vivo scan on a clinical scanner.We extend the ultra-high resolution diffusion MRI acquisition technique, gSlider, by allowing undersampling in q-space and radiofrequency (RF)-encoding space, thereby dramatically reducing the total acquisition time of conventional gSlider. The novel method, termed gSlider-SR, compensates for the lack of acquired information by exploiting redundancy in the dMRI data using a basis of spherical ridgelets (SR), while simultaneously enhancing the signal-to-noise ratio. Using Monte Carlo simulation with realistic noise levels and several acquisitions of in vivo human brain dMRI data (acquired on a Siemens Prisma 3T scanner), we demonstrate the efficacy of our method using several quantitative metrics.For high-resolution dMRI data with realistic noise levels (synthetically added), we show that gSlider-SR can reconstruct high-quality dMRI data at different acceleration factors preserving both signal and angular information. With in vivo data, we demonstrate that gSlider-SR can accurately reconstruct 860 μm diffusion MRI data (64 diffusion directions at b = 2000 s / mm 2 ), at comparable quality as that obtained with conventional gSlider with four averages, thereby providing an eight-fold reduction in scan time (from 1 hour 20 to 10 minutes).gSlider-SR enables whole-brain high angular resolution dMRI at a submillimeter spatial resolution with a dramatically reduced acquisition time, making it feasible to use the proposed scheme on existing clinical scanners.

  View details for DOI 10.1002/mrm.28232

  View details for Web of Science ID 000517642500001

  View details for PubMedID 32125020

• Fast submillimeter diffusion MRI using gSlider-SMS and SNR-enhancing joint reconstruction MAGNETIC RESONANCE IN MEDICINE Haldar, J. P., Liu, Y., Liao, C., Fan, Q., Setsompop, K. 2020; 84 (2): 762–76

  Abstract

  We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed.A recent method called gSlider-SMS enables whole-brain submillimeter diffusion MRI with high signal-to-noise ratio (SNR) efficiency. However, despite the efficient acquisition, the resulting images can still suffer from low SNR due to the small size of the imaging voxels. This work proposes to mitigate the SNR problem by combining gSlider-SMS with a regularized SNR-enhancing reconstruction approach.Illustrative results show that, from gSlider-SMS data acquired over a span of only 25 minutes on a 3T scanner, the proposed method is able to produce 71 MRI images (64 diffusion encoding orientations with b = 1500 s/ mm 2 , and 7 images without diffusion weighting) of the entire in vivo human brain with nominal 0.66 mm spatial resolution. Using data acquired from 75 minutes of acquisition as a gold standard reference, we demonstrate that the proposed SNR-enhancement procedure leads to substantial improvements in estimated diffusion parameters compared to conventional gSlider reconstruction. Results also demonstrate that the proposed method has advantages relative to denoising methods based on low-rank matrix modeling. A theoretical analysis of the trade-off between spatial resolution and SNR suggests that the proposed approach has high efficiency.The combination of gSlider-SMS with advanced regularized reconstruction enables high-resolution quantitative diffusion MRI from a relatively fast acquisition.

  View details for DOI 10.1002/mrm.28172

  View details for Web of Science ID 000506437900001

  View details for PubMedID 31919908

  View details for PubMedCentralID PMC7968733

• High-fidelity, high-isotropic-resolution diffusion imaging through gSlider acquisition with B 1 + and T1 corrections and integrated ΔB0 /Rx shim array. Magnetic resonance in medicine Liao, C. n., Stockmann, J. n., Tian, Q. n., Bilgic, B. n., Arango, N. S., Manhard, M. K., Huang, S. Y., Grissom, W. A., Wald, L. L., Setsompop, K. n. 2020; 83 (1): 56–67

  Abstract

  B 1 + and T1 corrections and dynamic multicoil shimming approaches were proposed to improve the fidelity of high-isotropic-resolution generalized slice-dithered enhanced resolution (gSlider) diffusion imaging.An extended reconstruction incorporating B 1 + inhomogeneity and T1 recovery information was developed to mitigate slab-boundary artifacts in short-repetition time (TR) gSlider acquisitions. Slab-by-slab dynamic B0 shimming using a multicoil integrated ΔB0 /Rx shim array and high in-plane acceleration (Rinplane = 4) achieved with virtual-coil GRAPPA were also incorporated into a 1-mm isotropic resolution gSlider acquisition/reconstruction framework to achieve a significant reduction in geometric distortion compared to single-shot echo planar imaging (EPI).The slab-boundary artifacts were alleviated by the proposed B 1 + and T1 corrections compared to the standard gSlider reconstruction pipeline for short-TR acquisitions. Dynamic shimming provided >50% reduction in geometric distortion compared to conventional global second-order shimming. One-millimeter isotropic resolution diffusion data show that the typically problematic temporal and frontal lobes of the brain can be imaged with high geometric fidelity using dynamic shimming.The proposed B 1 + and T1 corrections and local-field control substantially improved the fidelity of high-isotropic-resolution diffusion imaging, with reduced slab-boundary artifacts and geometric distortion compared to conventional gSlider acquisition and reconstruction. This enabled high-fidelity whole-brain 1-mm isotropic diffusion imaging with 64 diffusion directions in 20 min using a 3T clinical scanner.

  View details for DOI 10.1002/mrm.27899

  View details for PubMedID 31373048

  View details for PubMedCentralID PMC6778699

• AN EVALUATION OF REGULARIZATION STRATEGIES FOR SUBSAMPLED SINGLE-SHELL DIFFUSION MRI Liu, Y., Liao, C., Setsompop, K., Haldar, J. P., IEEE IEEE. 2020: 1437–40

  View details for Web of Science ID 000578080300297

• Accelerated spin-echo functional MRI using multisection excitation by simultaneous spin-echo interleaving (MESSI) with complex-encoded generalized slice dithered enhanced resolution (cgSlider) simultaneous multislice echo-planar imaging MAGNETIC RESONANCE IN MEDICINE Han, S., Liao, C., Manhard, M., Park, D., Bilgic, B., Fair, M. J., Wang, F., Blazejewska, A., Grissom, W. A., Polimeni, J. R., Setsompop, K. 2020; 84 (1): 206–20

  Abstract

  Spin-echo functional MRI (SE-fMRI) has the potential to improve spatial specificity when compared with gradient-echo fMRI. However, high spatiotemporal resolution SE-fMRI with large slice-coverage is challenging as SE-fMRI requires a long echo time to generate blood oxygenation level-dependent (BOLD) contrast, leading to long repetition times. The aim of this work is to develop an acquisition method that enhances the slice-coverage of SE-fMRI at high spatiotemporal resolution.An acquisition scheme was developed entitled multisection excitation by simultaneous spin-echo interleaving (MESSI) with complex-encoded generalized slice dithered enhanced resolution (cgSlider). MESSI uses the dead-time during the long echo time by interleaving the excitation and readout of 2 slices to enable 2× slice-acceleration, while cgSlider uses the stable temporal background phase in SE-fMRI to encode/decode 2 adjacent slices simultaneously with a "phase-constrained" reconstruction method. The proposed cgSlider-MESSI was also combined with simultaneous multislice (SMS) to achieve further slice-acceleration. This combined approach was used to achieve 1.5-mm isotropic whole-brain SE-fMRI with a temporal resolution of 1.5 s and was evaluated using sensory stimulation and breath-hold tasks at 3T.Compared with conventional SE-SMS, cgSlider-MESSI-SMS provides 4-fold increase in slice-coverage for the same repetition time, with comparable temporal signal-to-noise ratio. Corresponding fMRI activation from cgSlider-MESSI-SMS for both fMRI tasks were consistent with those from conventional SE-SMS. Overall, cgSlider-MESSI-SMS achieved a 32× encoding-acceleration by combining Rinplane × MB × cgSlider × MESSI = 4 × 2 × 2 × 2.High-quality, high-resolution whole-brain SE-fMRI was acquired at a short repetition time using cgSlider-MESSI-SMS. This method should be beneficial for high spatiotemporal resolution SE-fMRI studies requiring whole-brain coverage.

  View details for DOI 10.1002/mrm.28108

  View details for Web of Science ID 000502704900001

  View details for PubMedID 31840295

  View details for PubMedCentralID PMC7083698

• Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction MAGNETIC RESONANCE IN MEDICINE Bilgic, B., Chatnuntawech, I., Manhard, M., Tian, Q., Liao, C., Iyer, S. S., Cauley, S. F., Huang, S. Y., Polimeni, J. R., Wald, L. L., Setsompop, K. 2019; 82 (4): 1343–58

  Abstract

  To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging.Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution.Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network.Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.

  View details for DOI 10.1002/mrm.27813

  View details for Web of Science ID 000483917000010

  View details for PubMedID 31106902

  View details for PubMedCentralID PMC6626584

• Accelerated whole-brain perfusion imaging using a simultaneous multislice spin-echo and gradient-echo sequence with joint virtual coil reconstruction MAGNETIC RESONANCE IN MEDICINE Manhard, M., Bilgic, B., Liao, C., Han, S., Witzel, T., Yen, Y., Setsompop, K. 2019; 82 (3): 973–83

  Abstract

  Dynamic susceptibility contrast imaging requires high temporal sampling, which poses limits on achievable spatial coverage and resolution. Additionally, more encoding-intensive multi-echo acquisitions for quantitative imaging are desired to mitigate contrast leakage effects, which further limits spatial encoding. We present an accelerated sequence that provides whole-brain coverage at an improved spatio-temporal resolution, to allow for dynamic quantitative R2 and R2 * mapping during contrast-enhanced imaging.A multi-echo spin and gradient-echo sequence was implemented with simultaneous multislice acquisition. Complementary k-space sampling between repetitions and joint virtual coil reconstruction were used along with a dynamic phase-matching technique to achieve high-quality reconstruction at 9-fold acceleration, which enabled 2 × 2 × 5 mm whole-brain imaging at TR of 1.5 to 1.7 seconds. The multi-echo images from this sequence were fit to achieve quantitative R2 and R2 * maps for each repetition, and subsequently used to find perfusion measures including cerebral blood flow and cerebral blood volume.Images reconstructed using joint virtual coil show improved image quality and g-factor compared with conventional reconstruction methods, resulting in improved quantitative maps with a 9-fold acceleration factor and whole-brain coverage during the dynamic perfusion acquisition.The method presented shows the advantage of using a joint virtual coil-GRAPPA reconstruction to allow for high acceleration factors while maintaining reliable image quality for quantitative perfusion mapping, with the potential to improve tumor diagnostics and monitoring.

  View details for DOI 10.1002/mrm.27784

  View details for Web of Science ID 000485077600010

  View details for PubMedID 31069861

  View details for PubMedCentralID PMC6692914

• Phase-matched virtual coil reconstruction for highly accelerated diffusion echo-planar imaging NEUROIMAGE Liao, C., Manhard, M., Bilgic, B., Tian, Q., Fan, Q., Han, S., Wang, F., Park, D., Witzel, T., Zhong, J., Wang, H., Wald, L. L., Setsompop, K. 2019; 194: 291–302

  Abstract

  To propose a virtual coil (VC) acquisition/reconstruction framework to improve highly accelerated single-shot EPI (SS-EPI) and generalized slice dithered enhanced resolution (gSlider) acquisition in high-resolution diffusion imaging (DI).For robust VC-GRAPPA reconstruction, a background phase correction scheme was developed to match the image phase of the reference data with the corrupted phase of the accelerated diffusion-weighted data, where the corrupted phase of the diffusion data varies from shot to shot. A Gy prewinding-blip was also added to the EPI acquisition, to create a shifted-ky sampling strategy that allows for better exploitation of VC concept in the reconstruction. To evaluate the performance of the proposed methods, 1.5 mm isotropic whole-brain SS-EPI and 860 μm isotropic whole-brain gSlider-EPI diffusion data were acquired at an acceleration of 8-9 fold. Conventional and VC-GRAPPA reconstructions were performed and compared, and corresponding g-factors were calculated.The proposed VC reconstruction substantially improves the image quality of both SS-EPI and gSlider-EPI, with reduced g-factor noise and reconstruction artifacts when compared to the conventional method. This has enabled high-quality low-noise diffusion imaging to be performed at 8-9 fold acceleration.The proposed VC acquisition/reconstruction framework improves the reconstruction of DI at high accelerations. The ability to now employ such high accelerations will allow DI with EPI at reduced distortion and faster scan time, which should be beneficial for many clinical and neuroscience applications.

  View details for DOI 10.1016/j.neuroimage.2019.04.002

  View details for Web of Science ID 000468742800024

  View details for PubMedID 30953837

• Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramer-Rao Bound Meets Spin Dynamics IEEE TRANSACTIONS ON MEDICAL IMAGING Zhao, B., Haldar, J. P., Liao, C., Ma, D., Jiang, Y., Griswold, M. A., Setsompop, K., Wald, L. L. 2019; 38 (3): 844–61

  Abstract

  Magnetic resonance (MR) fingerprinting is a new quantitative imaging paradigm, which simultaneously acquires multiple MR tissue parameter maps in a single experiment. In this paper, we present an estimation-theoretic framework to perform experiment design for MR fingerprinting. Specifically, we describe a discrete-time dynamic system to model spin dynamics, and derive an estimation-theoretic bound, i.e., the Cramér-Rao bound, to characterize the signal-to-noise ratio (SNR) efficiency of an MR fingerprinting experiment. We then formulate an optimal experiment design problem, which determines a sequence of acquisition parameters to encode MR tissue parameters with the maximal SNR efficiency, while respecting the physical constraints and other constraints from the image decoding/reconstruction process. We evaluate the performance of the proposed approach with numerical simulations, phantom experiments, and in vivo experiments. We demonstrate that the optimized experiments substantially reduce data acquisition time and/or improve parameter estimation. For example, the optimized experiments achieve about a factor of two improvement in the accuracy of T2 maps, while keeping similar or slightly better accuracy of T1 maps. Finally, as a remarkable observation, we find that the sequence of optimized acquisition parameters appears to be highly structured rather than randomly/pseudo-randomly varying as is prescribed in the conventional MR fingerprinting experiments.

  View details for DOI 10.1109/TMI.2018.2873704

  View details for Web of Science ID 000460662400018

  View details for PubMedID 30295618

  View details for PubMedCentralID PMC6447464

• Fast 3D brain MR fingerprinting based on multi-axis spiral projection trajectory. Magnetic resonance in medicine Cao, X. n., Ye, H. n., Liao, C. n., Li, Q. n., He, H. n., Zhong, J. n. 2019; 82 (1): 289–301

  Abstract

  To develop a fast, sub-millimeter 3D magnetic resonance fingerprinting (MRF) technique for whole-brain quantitative scans.An acquisition trajectory based on multi-axis spiral projection imaging (maSPI) was implemented for 3D MRF with steady-state precession and slab excitation. By appropriately assigning the in-plane and through-plane rotations of spiral interleaves in a novel acquisition scheme, an maSPI-based acquisition was implemented, and the total acquisition time was reduced by up to a factor of 8 compared to stack-of-spiral (SOS)-based acquisition. A sliding-window method was also used to further reduce the required number of time points for a faster acquisition. The experiments were conducted both on a phantom and in vivo.The results from the phantom measurements with the proposed and gold standard methods were consistent with a good linear correlation and an R2 value approaching 0.99. The in vivo experiments achieved whole-brain parametric maps with isotropic resolutions of 1 mm and 0.8 mm in 5.0 and 6.0 min, respectively, with potential for further acceleration. An in vivo experiment with intentionally moving subjects demonstrated that the maSPI scheme largely outperforms the SOS scheme in terms of robustness to head motion.3D MRF with an maSPI acquisition scheme enables fast and robust scans for high-resolution parametric mapping.

  View details for DOI 10.1002/mrm.27726

  View details for PubMedID 30883867

• Highly efficient MRI through multi-shot echo planar imaging Liao, C., Cao, X., Cho, J., Zhang, Z., Setsompop, K., Bilgic, B., VanDeVille, D., Papadakis, M., Lu, Y. M. SPIE-INT SOC OPTICAL ENGINEERING. 2019

  View details for DOI 10.1117/12.2527183

  View details for Web of Science ID 000511301800031

• Magnetic resonance fingerprinting of temporal lobe white matter in mesial temporal lobe epilepsy. Annals of clinical and translational neurology Wang, K. n., Cao, X. n., Wu, D. n., Liao, C. n., Zhang, J. n., Ji, C. n., Zhong, J. n., He, H. n., Chen, Y. n. 2019; 6 (9): 1639–46

  Abstract

  Mesial temporal lobe epilepsy (MTLE) is a network disorder. We aimed to quantify the white matter alterations in the temporal lobe of MTLE patients with hippocampal sclerosis (MTLE-HS) by using magnetic resonance fingerprinting (MRF), a novel imaging technique, which allows simultaneous measurements of multiple parameters with a single acquisition.We consecutively recruited 27 unilateral MTLE-HS patients and 22 healthy controls. Measurements including T1, T2, and PD values in the temporopolar white matter and temporal stem were recorded and analyzed.We found increased T2 value in both sides, and increased T1 value in the ipsilateral temporopolar white matter of MTLE-HS patients, as compared with healthy controls. The T1 and T2 values were higher in the ipsilateral than the contralateral side. In the temporal stem, increased T1 and T2 values in the ipsilateral side of the MTLE-HS patients were also observed. Only increased T2 values were observed in the contralateral temporal stem. No significant differences in PD values were observed in either the temporopolar white matter or temporal stem of the MTLE-HS patients. Correlation analysis revealed that T1 and T2 values in the ipsilateral temporopolar white matter were negatively correlated with the age at epilepsy onset.By using MRF, we were able to assess the alterations of T1 and T2 in the temporal lobe white matter of MTLE-HS patients. MRF could be a promising imaging technique in identifying mild changes in MTLE patients, which might optimize the pre-surgical evaluation and therapeutic interventions in these patients.

  View details for DOI 10.1002/acn3.50851

  View details for PubMedID 31359636

  View details for PubMedCentralID PMC6764497

• Ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) for simultaneous quantification of long and ultrashort T2 tissues. Magnetic resonance in medicine Li, Q. n., Cao, X. n., Ye, H. n., Liao, C. n., He, H. n., Zhong, J. n. 2019; 82 (4): 1359–72

  Abstract

  To demonstrate an ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) method that allows quantifying relaxation times for muscle and bone in the musculoskeletal system and generating bone enhanced images that mimic CT scans.A fast imaging steady-state free precession MRF sequence with half pulse excitation and half projection readout was designed to sample fast T2 decay signals. Varying echo time (TE) of a sinusoidal pattern was applied to enhance sensitivity for tissues with short and ultrashort T2 values. The performance of UTE-MRF was evaluated via simulations, phantom, and in vivo experiments.A minimal TE of 0.05 ms was achieved. Simulations indicated the sinusoidal TE sampling increased T2 quantification accuracy in the cortical bone and tendon but had little impact on long T2 muscle quantifications. For the rubber phantom, the averaged relaxometries from UTE-MRF (T1 = 162 ms and T2 = 1.07 ms) compared well with the gold standard (T1 = 190 ms and T 2 ∗ = 1.03 ms). For the long T2 agarose phantom, the linear regression slope between UTE-MRF and gold standard was 1.07 (R2 = 0.991) for T1 and 1.04 (R2 = 0.994) for T2 . In vivo experiments showed the detection of the cortical bone (averaged T2 = 1.0 ms) and Achilles tendon (averaged T2 = 15 ms). Scalp structures from the bone enhanced image show high similarity with CT.The UTE-MRF with sinusoidal TEs can simultaneously quantify T1 , T2 , proton density, and B0 in long, short, even ultrashort T2 musculoskeletal structures. Bone enhanced images can be achieved in the brain with UTE-MRF.

  View details for DOI 10.1002/mrm.27812

  View details for PubMedID 31131911

• Improving parallel imaging by jointly reconstructing multi-contrast data MAGNETIC RESONANCE IN MEDICINE Bilgic, B., Kim, T., Liao, C., Manhard, M., Wald, L. L., Haldar, J. P., Setsompop, K. 2018; 80 (2): 619–32

  Abstract

  To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition.We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquired with different contrast preparations, and show its application in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions. We extend the joint parallel imaging concept to exploit limited support and smooth phase constraints through Joint (J-) LORAKS formulation. J-LORAKS allows joint parallel imaging from limited autocalibration signal region, as well as permitting partial Fourier sampling and calibrationless reconstruction.We demonstrate highly accelerated 2D balanced steady-state free precession with phase cycling, SMS multi-echo spin echo, 3D multi-echo magnetization-prepared rapid gradient echo, and multi-echo gradient recalled echo acquisitions in vivo. Compared to conventional GRAPPA, proposed joint acquisition/reconstruction techniques provide more than 2-fold reduction in reconstruction error.JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

  View details for DOI 10.1002/mrm.27076

  View details for Web of Science ID 000430469300019

  View details for PubMedID 29322551

  View details for PubMedCentralID PMC5910232

• Detection of Lesions in Mesial Temporal Lobe Epilepsy by Using MR Fingerprinting. Radiology Liao, C. n., Wang, K. n., Cao, X. n., Li, Y. n., Wu, D. n., Ye, H. n., Ding, Q. n., He, H. n., Zhong, J. n. 2018; 288 (3): 804–12

  Abstract

  Purpose To improve diagnosis of hippocampal sclerosis (HS) in patients with mesial temporal lobe epilepsy (MTLE) by using MR fingerprinting and compare with visual assessment of T1- and T2-weighted MR images. Materials and Methods For this prospective study performed between April and November 2016, T1 and T2 maps were obtained and tissue segmentation performed in consecutive patients with drug-resistant MTLE with unilateral or bilateral HS. T1 and T2 maps were compared between 33 patients with MTLE (23 women and 10 men; mean age, 32.6 years; age range, 16-60 years) and 30 healthy participants (20 women and 10 men; mean age, 28.8 years; age range, 18-40 years). Differences in individual bilateral hippocampi were compared by using a Wilcoxon signed rank test, whereas the Wilcoxon rank-sum test was used for difference analysis between healthy control participants and patients with MTLE. Results The diagnosis rate (ie, ratio of HS diagnosed on the basis of a 2.5-minute MR fingerprinting examination compared with standard methods: MRI, electroencephalography, and PET) was 32 of 33 (96.9%; 95% confidence interval: 84.9%, 100%), reflecting improved accuracy of diagnosis (P = 1.92 × 10-12) over routine MR examinations that had a diagnostic rate of 23 of 33 (69.7%; 95% confidence interval: 51.5%, 81.6%). The comparison between atrophic and normal-appearing hippocampus in 33 patients with MTLE and healthy control participants demonstrated that both T1 and T2 values in HS lesions were higher than those of normal hippocampal tissue of healthy participants (T1: 1361 msec ± 85 vs 1249 msec ± 59, respectively; T2: 135 msec ± 15 vs 104 msec ± 9, respectively; P < .0001). Conclusion MR fingerprinting allowed for multiparametric mapping of temporal lobe within 2.5 minutes and helped to identify lesions suspicious for HS in patients with MTLE with improved accuracy.

  View details for DOI 10.1148/radiol.2018172131

  View details for PubMedID 29916782

• Squeezed Trajectory Design for Peak RF and Integrated RF Power Reduction in Parallel Transmission MRI. IEEE transactions on medical imaging Li, Q. n., Liao, C. n., Ye, H. n., Chen, Y. n., Cao, X. n., Yuan, L. n., He, H. n., Zhong, J. n. 2018; 37 (8): 1809–21

  Abstract

  High peak RF amplitude and excessive specific absorption rate (SAR) are two critical concerns for hardware implementation and patient safety in scientific and clinical research for high field MRI using parallel transmissions (pTX). In this paper, we introduce a squeezing strategy to reduce peak RF amplitude and integrated RF power via direct reshaping of the k-space trajectory. In the existing peak RF / integrated RF power optimization methods gradient amplitude or slew rate is reduced, but the k-space trajectory remains unchanged. Unlike these traditional methods, we worked directly in the excitation k-space to reshape k-space traversal by a squeezing vector in order to achieve peak RF and total RF power optimization, using a particle swarm optimization algorithm. The squeezing strategy was applied to the conventional variable density spiral (CVDS) and the variable rate selective excitation (VERSE) trajectories, dubbed SVDS (squeezed variable density spiral) and SVERSE (squeezing trajectory with VERSE), respectively, for different excitation profiles of small or large tip angles. Pulse acceleration and off-resonance effects were evaluated for an 8-ch pTX via Bloch simulation. CVDS, VERSE, SVDS, and SVERSE pulses were implemented on a 3T scanner with a 2-ch pTX. Phantom and in vivo experiments were performed for reduced FOV (rFOV) imaging. The results show that SVDS pulses simultaneously reduce integrated RF power and peak RF by about 30% on average compared to CVDS pulses for a square pattern ( $80\times80$ mm2) with flip angles of 30°, 90°, and 180°. Compared with the VERSE method under the same peak RF constraints, the SVDS method reduces integrated RF power by an average of 20% for small tip excitations for profiles of slice, rectangular, square, and circle, and has slightly reduced excitation accuracy slightly (about 0.6%, from 6.8% to 7.4%). The SVERSE method shortens the duration of the VERSE pulse by 12.8% at large ti p angle (180°). Feasibility for rFOV imaging was demonstrated with phantom and in vivo experiments with squeezed pulses.

  View details for DOI 10.1109/TMI.2018.2828112

  View details for PubMedID 29993630

• 3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction NEUROIMAGE Liao, C., Bilgic, B., Manhard, M., Zhao, B., Cao, X., Zhong, J., Wald, L. L., Setsompop, K. 2017; 162: 13–22

  Abstract

  Whole-brain high-resolution quantitative imaging is extremely encoding intensive, and its rapid and robust acquisition remains a challenge. Here we present a 3D MR fingerprinting (MRF) acquisition with a hybrid sliding-window (SW) and GRAPPA reconstruction strategy to obtain high-resolution T1, T2 and proton density (PD) maps with whole brain coverage in a clinically feasible timeframe.3D MRF data were acquired using a highly under-sampled stack-of-spirals trajectory with a steady-state precession (FISP) sequence. For data reconstruction, kx-ky under-sampling was mitigated using SW combination along the temporal axis. Non-uniform fast Fourier transform (NUFFT) was then applied to create Cartesian k-space data that are fully-sampled in the in-plane direction, and Cartesian GRAPPA was performed to resolve kz under-sampling to create an alias-free SW dataset. T1, T2 and PD maps were then obtained using dictionary matching.Phantom study demonstrated that the proposed 3D-MRF acquisition/reconstruction method is able to produce quantitative maps that are consistent with conventional quantification techniques. Retrospectively under-sampled in vivo acquisition revealed that SW + GRAPPA substantially improves quantification accuracy over the current state-of-the-art accelerated 3D MRF. Prospectively under-sampled in vivo study showed that whole brain T1, T2 and PD maps with 1 mm3 resolution could be obtained in 7.5 min.3D MRF stack-of-spirals acquisition with hybrid SW + GRAPPA reconstruction may provide a feasible approach for rapid, high-resolution quantitative whole-brain imaging.

  View details for DOI 10.1016/j.neuroimage.2017.08.030

  View details for Web of Science ID 000416502800002

  View details for PubMedID 28842384

  View details for PubMedCentralID PMC6031129

• Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint. Magnetic resonance in medicine Liao, C. n., Chen, Y. n., Cao, X. n., Chen, S. n., He, H. n., Mani, M. n., Jacob, M. n., Magnotta, V. n., Zhong, J. n. 2017; 77 (3): 1359–66

  Abstract

  To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging.The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets.It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions.Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

  View details for DOI 10.1002/mrm.26199

  View details for PubMedID 26968846

• Robust sliding-window reconstruction for Accelerating the acquisition of MR fingerprinting. Magnetic resonance in medicine Cao, X. n., Liao, C. n., Wang, Z. n., Chen, Y. n., Ye, H. n., He, H. n., Zhong, J. n. 2017; 78 (4): 1579–88

  Abstract

  To develop a method for accelerated and robust MR fingerprinting (MRF) with improved image reconstruction and parameter matching processes.A sliding-window (SW) strategy was applied to MRF, in which signal and dictionary matching was conducted between fingerprints consisting of mixed-contrast image series reconstructed from consecutive data frames segmented by a sliding window, and a precalculated mixed-contrast dictionary. The effectiveness and performance of this new method, dubbed SW-MRF, was evaluated in both phantom and in vivo. Error quantifications were conducted on results obtained with various settings of SW reconstruction parameters.Compared with the original MRF strategy, the results of both phantom and in vivo experiments demonstrate that the proposed SW-MRF strategy either provided similar accuracy with reduced acquisition time, or improved accuracy with equal acquisition time. Parametric maps of T1 , T2 , and proton density of comparable quality could be achieved with a two-fold or more reduction in acquisition time. The effect of sliding-window width on dictionary sensitivity was also estimated.The novel SW-MRF recovers high quality image frames from highly undersampled MRF data, which enables more robust dictionary matching with reduced numbers of data frames. This time efficiency may facilitate MRF applications in time-critical clinical settings. Magn Reson Med 78:1579-1588, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

  View details for DOI 10.1002/mrm.26521

  View details for PubMedID 27851871

• Uncertainty assessment of gamma-aminobutyric acid concentration of different brain regions in individual and group using residual bootstrap analysis. Medical & biological engineering & computing Chen, M. n., Liao, C. n., Chen, S. n., Ding, Q. n., Zhu, D. n., Liu, H. n., Yan, X. n., Zhong, J. n. 2017; 55 (6): 1051–59

  Abstract

  The aim of this work is to quantify individual and regional differences in the relative concentration of gamma-aminobutyric acid (GABA) in human brain with in vivo magnetic resonance spectroscopy. Spectral editing Mescher-Garwood point resolved spectroscopy (MEGA-PRESS) sequence and GABA analysis toolkit (Gannet) were used to detect and quantify GABA in anterior cingulate cortex (ACC) and occipital cortex (OCC) of healthy volunteers. Residual bootstrap, a model-based statistical analysis technique, was applied to resample the fitting residuals of GABA from the Gaussian fitting model (referred to as GABA+ thereafter) in both individual and group data of ACC and OCC. The inter-subject coefficient of variation (CV) of GABA+ in OCC (20.66 %) and ACC (12.55 %) with residual bootstrap was lower than that of a standard Gaussian model analysis (21.58 % and 16.73 % for OCC and ACC, respectively). The intra-subject uncertainty and CV of OCC were lower than that of ACC in both analyses. The residual bootstrap analysis thus provides a more robust uncertainty estimation of individual and group GABA+ detection in different brain regions, which may be useful in our understanding of GABA biochemistry in brain and its use for the diagnosis of related neuropsychiatric diseases.

  View details for DOI 10.1007/s11517-016-1579-5

  View details for PubMedID 27696130