Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026
· Cape Town, South Africa
304-02-008
ISMRM Abstract
Reconstructing High-b-Value DWI from a Single Average Using Low-b-Value Side Information
Primary:
Diffusion - Diffusion Reconstruction
Secondary:
Acquisition & Reconstruction - Image Reconstruction: AI
304-02-008 · Diffusion Acquisition and Reconstruction
· Monday, 11 May, 8:20 AM–10:10 AM · Ballroom East
Keywords:Prostate MRIDiffusion-weighted MRIPhysics-guided deep learningDeep-learning-based image reconstructionSide information
Accepted
Arda Atalik 1,2,3, Sumit Chopra2,3,4, Hersh Chandarana2,3, Daniel K Sodickson2,3
1Center for Data Science, New York University, New York, United States of America
2The Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
3The Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
4Courant Institute of Mathematical Sciences, New York University, New York, United States of America
Presenting Author: Arda Atalik
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. Kratzer, T. B., Mazzitelli, N., Star, J., Dahut, W. L., Jemal, A., & Siegel, R. L. (2025). Prostate cancer statistics, 2025. CA: A Cancer Journal for Clinicians. https://doi.org/10.3322/caac.70028 [doi]
2. Tamada, T., Ueda, Y., Ueno, Y., Kojima, Y., Kido, A., & Yamamoto, A. (2022). Diffusion-weighted imaging in prostate cancer. Magnetic Resonance Materials in Physics, Biology and Medicine, 35(4), 533–547. https://doi.org/10.1007/s10334-021-00957-6 [doi]
3. Tan, C. H., Wei, W., Johnson, V., & Kundra, V. (2012). Diffusion-weighted MRI in the detection of prostate cancer: meta-analysis. American Journal of Roentgenology, 199(4), 822–829. https://doi.org/10.2214/AJR.11.7805 [doi]
4. Hyun, C. M., Kim, H. P., Lee, S. M., Lee, S., & Seo, J. K. (2018). Deep learning for undersampled MRI reconstruction. Physics in Medicine & Biology, 63(13), 135007. https://doi.org/10.1088/1361-6560/aac71a [doi]
5. Aggarwal, H. K., Mani, M. P., & Jacob, M. (2018). MoDL: Model-based deep learning architecture for inverse problems. IEEE Transactions on Medical Imaging, 38(2), 394–405. https://doi.org/10.1109/TMI.2018.2865356 [doi]
6. Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T., & Knoll, F. (2018). Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance in Medicine, 79(6), 3055–3071. https://doi.org/10.1002/mrm.26977 [doi]
7. Tibrewala, R., Dutt, T., Tong, A., Ginocchio, L., Lattanzi, R., Keerthivasan, M. B., ... & Johnson, P. M. (2024). FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging. Scientific data, 11(1), 404. https://doi.org/10.1038/s41597-024-03252-w [doi]
8. Ursprung, S., Herrmann, J., Joos, N., Weiland, E., Benkert, T., Almansour, H., ... & Gassenmaier, S. (2023). Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: a retrospective comparison with standard diffusion-weighted imaging. European Journal of Radiology, 165, 110953. https://doi.org/10.1016/j.ejrad.2023.110953 [doi]
9. Ueda, T., Ohno, Y., Yamamoto, K., Murayama, K., Ikedo, M., Yui, M., ... & Toyama, H. (2022). Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology, 303(2), 373–381. https://doi.org/10.1148/radiol.204097 [doi]
10. Atalık, A., Chopra, S., & Sodickson, D. K. (2025, September). Harnessing Side Information for Highly Accelerated MRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 244–254). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-05169-1_24 [doi]
11. Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellus, V., Wang, J., ... & Haase, A. (2002). Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magnetic Resonance in Medicine, 47(6), 1202–1210. https://doi.org/10.1002/mrm.10171 [doi]
12. Uecker, M., Lai, P., Murphy, M. J., Virtue, P., Elad, M., Pauly, J. M., ... & Lustig, M. (2014). ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magnetic Resonance in Medicine, 71(3), 990–1001. https://doi.org/10.1002/mrm.24751 [doi]
13. Landweber, L. (1951). An Iteration Formula for Fredholm Integral Equations of the First Kind. American Journal of Mathematics, 73(3), 615–624. https://doi.org/10.2307/2372313 [doi]
14. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Cham: Springer international publishing. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
15. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861 [doi]
16. Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80–83. https://doi.org/10.2307/3001968 [doi]