All Publications


  • Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction. Magnetic resonance in medicine Sandino, C. M., Lai, P., Vasanawala, S. S., Cheng, J. Y. 2020

    Abstract

    PURPOSE: To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.METHODS: We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as l 1 -ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R=12) with respect to standard image quality metrics as well as automatic deep learning-based segmentations of left ventricular volumes. Feasibility of DL-ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice.RESULTS: The (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to l 1 -ESPIRiT reconstructions with respect to standard image quality metrics (P < .001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from l 1 -ESPIRiT images.CONCLUSIONS: DL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.

    View details for DOI 10.1002/mrm.28420

    View details for PubMedID 32697891

  • Rosette Trajectories Enable Ungated, Motion-Robust, Simultaneous Cardiac and Liver T2 * Iron Assessment. Journal of magnetic resonance imaging : JMRI Bush, A. M., Sandino, C. M., Ramachandran, S., Ong, F., Dwork, N., Zucker, E. J., Syed, A. B., Pauly, J. M., Alley, M. T., Vasanawala, S. S. 2020: e27196

    Abstract

    BACKGROUND: Quantitative T2 * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T2 * estimates.PURPOSE: To develop and evaluate a motion-robust, simultaneous cardiac and liver T2 * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI.STUDY TYPE: Prospective.PHANTOM/POPULATION: Six ferumoxytol-containing phantoms (26-288mug/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment.FIELD STRENGTH/SEQUENCE: 1.5T, 2D Cartesian and rosette gradient echo (GRE) ASSESSMENT: GRE T2 * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T2 * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale.STATISTICAL TESTS: Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis.RESULTS: Rosette R2* (1/T2 *) was linearly correlated with ferumoxytol concentration (r2 = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T2 * had lower spatial CoV (liver: 18.4±9.3% Cartesian, 8.8%±3.4% rosette, P = 0.02, heart: 37.7%±14.3% Cartesian, 13.4%±1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001).DATA CONCLUSION: Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T2 * mapping approach for iron quantification.

    View details for DOI 10.1002/jmri.27196

    View details for PubMedID 32452088

  • Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model. Magnetic resonance in medicine Malave, M. O., Baron, C. A., Koundinyan, S. P., Sandino, C. M., Ong, F., Cheng, J. Y., Nishimura, D. G. 2020

    Abstract

    PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA).METHODS: An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l 1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l 1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences.RESULTS: 3D iNAVs reconstructed using the DL model-based approach and conventional l 1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l 1 -ESPIRiT (20* and 3* speed increases, respectively).CONCLUSIONS: We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.

    View details for DOI 10.1002/mrm.28177

    View details for PubMedID 32011021

  • DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES. Proceedings. IEEE International Symposium on Biomedical Imaging Ma, J. J., Nakarmi, U. n., Kin, C. Y., Sandino, C. M., Cheng, J. Y., Syed, A. B., Wei, P. n., Pauly, J. M., Vasanawala, S. S. 2020; 2020: 337–40

    Abstract

    Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.

    View details for DOI 10.1109/isbi45749.2020.9098735

    View details for PubMedID 33274013

    View details for PubMedCentralID PMC7710391

  • Near-Silent and Distortion-Free Diffusion MRI in Pediatric Musculoskeletal Disorders: Comparison With Echo Planar Imaging Diffusion. Journal of magnetic resonance imaging : JMRI Sandberg, J. K., Young, V. A., Syed, A. B., Yuan, J. n., Hu, Y. n., Sandino, C. n., Menini, A. n., Hargreaves, B. n., Vasanawala, S. n. 2020

    Abstract

    Diffusion-weighted imaging (DWI) is common for evaluating pediatric musculoskeletal lesions, but suffers from geometric distortion and intense acoustic noise.To investigate the performance of a near-silent and distortion-free DWI sequence (DW-SD) relative to standard echo-planar DWI (DW-EPI) in pediatric extremity MRI.Prospective validation study.Thirty-nine children referred for extremity MRI.DW-EPI and DW-SD, based on a rotating ultrafast sequence modified with sinusoidal diffusion preparation gradients, at 3T.DW-SD image quality (Sanat ) was assessed from 0 (nondiagnostic) to 5 (outstanding) and comparative image quality (Scomp ) (from -2 = DW-EPI more delineated to +2 = DW-SD more delineated, 0 = same). ADC measured by DW-SD and DW-EPI were compared in bone marrow, muscle, and lesions.Wilcoxon rank-sum test and confidence interval of proportions (CIOP) were calculated for Scomp , Student's t-test, coefficient of variation (COV), and Bland-Altman analysis for ADC values, and intraclass correlation coefficient (ICC) for interreader agreement.DW-SD and DW-EPI ADC values for bone marrow, muscle, and lesions were not significantly different (P = 0.3, P = 0.2, and P = 0.27, respectively) and had an overall ADC COV of 14.8% (95% confidence interval: 12.3%, 16.9%) and no significant proportional bias on Bland-Altman analysis. Sanat CIOP was rated diagnostic or better (score of 3, 4, or 5) in 72-98% of cases for bone marrow, muscle, and soft tissues. DW-SD was equivalent to or preferred over DW-EPI in muscles and soft tissues, with CIOP 86-93% and 93%, respectively. Lesions were equally visualized on DW-SD and DW-EPI in 40-51%, with DW-SD preferred in 44-56% of cases. DW-SD was rated significantly better than DW-EPI across all comparative variables that included bone marrow, muscle, soft tissue, cartilage, and lesions (P < 0.05). Readers had moderate to near-perfect (ICC range = 0.45-0.85).DW-SD of the extremities provided similar ADC values and improved image quality compared with conventional DW-EPI.2 TECHNICAL EFFICACY STAGE: 2.

    View details for DOI 10.1002/jmri.27330

    View details for PubMedID 32815203

  • Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks. IEEE signal processing magazine Sandino, C. M., Cheng, J. Y., Chen, F. n., Mardani, M. n., Pauly, J. M., Vasanawala, S. S. 2020; 37 (1): 111–27

    Abstract

    Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.

    View details for DOI 10.1109/MSP.2019.2950433

    View details for PubMedID 33192036

    View details for PubMedCentralID PMC7664163

  • Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. Journal of magnetic resonance imaging : JMRI Chaudhari, A. S., Sandino, C. M., Cole, E. K., Larson, D. B., Gold, G. E., Vasanawala, S. S., Lungren, M. P., Hargreaves, B. A., Langlotz, C. P. 2020

    Abstract

    Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

    View details for DOI 10.1002/jmri.27331

    View details for PubMedID 32830874

  • Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI. Journal of magnetic resonance imaging : JMRI Moran, C. J., Cheng, J. Y., Sandino, C. M., Carl, M. n., Alley, M. T., Rosenberg, J. n., Daniel, B. L., Pittman, S. M., Rosen, E. L., Hargreaves, B. A. 2020

    Abstract

    The image quality limitations of echo-planar diffusion-weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady-state DWI is an alternative DWI method with more robust image quality but its contrast for imaging breast cancer is not well-understood. The aim of this study was to develop and evaluate diffusion-weighted double-echo steady-state imaging with a three-dimensional cones trajectory (DW-DESS-Cones) as an alternative to conventional DWI for non-contrast-enhanced MRI in the breast. This prospective study included 28 women undergoing clinically indicated breast MRI and six asymptomatic volunteers. In vivo studies were performed at 3 T and included DW-DESS-Cones, DW-DESS-Cartesian, DWI, and CE-MRI acquisitions. Phantom experiments (diffusion phantom, High Precision Devices) and simulations were performed to establish framework for contrast of DW-DESS-Cones in comparison to DWI in the breast. Motion artifacts of DW-DESS-Cones were measured with artifact-to-noise ratio in volunteers and patients. Lesion-to-fibroglandular tissue signal ratios were measured, lesions were categorized as hyperintense or hypointense, and an image quality observer study was performed in DW-DESS-Cones and DWI in patients. Effect of DW-DESS-Cones method on motion artifacts was tested by mixed-effects generalized linear model. Effect of DW-DESS-Cones on signal in phantom was tested by quadratic regression. Correlation was calculated between DW-DESS-Cones and DWI lesion-to-fibroglandular tissue signal ratios. Inter-observer agreement was assessed with Gwet's AC. Simulations predicted hyperintensity of lesions with DW-DESS-Cones but at a 3% to 67% lower degree than with DWI. Motion artifacts were reduced with DW-DESS-Cones versus DW-DESS-Cartesian (p < 0.05). Lesion-to-fibroglandular tissue signal ratios were not correlated between DW-DESS-Cones and DWI (r = 0.25, p = 0.38). Concordant hyperintensity/hypointensity was observed between DW-DESS-Cones and DWI in 11/14 lesions. DW-DESS-Cones improved sharpness, distortion, and overall image quality versus DWI. DW-DESS-Cones may be able to eliminate motion artifacts in the breast allowing for investigation of higher degrees of steady-state diffusion weighting. Malignant breast lesions in DW-DESS-Cones demonstrated hyperintensity with respect to surrounding tissue without an injection of contrast. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 1.

    View details for DOI 10.1002/jmri.27492

    View details for PubMedID 33382171

  • Unsupervised clustering method to convert high-resolution magnetic resonance volumes to three-dimensional acoustic models for full-wave ultrasound simulations. Journal of medical imaging (Bellingham, Wash.) Looby, K. n., Herickhoff, C. D., Sandino, C. n., Zhang, T. n., Vasanawala, S. n., Dahl, J. J. 2019; 6 (3): 037001

    Abstract

    Simulations of acoustic wave propagation, including both the forward and the backward propagations of the wave (also known as full-wave simulations), are increasingly utilized in ultrasound imaging due to their ability to more accurately model important acoustic phenomena. Realistic anatomic models, particularly those of the abdominal wall, are needed to take full advantage of the capabilities of these simulation tools. We describe a method for converting fat-water-separated magnetic resonance imaging (MRI) volumes to anatomical models for ultrasound simulations. These acoustic models are used to map acoustic imaging parameters, such as speed of sound and density, to grid points in an ultrasound simulation. The tissues of these models are segmented from the MRI volumes into five primary classes of tissue in the human abdominal wall (skin, fat, muscle, connective tissue, and nontissue). This segmentation is achieved using an unsupervised machine learning algorithm, fuzzy c-means clustering (FCM), on a multiscale feature representation of the MRI volumes. We describe an automated method for utilizing FCM weights to produce a model that achieves ∼ 90 % agreement with manual segmentation. Two-dimensional (2-D) and three-dimensional (3-D) full-wave nonlinear ultrasound simulations are conducted, demonstrating the utility of realistic 3-D abdominal wall models over previously available 2-D abdominal wall models.

    View details for DOI 10.1117/1.JMI.6.3.037001

    View details for PubMedID 31338389

    View details for PubMedCentralID PMC6643101

  • Near-silent distortionless DWI using magnetization-prepared RUFIS. Magnetic resonance in medicine Yuan, J. n., Hu, Y. n., Menini, A. n., Sandino, C. M., Sandberg, J. n., Sheth, V. n., Moran, C. J., Alley, M. n., Lustig, M. n., Hargreaves, B. n., Vasanawala, S. n. 2019

    Abstract

    To develop a near-silent and distortionless DWI (sd-DWI) sequence using magnetization-prepared rotating ultrafast imaging sequence.A rotating ultrafast imaging sequence was modified with driven-equilibrium diffusion preparation, including eddy-current compensation methods. To compensate for the T1 recovery during readout, a phase-cycling method was used. Both compensation methods were validated in phantoms. The optimized sequence was compared with an EPI diffusion sequence for image distortion, contrast, ADC, and acoustic noise level in phantoms. The sequence was evaluated in 1 brain volunteer, 5 prostate volunteers, and 10 pediatric patients with joint diseases.Combination of several eddy-current compensation methods reduced the artifact to an acceptable level. Phase cycling reduced T1 recovery contamination during readout. In phantom scans, the optimized sequence generated similar image contrast to the EPI diffusion sequence, and ADC maps between the sequences were comparable; sd-DWI had significantly lower acoustic noise (P < .05). In vivo brain scan showed reduced image distortion in sd-DWI compared with the EPI diffusion, although residual motion artifact remains due to brain pulsation. The prostate scans showed that sd-DWI can provide similar ADC compared with EPI diffusion, with no image distortion. Patient scans showed that the sequence can clearly depict joint lesions.An sd-DWI sequence was developed and optimized. Compared with conventional EPI diffusion, sd-DWI provided similar diffusion contrast, accurate ADC measurement, improved image quality, and minimal ambient scanning noise. The sequence showed the ability to obtain in vivo diffusion contrast in relatively motion-free body regions, such as prostate and joint.

    View details for DOI 10.1002/mrm.28106

    View details for PubMedID 31782557

  • K-Means Clustering for High-Resolution, Realistic Acoustic Maps Looby, K., Sandino, C., Zhang, T., Vasanawala, S., Dahl, J., Duric, N., Byram, B. C. SPIE-INT SOC OPTICAL ENGINEERING. 2018

    View details for DOI 10.1117/12.2293990

    View details for Web of Science ID 000450861900027