Ariel Hannum

All Publications

• Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising. Investigative radiology Wang, K., Middione, M. J., Loening, A. M., Syed, A. B., Hannum, A. J., Wang, X., Guidon, A., Lan, P., Ennis, D. B., Brunsing, R. L. 2025

  Abstract

  Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal. Thus, a method that combines msDWI with MCGs while minimizing the echo time penalty and maximizing signal would improve pancreatic DWI. In this work, we combine MCGs generated via convex-optimized diffusion encoding (CODE), which reduces the echo time penalty of motion compensation, with deep learning (DL)-based denoising to address residual signal loss. We hypothesize this method will qualitatively and quantitatively improve msDWI of the pancreas.This prospective institutional review board-approved study included 22 patients who underwent abdominal MR examinations from August 22, 2022 and May 17, 2023 on 3.0 T scanners. Following informed consent, 2-shot spin-echo echo-planar DWI (b = 0, 800 s/mm2) without (M0) and with (M1) CODE-generated first-order gradient moment nulling was added to their clinical examinations. DL-based denoising was applied to the M1 images (M1 + DL) off-line. ADC maps were reconstructed for all 3 methods. Blinded pair-wise comparisons of b = 800 s/mm2 images were done by 3 subspecialist radiologists. Five metrics were compared: pancreatic boundary delineation, motion artifacts, signal homogeneity, perceived noise, and diagnostic preference. Regions of interest of the pancreatic head, body, and tail were drawn, and mean ADC values were computed. Repeated analysis of variance and post hoc pairwise t test with Bonferroni correction were used for comparing mean ADC values. Bland-Altman analysis compared mean ADC values. Reader preferences were tabulated and compared using Wilcoxon signed rank test with Bonferroni correction and Fleiss κ.M1 was significantly preferred over M0 for perceived motion artifacts and signal homogeneity (P < 0.001). M0 was significantly preferred over M1 for perceived noise (P < 0.001), but DL-based denoising (M1 + DL) reversed this trend and was significantly favored over M0 (P < 0.001). ADC measurements from M0 varied between different regions of the pancreas (P = 0.001), whereas motion correction with M1 and M1 + DL resulted in homogeneous ADC values (P = 0.24), with values similar to those reported for ssDWI with motion correction. ADC values from M0 were significantly higher than M1 in the head (bias 16.6%; P < 0.0001), body (bias 11.0%; P < 0.0001), and tail (bias 8.6%; P = 0.001). A small but significant bias (2.6%) existed between ADC values from M1 and M1 + DL.CODE-generated motion compensating gradients improves multishot pancreatic DWI as interpreted by expert readers and eliminated ADC variation throughout the pancreas. DL-based denoising mitigated signal losses from motion compensation while maintaining ADC consistency. Integrating both techniques could improve the accuracy and reliability of multishot pancreatic DWI.

  View details for DOI 10.1097/RLI.0000000000001148

  View details for PubMedID 39823511

• Phase stabilization with motion compensated diffusion weighted imaging. Magnetic resonance in medicine Hannum, A. J., Cork, T. E., Setsompop, K., Ennis, D. B. 2024

  Abstract

  Diffusion encoding gradient waveforms can impart intra-voxel and inter-voxel dephasing owing to bulk motion, limiting achievable signal-to-noise and complicating multishot acquisitions. In this study, we characterize improvements in phase consistency via gradient moment nulling of diffusion encoding waveforms.Healthy volunteers received neuro ( N = 10 $$ N=10 $$ ) and cardiac ( N = 10 $$ N=10 $$ ) MRI. Three gradient moment nulling levels were evaluated: compensation for position ( M 0 $$ {M}_0 $$ ), position + velocity ( M 1 $$ {M}_1 $$ ), and position + velocity + acceleration ( M 1 + M 2 $$ {M}_1+{M}_2 $$ ). Three experiments were completed: (Exp-1) Fixed Trigger Delay Neuro DWI; (Exp-2) Mixed Trigger Delay Neuro DWI; and (Exp-3) Fixed Trigger Delay Cardiac DWI. Significant differences ( p < 0 . 05 $$ p<0.05 $$ ) of the temporal phase SD between repeated acquisitions and the spatial phase gradient across a given image were assessed.M 0 $$ {M}_0 $$ moment nulling was a reference for all measures. In Exp-1, temporal phase SD for G z $$ {G}_z $$ diffusion encoding was significantly reduced with M 1 $$ {M}_1 $$ (35% of t-tests) and M 1 + M 2 $$ {M}_1+{M}_2 $$ (68% of t-tests). The spatial phase gradient was reduced in 23% of t-tests for M 1 $$ {M}_1 $$ and 2% of cases for M 1 + M 2 $$ {M}_1+{M}_2 $$ . In Exp-2, temporal phase SD significantly decreased with M 1 + M 2 $$ {M}_1+{M}_2 $$ gradient moment nulling only for G z $$ {G}_z $$ (83% of t-tests), but spatial phase gradient significantly decreased with only M 1 $$ {M}_1 $$ (50% of t-tests). In Exp-3, M 1 + M 2 $$ {M}_1+{M}_2 $$ gradient moment nulling significantly reduced temporal phase SD and spatial phase gradients (100% of t-tests), resulting in less signal attenuation and more accurate ADCs.We characterized gradient moment nulling phase consistency for DWI. Using M1 for neuroimaging and M1 + M2 for cardiac imaging minimized temporal phase SDs and spatial phase gradients.

  View details for DOI 10.1002/mrm.30218

  View details for PubMedID 38997801

• An nnU-Net Model to Enhance Segmentation of Cardiac Cine DENSE-MRI Using Phase Information Jahromi, M., Marques, A., Ahmed, M., Liu, Z., Hannum, A. J., Ennis, D. B., Perotti, L. E., Wu, D., IEEE COMPUTER SOC IEEE COMPUTER SOC. 2024: 670-673

  View details for DOI 10.1109/ICHI61247.2024.00106

  View details for Web of Science ID 001304501700099

• 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

• Correlated noise in brain magnetic resonance elastography MAGNETIC RESONANCE IN MEDICINE Hannum, A. J., McIlvain, G., Sowinski, D., McGarry, M. J., Johnson, C. L. 2022; 87 (3): 1313-1328

  Abstract

  Magnetic resonance elastography (MRE) uses phase-contrast MRI to generate mechanical property maps of the in vivo brain through imaging of tissue deformation from induced mechanical vibration. The mechanical property estimation process in MRE can be susceptible to noise from physiological and mechanical sources encoded in the phase, which is expected to be highly correlated. This correlated noise has yet to be characterized in brain MRE, and its effects on mechanical property estimates computed using inversion algorithms are undetermined.To characterize the effects of signal noise in MRE, we conducted 3 experiments quantifying (1) physiomechanical sources of signal noise, (2) physiological noise because of cardiac-induced movement, and (3) impact of correlated noise on mechanical property estimates. We use a correlation length metric to estimate the extent that correlated signal persists in MRE images and demonstrate the effect of correlated noise on property estimates through simulations.We found that both physiological noise and vibration noise were greater than image noise and were spatially correlated across all subjects. Added physiological and vibration noise to simulated data resulted in property maps with higher error than equivalent levels of Gaussian noise.Our work provides the foundation to understand contributors to brain MRE data quality and provides recommendations for future work to correct for signal noise in MRE.

  View details for DOI 10.1002/mrm.29050

  View details for Web of Science ID 000710004900001

  View details for PubMedID 34687069

  View details for PubMedCentralID PMC8776601

• Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI. Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Loecher, M., Hannum, A. J., Perotti, L. E., Ennis, D. B. 2021; 12738: 213-222

  Abstract

  Cardiac tagged MR images allow for deformation fields to be measured in the heart by tracking the motion of tag lines throughout the cardiac cycle. Machine learning (ML) algorithms enable accurate and robust tracking of tag lines. Herein, the use of a massive synthetic physics-driven training dataset with known ground truth was used to train an ML network to enable tracking any number of points at arbitrary positions rather than anchored to the tag lines themselves. The tag tracking and strain calculation methods were investigated in a computational deforming cardiac phantom with known (ground truth) strain values. This enabled both tag tracking and strain accuracy to be characterized for a range of image acquisition and tag tracking parameters. The methods were also tested on in vivo volunteer data. Median tracking error was <0.26mm in the computational phantom, and strain measurements were improved in vivo when using the arbitrary point tracking for a standard clinical protocol.

  View details for DOI 10.1007/978-3-030-78710-3_21

  View details for PubMedID 34590079