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Lab Officially Launches on September 1, 2026
Published:
The Intelligent MRI Lab will officially launch at the University of Massachusetts Amherst on September 1, 2026.
portfolio
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publications
Rsanet: Recurrent slice-wise attention network for multiple sclerosis lesion segmentation
MICCAI, 2019.
Citation: Zhang, H., Zhang, J., Zhang, Q., Kim, J., Zhang, S., Gauthier, S.A., Spincemaille, P., Nguyen, T.D., Sabuncu, M. and Wang, Y. (2019). "Rsanet: Recurrent slice-wise attention network for multiple sclerosis lesion segmentation." MICCAI, 411-419.
Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction
NeuroImage, 2020.
Citation: Zhang, J., Liu, Z., Zhang, S., Zhang, H., Spincemaille, P., Nguyen, T.D., Sabuncu, M.R. and Wang, Y. (2020). "Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction." NeuroImage, 211, 116579.
Bayesian learning of probabilistic dipole inversion for quantitative susceptibility mapping
Medical Imaging with Deep Learning (MIDL), 2020.
Citation: Zhang, J., Zhang, H., Sabuncu, M., Spincemaille, P., Nguyen, T. and Wang, Y. (2020). "Bayesian learning of probabilistic dipole inversion for quantitative susceptibility mapping." Medical Imaging with Deep Learning (MIDL).
Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI
MLMI at MICCAI, 2020.
Citation: Zhang, J., Zhang, H., Wang, A., Zhang, Q., Sabuncu, M., Spincemaille, P., Nguyen, T.D. and Wang, Y. (2020). "Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI." MLMI@MICCAI.
ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation
NeuroImage: Clinical, 2021.
Citation: Zhang, H., Zhang, J., Li, C., Sweeney, E.M., Spincemaille, P., Nguyen, T.D., Gauthier, S.A., Wang, Y. and Marcille, M. (2021). "ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation." NeuroImage: Clinical, 32, 102854.
Efficient Folded Attention for Medical Image Reconstruction and Segmentation
AAAI, 2021.
Citation: Zhang, H., Zhang, J., Wang, R., Zhang, Q., Spincemaille, P., Nguyen, T.D. and Wang, Y. (2021). "Efficient Folded Attention for Medical Image Reconstruction and Segmentation." AAAI, 35(12), 10868-10876.
Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mapping
Machine Learning for Biomedical Imaging, 2021.
Citation: Zhang, J., Zhang, H., Sabuncu, M., Spincemaille, P., Nguyen, T. and Wang, Y. (2021). "Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mapping." Machine Learning for Biomedical Imaging, 1, 1-19.
Deep neural network for water/fat separation: supervised training, unsupervised training, and no training
Magnetic Resonance in Medicine, 2021.
Citation: Jafari, R., Spincemaille, P., Zhang, J., Nguyen, T.D., Luo, X., Cho, J., Margolis, D., Prince, M.R. and Wang, Y. (2021). "Deep neural network for water/fat separation: supervised training, unsupervised training, and no training." Magnetic Resonance in Medicine, 85(4), 2263-2277.
Geometric Loss for Deep Multiple Sclerosis lesion Segmentation
IEEE ISBI, 2021.
Citation: Zhang, H., Zhang, J., Wang, R., Zhang, Q., Gauthier, S.A., Spincemaille, P., Nguyen, T.D. and Wang, Y. (2021). "Geometric Loss for Deep Multiple Sclerosis lesion Segmentation." IEEE ISBI.
Hybrid optimization between iterative and network fine-tuning reconstructions for fast quantitative susceptibility mapping
Medical Imaging with Deep Learning (MIDL), 2021.
Citation: Zhang, J., Zhang, H., Spincemaille, P., Nguyen, T., Sabuncu, M.R. and Wang, Y. (2021). "Hybrid optimization between iterative and network fine-tuning reconstructions for fast quantitative susceptibility mapping." MIDL.
Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction
MICCAI, 2021.
Citation: Zhang, J., Zhang, H., Li, C., Spincemaille, P., Sabuncu, M., Nguyen, T.D. and Wang, Y. (2021). "Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction." MICCAI.
QQ-NET–using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude based oxygen extraction fraction mapping
Magnetic Resonance in Medicine, 2022.
Citation: Cho, J., Zhang, J., Spincemaille, P., Zhang, H., Hubertus, S., Wen, Y., Jafari, R., Zhang, S., Nguyen, T.D., Dimov, A.V. and Gupta, A. (2022). "QQ-NET–using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude based oxygen extraction fraction mapping." Magnetic Resonance in Medicine, 87(3), 1583-1594.
QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps
NeuroImage: Clinical, 2022.
Citation: Zhang, H., Nguyen, T.D., Zhang, J., Marcille, M., Spincemaille, P., Wang, Y., Gauthier, S.A. and Sweeney, E.M. (2022). "QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps." NeuroImage: Clinical, 34, 102979.
Subsecond accurate myelin water fraction reconstruction from FAST-T2 data with 3D UNET
Magnetic Resonance in Medicine, 2022.
Citation: Kim, J., Nguyen, T.D., Zhang, J., Gauthier, S.A., Marcille, M., Zhang, H., Cho, J., Spincemaille, P. and Wang, Y. (2022). "Subsecond accurate myelin water fraction reconstruction from FAST-T2 data with 3D UNET." Magnetic Resonance in Medicine, 87(6), 2979-2988.
LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping
NeuroImage, 2023.
Citation: Zhang, J., Spincemaille, P., Zhang, H., Nguyen, T.D., Li, C., Li, J., Kovanlikaya, I., Sabuncu, M.R. and Wang, Y. (2023). "LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping." NeuroImage, 268, 119886.
Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs
Frontiers in Neuroscience, 2023.
Citation: Si, W., Guo, Y., Zhang, Q., Zhang, J., Wang, Y. and Feng, Y. (2023). "Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs." Frontiers in Neuroscience, 17, 1165446.
Spatially Covariant Lesion Segmentation
IJCAI, 2023.
Citation: Zhang, H., Wang, R., Zhang, J., Liu, D., Li, C., Li, J. (2023). "Spatially Covariant Lesion Segmentation." IJCAI, 1713-1721.
DEDA: Deep directed accumulator
MICCAI, 2023.
Citation: Zhang, H., Wang, R., Hu, R., Zhang, J. and Li, J. (2023). "DEDA: Deep directed accumulator." MICCAI, 765-775.
mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping
Magnetic Resonance in Medicine, 2024.
Citation: Zhang, J., Nguyen, T.D., Solomon, E., Li, C., Zhang, Q., Li, J., Zhang, H., Spincemaille, P. and Wang, Y. (2024). "mcLARO: Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping." Magnetic Resonance in Medicine, 91(1), 344-356.
Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude for Oxygen Extraction Fraction Mapping
Bioengineering, 2024.
Citation: Cho, J., Zhang, J., Spincemaille, P., Zhang, H., Nguyen, T.D., Zhang, S., Gupta, A. and Wang, Y. (2024). "Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude for Oxygen Extraction Fraction Mapping." Bioengineering, 11(2), 131.
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
SPIE Medical Imaging, 2024.
Citation: Zhang, J., Zuo, L., Dewey, B.E., Remedios, S.W., Hays, S.P., Pham, D.L., Prince, J.L. and Carass, A. (2024). "Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation." SPIE Medical Imaging, 12930, 635-641.
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion
IEEE ISBI, 2024.
Citation: Zhang, J., Zuo, L., Dewey, B.E., Remedios, S.W., Pham, D.L., Carass, A. and Prince, J.L. (2024). "Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion." IEEE ISBI.
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement
Journal of Medical Imaging, 2025.
Citation: Remedios, S.W., Wei, S., Han, S., Zhang, J., Guo, L., Carass, A., Schilling, K.G., Korotcov, A., Pham, D.L., Prince, J.L. and Dewey, B.E. (2025). "ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement." Journal of Medical Imaging, 13(2), 024001.
Encoder-Only Image Registration
IEEE Transactions on Circuits and Systems for Video Technology, 2025.
Citation: Chen, X., Hu, R., Zhang, J., Zhang, Y., Yue, X., Liu, M., Wang, Y. and Zhang, H. (2025). "Encoder-Only Image Registration." IEEE Transactions on Circuits and Systems for Video Technology.
Navigator motion-resolved MR fingerprinting using implicit neural representation (FINR): feasibility for free-breathing 3D whole-liver multiparametric mapping
Magnetic Resonance in Medicine, 2025.
Citation: Li, C., Li, J., Zhang, J., Solomon, E., Dimov, A.V., Spincemaille, P., Nguyen, T.D., Prince, M.R. and Wang, Y. (2025). "Navigator motion-resolved MR fingerprinting using implicit neural representation (FINR): feasibility for free-breathing 3D whole-liver multiparametric mapping." Magnetic Resonance in Medicine, 95(1), 613-627.
Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson’s Disease
Neurosurgery, 2025.
Citation: Roberts, A., Zhang, J., Tozlu, C., Romano, D., Akkus, S., Kim, H., Sabuncu, M., Spincemaille, P., Li, J., Wang, Y., Wu, X. and Kopell, B. (2025). "Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson’s Disease." Neurosurgery.
Spiral cardiac quantitative susceptibility mapping for differential cardiac chamber oxygenation—Initial validation in relation to invasive blood sampling
Magnetic Resonance in Medicine, 2025.
Citation: Li, J., Villar-Calle, P., Chiu, C., Reza, M., Narula, N., Li, C., Zhang, J., Nguyen, T.D., Wang, Y., Zhang, R.S. and Kim, J. (2025). "Spiral cardiac quantitative susceptibility mapping for differential cardiac chamber oxygenation—Initial validation in relation to invasive blood sampling." Magnetic Resonance in Medicine, 93(5), 2029-2039.
The TRaditional versus Early Aggressive Therapy for MS (TREAT-MS) trial: Design and baseline characteristics of participants
Contemporary Clinical Trials, 2025.
Citation: Mowry, E.M., Qian, P., Meador, W., Lynch, S., Narayan, R., Borazanci, A., ... & treat-MS Trial Team (including Zhang, J.) (2025). "The TRaditional versus Early Aggressive Therapy for MS (TREAT-MS) trial: Design and baseline characteristics of participants." Contemporary Clinical Trials, 108117.
UNISELF: A Unified Network with Instance normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Medical Image Analysis, 2025.
Citation: Zhang, J., Zuo, L., Dewey, B.E., Remedios, S.W., Liu, Y., Hays, S.P., Pham, D.L., Mowry, E.M., Newsome, S.D., Calabresi, P.A., Saidha, S., Carass, A. and Prince, J.L. (2025). "UNISELF: A Unified Network with Instance normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation." Medical Image Analysis, 103954.
Bi-directional MS lesion filling and synthesis using denoising diffusion implicit model-based lesion repainting
SPIE Medical Imaging, 2025.
Citation: Zhang, J., Zuo, L., Liu, Y., Remedios, S., Landman, B.A., Prince, J.L. and Carass, A. (2025). "Bi-directional MS lesion filling and synthesis using denoising diffusion implicit model-based lesion repainting." SPIE Medical Imaging, 13406, 217-223.
Unique MS lesion identification from MRI
SPIE Medical Imaging, 2025.
Citation: Rivas, C.A., Zhang, J., Wei, S., Remedios, S.W., Carass, A. and Prince, J.L. (2025). "Unique MS lesion identification from MRI." SPIE Medical Imaging, 13406, 592-599.
An Unsupervised Approach for Artifact Severity Scoring in Multi-Contrast MR Images
Medical Imaging with Deep Learning (MIDL), 2025.
Citation: Hays, S., Zuo, L., Dewey, B.E., Remedios, S., Zhang, J., Mowry, E.M., Newsome, S.D., Carass, A. and Prince, J.L. (2025). "An Unsupervised Approach for Artifact Severity Scoring in Multi-Contrast MR Images." MIDL.
Unsupervised deformable image registration with structural nonparametric smoothing
IPMI, 2025.
Citation: Zhang, H., Hu, R., Chen, X., Liu, M., Wang, Y., Wang, R., Zhang, J., Li, G., Cheng, X. and Duan, J. (2025). "Unsupervised deformable image registration with structural nonparametric smoothing." IPMI, 108-124.
A Pipeline for DTI Indices and Connectivity Analysis Applied to a Longitudinal Multiple Sclerosis Cohort
ISBI, accepted, 2026.
Citation: Zhou, L., Bian, Z., Zhang, J., Dewey, B., Saidha, S., Calabresi, P., Carass, A. and Prince, J. (2026). "A Pipeline for DTI Indices and Connectivity Analysis Applied to a Longitudinal Multiple Sclerosis Cohort." IEEE ISBI (accepted).
Harmonizing MR Images Across 100+ Scanners: Multi-site Validation with Traveling Subjects and Real-world Protocols
MIDL, accepted, 2026.
Citation: Hays, S.P., Zuo, L., Chaudhary, M.F.A., Bartz, K.M., Remedios, S.W., Zhang, J., Zhuo, J., Bilgel, M., Saidha, S., Mowry, E.M., Newsome, S.D., Prince, J.L., Dewey, B.E. and Carass, A. (2026). "Harmonizing MR Images Across 100+ Scanners: Multi-site Validation with Traveling Subjects and Real-world Protocols." MIDL (accepted).
Feasibility of implicit neural representation learned motion compensation for 3D stack-of-spirals free-breathing cardiac quantitative susceptibility mapping
Magnetic Resonance in Medicine, accepted, 2026.
Citation: Li, J., Deng, A., Li, C., Villar-Calle, P., Zhang, J., Dimov, A.V., Kim, J., Nguyen, T.D., Wang, Y., Weinsaft, J.W. and Spincemaille, P. (2026). "Feasibility of implicit neural representation learned motion compensation for 3D stack-of-spirals free-breathing cardiac quantitative susceptibility mapping." Magnetic Resonance in Medicine (accepted).
Evidence of iron accumulation in cerebral adrenoleukodystrophy: a potential novel disease mechanism
Annals of Clinical and Translational Neurology, accepted, 2026.
Citation: Nemeth, C., Sisman, M., Zhang, J., Shin, H.-G., Li, X., Turk, B., Fatemi, A., Nguyen, T. and Mallack, E. (2026). "Evidence of iron accumulation in cerebral adrenoleukodystrophy: a potential novel disease mechanism." Annals of Clinical and Translational Neurology (accepted).
MRI Quantification of Liver Fibrosis Using Diamagnetic Susceptibility: An Ex Vivo Validation Study
Tomography, 2026.
Citation: Li, C., Zhang, J., Dimov, A.V., Koehne de González, A.K., Prince, M.R., Li, J., Romano, D., Spincemaille, P., Nguyen, T.D., Brittenham, G.M. and Wang, Y. (2026). "MRI Quantification of Liver Fibrosis Using Diamagnetic Susceptibility: An Ex Vivo Validation Study." Tomography, 12(4), 46.
Pipeline refinement on diffusion tractography and T1 tractography in the presence of multiple sclerosis lesions
SPIE Medical Imaging, accepted, 2026.
Citation: Zhou, L., Bian, Z., Zhang, J., Saidha, S., Calabresi, P.A., Carass, A. and Prince, J.L. (2026). "Pipeline refinement on diffusion tractography and T1 tractography in the presence of multiple sclerosis lesions." SPIE Medical Imaging (accepted).
VAID: Valve Artifact Inpainting for Normal Pressure Hydrocephalus from a 3D MRI Diffusion Model
ISBI, accepted, 2026.
Citation: Wang, S., Wei, S., Remedios, S.W., Zhang, J., Dewey, B., Blitz, A., Luciano, M.G., Carass, A. and Prince, J. (2026). "VAID: Valve Artifact Inpainting for Normal Pressure Hydrocephalus from a 3D MRI Diffusion Model." IEEE ISBI (accepted).
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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