Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
431-03-006 / 431-03-006 ISMRM Abstract

Joint multi-sequence reconstruction via a joint conditional diffusion model for highly-accelerated brain tumor MRI

Accepted
Anthony Mekhanik 1, Robert J Young2, Ricardo Otazo1,2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States of America
2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States of America
Presenting Author: Anthony Mekhanik

Synopsis

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References

1. Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79(6):3055-3071.
2. Sriram A, Zbontar J, Murrell T, et al. End-to-end variational networks for accelerated MRI reconstruction. International conference on medical image computing and computer-assisted intervention. 2020:64-73.
3. Xiang L, Chen Y, Chang W, et al. Deep leaning based multi-modal fusion for fast MR reconstruction. IEEE Trans Biomed Eng. 2019;66(7):2105-2114. doi:10.1109/TBME.2018.2883958 [doi]
4. Liu X, Wang J, Sun H, et al. On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks. Magn Reson Imaging. 2021;77:159-168. doi:10.1016/j.mri.2020.12.019 [doi]
5. Liu X, Wang J, Jin J, et al. Deep unregistered multi-contrast MRI reconstruction. Magn Reson Imaging. 2021;81:33-41. doi:10.1016/j.mri.2021.05.005 [doi]
6. Kim KH, Do WJ, Park SH. Improving resolution of MR images with an adversarial network incorporating images with different contrast. Med Phys. 2018;45(7):3120-3131. doi:10.1002/mp.12945 [doi]
7. Atalik A, Chopra S, Sodickson DK. Multi-coil multi-contrast joint reconstruction with protection from hallucination: application to low-field MRI. Proceedings of the International Society for Magnetic Resonance in Medicine. 2025.
8. Do WJ, Seo S, Han Y, Ye JC, Choi SH, Park SH. Reconstruction of multicontrast MR images through deep learning. Med Phys. 2020;47(3):983-997. doi:10.1002/mp.14006 [doi]
9. Sun L, Fan Z, Fu X, Huang Y, Ding X, Paisley J. A deep information sharing network for multi-contrast compressed sensing MRI reconstruction. IEEE Trans Image Process. 2019;28(12):6141-6153. doi: 10.1109/TIP.2019.2925288 [doi]
10. Polak D, Cauley S, Bilgic B, et al. Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging. Magn Reson Med. 2020;84(3):1456-1469. doi:10.1002/mrm.28219 [doi]
11. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Advances in neural information processing systems. 2020:6840-6851.
12. Salimans T, Ho J. Progressive distillation for fast sampling of diffusion models. International conference on learning representations. 2022.
13. Harris RJ, Cloughesy TF, Pope WB, et al. Pre- and post-contrast three-dimensional double inversion-recovery MRI in human glioblastoma. J Neurooncol. 2013;112(2):257-266.
14. Kaufmann TJ, Smits M, Boxerman J, et al. Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases. Neuro Oncol. 2020;22(6):757-772. doi:10.1093/neuonc/noaa030 [doi]
15. Mabray MC, Barajas RF Jr, Cha S. Modern brain tumor imaging. Brain Tumor Res Treat. 2015;3(1):8-23. doi:10.14791/btrt.2015.3.1.8 [doi]

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