Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
651-02-005 / 651-02-005 ISMRM Abstract

TRACED: a novel diffusion model for characterizing extracellular diffusivity, tortuosity, and cell size and density in tumors

Primary: Diffusion - Diffusion Modeling
Secondary: Diffusion - Microstructure
651-02-005 · Diffusion Modeling To Map Tissue Microstructure · Thursday, 14 May, 1:40 PM–3:16 PM · Power Pitch Theatre 1
651-02-005 · Diffusion Modeling To Map Tissue Microstructure · Thursday, 14 May, 1:40 PM–3:16 PM · Power Pitch Theatre 1
Keywords: Microstructure Diffusion MRI Physics-informed machine learning Time-dependent diffusion MRI Computational modeling
Accepted
Joshua K Marchant 1,2, Hong Hsi Lee1,3, Elizabeth R Gerstner1,4,5, Susie Huang1,2,3, Bruce Rosen1,2,3
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States of America
2Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, United States of America
3Harvard Medical School, Boston, United States of America
4Department of Neurology, Massachusetts General Hospital, Boston, United States of America
5Mass General Brigham Cancer Institute, Boston, United States of America
Presenting Author: Joshua K Marchant

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.

Log in

References

1. Jiang, X.; Li, H.; Xie, J.; Zhao, P.; Gore, J. C.; Xu, J. Quantification of Cell Size Using Temporal Diffusion Spectroscopy. Magn. Reson. Med. 2016, 75 (3), 1076–1085. https://doi.org/10.1002/mrm.25684. [doi]
2. Jiang, X.; Li, H.; Xie, J.; McKinley, E. T.; Zhao, P.; Gore, J. C.; Xu, J. In Vivo Imaging of Cancer Cell Size and Cellularity Using Temporal Diffusion Spectroscopy. Magn. Reson. Med. 2017, 78 (1), 156–164. https://doi.org/10.1002/mrm.26356. [doi]
3. Jiang, X.; Devan, S. P.; Xie, J.; Gore, J. C.; Xu, J. Improving MR Cell Size Imaging by Inclusion of Transcytolemmal Water Exchange. NMR Biomed. 2022, 35 (12), e4799. https://doi.org/10.1002/nbm.4799. [doi]
4. Xu, J.; Jiang, X.; Li, H.; Arlinghaus, L. R.; McKinley, E. T.; Devan, S. P.; Hardy, B. M.; Xie, J.; Kang, H.; Chakravarthy, A. B.; Gore, J. C. Magnetic Resonance Imaging of Mean Cell Size in Human Breast Tumors. Magn. Reson. Med. 2020, 83 (6), 2002–2014. https://doi.org/10.1002/mrm.28056. [doi]
5. Xu, J.; Jiang, X.; Devan, S. P.; Arlinghaus, L. R.; McKinley, E. T.; Xie, J.; Zu, Z.; Wang, Q.; Chakravarthy, A. B.; Wang, Y.; Gore, J. C. MRI‐cytometry: Mapping Nonparametric Cell Size Distributions Using Diffusion MRI. Magn. Reson. Med. 2021, 85 (2), 748–761. https://doi.org/10.1002/mrm.28454. [doi]
6. Devan, S. P.; Jiang, X.; Luo, G.; Xie, J.; Quirk, J. D.; Engelbach, J. A.; Harmsen, H.; McKinley, E. T.; Cui, J.; Zu, Z.; Attia, A.; Garbow, J. R.; Gore, J. C.; McKnight, C. D.; Kirschner, A. N.; Xu, J. Selective Cell Size MRI Differentiates Brain Tumors from Radiation Necrosis. Cancer Res. 2022, 82 (19), 3603–3613. https://doi.org/10.1158/0008-5472.CAN-21-2929. [doi]
7. Bao, L.; Li, S.; Wang, Z.; Sun, Y.; Qiu, Y.; Shen, Z.; Zhang, X.; Chen, X.; Zhang, X.; Zhang, J.; Ji, T. Advantages of Time-Dependent Diffusion MRI for Quantitative Microstructural Mapping in Breast Tumors. Front. Oncol. 2025, 15, 1537529. https://doi.org/10.3389/fonc.2025.1537529. [doi]
8. Marchant, J. K.; Rosen, B. R. Bridging the Resolution Gap in Alpha Therapy Dosimetry: A Space for Quantitative MRI? Phys. Med. Biol. 2025, 70 (19), 19TR01. https://doi.org/10.1088/1361-6560/ae02dd. [doi]
9. Xu, J.; Xie, J.; Semmineh, N. B.; Devan, S. P.; Jiang, X.; Gore, J. C. Diffusion Time Dependency of Extracellular Diffusion. Magn. Reson. Med. 2023, 89 (6), 2432–2440. https://doi.org/10.1002/mrm.29594. [doi]
10. Murday, J. S.; Cotts, R. M. Self-Diffusion Coefficient of Liquid Lithium. J. Chem. Phys. 1968, 48 (11), 4938–4945. https://doi.org/10.1063/1.1668160. [doi]
11. Lee, H.; Novikov, D. S.; Fieremans, E.; Huang, S. Y. Revealing Membrane Integrity and Cell Size from Diffusion Kurtosis Time Dependence. Magn. Reson. Med. 2025, 93 (3), 1329–1347. https://doi.org/10.1002/mrm.30335. [doi]
12. Lee, H.-H.; Fieremans, E.; Novikov, D. S. Realistic Microstructure Simulator (RMS): Monte Carlo Simulations of Diffusion in Three-Dimensional Cell Segmentations of Microscopy Images. J. Neurosci. Methods 2021, 350, 109018. https://doi.org/10.1016/j.jneumeth.2020.109018. [doi]
13. Herman, J.; Usher, W. SALib: An Open-Source Python Library for Sensitivity Analysis. J. Open Source Softw. 2017, 2 (9), 97. https://doi.org/10.21105/joss.00097. [doi]
14. Iwanaga, T.; Usher, W.; Herman, J. Toward SALib 2.0: Advancing the Accessibility and Interpretability of Global Sensitivity Analyses. Socio-Environ. Syst. Model. 2022, 4, 18155. https://doi.org/10.18174/sesmo.18155. [doi]
15. Setsompop, K.; Kimmlingen, R.; Eberlein, E.; Witzel, T.; Cohen-Adad, J.; McNab, J. A.; Keil, B.; Tisdall, M. D.; Hoecht, P.; Dietz, P.; Cauley, S. F.; Tountcheva, V.; Matschl, V.; Lenz, V. H.; Heberlein, K.; Potthast, A.; Thein, H.; Van Horn, J.; Toga, A.; Schmitt, F.; Lehne, D.; Rosen, B. R.; Wedeen, V.; Wald, L. L. Pushing the Limits of in Vivo Diffusion MRI for the Human Connectome Project. NeuroImage 2013, 80, 220–233. https://doi.org/10.1016/j.neuroimage.2013.05.078. [doi]
16. Song, Y.; Ly, I.; Fan, Q.; Nummenmaa, A.; Martinez-Lage, M.; Curry, W. T.; Dietrich, J.; Forst, D. A.; Rosen, B. R.; Huang, S. Y.; Gerstner, E. R. Measurement of Full Diffusion Tensor Distribution Using High-Gradient Diffusion MRI and Applications in Diffuse Gliomas. Front. Phys. 2022, 10, 813475. https://doi.org/10.3389/fphy.2022.813475. [doi]
17. Veraart, J.; Novikov, D. S.; Christiaens, D.; Ades-aron, B.; Sijbers, J.; Fieremans, E. Denoising of Diffusion MRI Using Random Matrix Theory. NeuroImage 2016, 142, 394–406. https://doi.org/10.1016/j.neuroimage.2016.08.016. [doi]
18. Veraart, J.; Fieremans, E.; Novikov, D. S. Diffusion MRI Noise Mapping Using Random Matrix Theory. Magn. Reson. Med. 2016, 76 (5), 1582–1593. https://doi.org/10.1002/mrm.26059. [doi]
19. Tournier, J.-D.; Jeurissen, B.; Christiaens, D. Iterative Model-Based Rician Bias Correction and Its Application to Denoising in Diffusion MRI; Toronto, ON, Canada; p 3795. https://doi.org/10.58530/2023/3795. [doi]
20. Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Köpf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; Chintala, S. PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv 2019. https://doi.org/10.48550/ARXIV.1912.01703. [doi]
21. Bingham, E.; Chen, J. P.; Jankowiak, M.; Obermeyer, F.; Pradhan, N.; Karaletsos, T.; Singh, R.; Szerlip, P.; Horsfall, P.; Goodman, N. D. Pyro: Deep Universal Probabilistic Programming. arXiv 2018. https://doi.org/10.48550/ARXIV.1810.09538. [doi]
22. Novikov, D. S.; Jensen, J. H.; Helpern, J. A.; Fieremans, E. Revealing Mesoscopic Structural Universality with Diffusion. Proc. Natl. Acad. Sci. 2014, 111 (14), 5088–5093. https://doi.org/10.1073/pnas.1316944111. [doi]
23. Novikov, D. S.; Kiselev, V. G. Effective Medium Theory of a Diffusion‐weighted Signal. NMR Biomed. 2010, 23 (7), 682–697. https://doi.org/10.1002/nbm.1584. [doi]
24. Memmel, S.; Sukhorukov, V. L.; Höring, M.; Westerling, K.; Fiedler, V.; Katzer, A.; Krohne, G.; Flentje, M.; Djuzenova, C. S. Cell Surface Area and Membrane Folding in Glioblastoma Cell Lines Differing in PTEN and P53 Status. PLoS ONE 2014, 9 (1), e87052. https://doi.org/10.1371/journal.pone.0087052. [doi]
25. Eidel, O.; Burth, S.; Neumann, J.-O.; Kieslich, P. J.; Sahm, F.; Jungk, C.; Kickingereder, P.; Bickelhaupt, S.; Mundiyanapurath, S.; Bäumer, P.; Wick, W.; Schlemmer, H.-P.; Kiening, K.; Unterberg, A.; Bendszus, M.; Radbruch, A. Tumor Infiltration in Enhancing and Non-Enhancing Parts of Glioblastoma: A Correlation with Histopathology. PLOS ONE 2017, 12 (1), e0169292. https://doi.org/10.1371/journal.pone.0169292. [doi]
26. Gates, E. D. H.; Weinberg, J. S.; Prabhu, S. S.; Lin, J. S.; Hamilton, J.; Hazle, J. D.; Fuller, G. N.; Baladandayuthapani, V.; Fuentes, D. T.; Schellingerhout, D. Estimating Local Cellular Density in Glioma Using MR Imaging Data. AJNR Am. J. Neuroradiol. 2021, 42 (1), 102–108. https://doi.org/10.3174/ajnr.A6884. [doi]
27. Sen, P. N.; Scala, C.; Cohen, M. H. A Self‐similar Model for Sedimentary Rocks with Application to the Dielectric Constant of Fused Glass Beads. GEOPHYSICS 1981, 46 (5), 781–795. https://doi.org/10.1190/1.1441215. [doi]
28. Tomadakis, M. M.; Sotirchos, S. V. Transport Properties of Random Arrays of Freely Overlapping Cylinders with Various Orientation Distributions. J. Chem. Phys. 1993, 98 (1), 616–626. https://doi.org/10.1063/1.464604. [doi]
29. Latour, L. L.; Svoboda, K.; Mitra, P. P.; Sotak, C. H. Time-Dependent Diffusion of Water in a Biologicalmodel System. Proc. Natl. Acad. Sci. 1994, 91 (4), 1229–1233. https://doi.org/10.1073/pnas.91.4.1229. [doi]
30. Mitra, P. P.; Sen, P. N.; Schwartz, L. M. Short-Time Behavior of the Diffusion Coefficient as a Geometrical Probe of Porous Media. Phys. Rev. B 1993, 47 (14), 8565–8574. https://doi.org/10.1103/PhysRevB.47.8565. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/651-02-005