Jinyang Yu 1,2, Oliver Gödicke1,3, Frederik B Laun4, Mark E Ladd1,3,5, David Bonekamp5,6,7, Tristan A Kuder1,3
1Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
2Faculty of Engineering Sciences, Heidelberg University, Heidelberg, Germany
3Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
4Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
5Faculty of Medicine, Heidelberg University, Heidelberg, Germany
6Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
7National Center for Tumor Diseases (NCT), Heidelberg, Germany
Presenting Author: Jinyang Yu
Synopsis
Motivation:
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1. Westin, C. F., Knutsson, H., Pasternak, O., Szczepankiewicz, F., Özarslan, E., van Westen, D., ... & Nilsson, M. (2016). Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. Neuroimage, 135, 345-362.
https://doi.org/10.1016/j.neuroimage.2016.02.039 [doi]
2. Coelho, S., Chen, J., Szczepankiewicz, F., Fieremans, E., & Novikov, D. S. (2025). Diffusion MRI invariants: from the group of rotations to a complete neuroimaging fingerprint. arXiv preprint arXiv:2409.03010.
https://doi.org/10.48550/arXiv.2409.03010 [doi]
3. Herberthson, M., Boito, D., Haije, T. D., Feragen, A., Westin, C. F., & Özarslan, E. (2021). Q-space trajectory imaging with positivity constraints (QTI+). NeuroImage, 238, 118198.
https://doi.org/10.1016/j.neuroimage.2021.118198 [doi]
4. Boito, D., Herberthson, M., Haije, T. D., Blystad, I., & Özarslan, E. (2023). Diffusivity-limited q-space trajectory imaging. Magnetic Resonance Letters, 3(2), 187-196.
https://doi.org/10.1016/j.mrl.2022.12.003 [doi]
5. Lampinen, B., Szczepankiewicz, F., Lätt, J., Knutsson, L., Mårtensson, J., Björkman-Burtscher, I. M., ... & Nilsson, M. (2023). Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. NeuroImage, 282, 120338.
https://doi.org/10.1016/j.neuroimage.2023.120338 [doi]
6. Szczepankiewicz, F., van Westen, D., Englund, E., Westin, C. F., Ståhlberg, F., Lätt, J., ... & Nilsson, M. (2016). The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE). Neuroimage, 142, 522-532.
https://doi.org/10.1016/j.neuroimage.2016.07.038 [doi]
7. Goedicke, O., Laun F., Martin J., Rauch J., Neher P., Rokuss M., Ladd M., Kuder T. Accelerated Microstructure Quantification by Q-Space Trajectory Imaging Using Machine Learning. Proc. Int. Soc. Mag. Reson. Med. 32;2024:3464.
https://doi.org/10.58530/2024/3464 [doi]
8. Yu, J., Goedicke, O., Laun F., Ladd M., Kuder T. Microstructure Quantification by Q-Space Trajectory Imaging via Unrolled Neural Networks: Exploring Model Generalizability. Proc. Int. Soc. Mag. Reson. Med. 33;2025:1283.
https://doi.org/10.58530/2025/1283 [doi]
9. Martin, J., Endt, S., Wetscherek, A., Kuder, T. A., Doerfler, A., Uder, M., ... & Laun, F. B. (2020). Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging. Zeitschrift für Medizinische Physik, 30(1), 4-16.
https://doi.org/10.1016/j.zemedi.2019.01.003 [doi]
10. Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. P. (2009). Elastix: a toolbox for intensity-based medical image registration. IEEE transactions on medical imaging, 29(1), 196-205.
https://doi.org/10.1109/TMI.2009.2035616 [doi]
11. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782-790.
https://doi.org/10.1016/j.neuroimage.2011.09.015 [doi]
12. Tournier, J. D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., ... & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage, 202, 116137.
https://doi.org/10.1016/j.neuroimage.2019.116137 [doi]
13. Diamond, S., & Boyd, S. (2016). CVXPY: A Python-embedded modeling language for convex optimization. Journal of Machine Learning Research, 17(83), 1-5.
https://doi.org/10.48550/arXiv.1603.00943 [doi]
14. Agrawal, A., Verschueren, R., Diamond, S., & Boyd, S. (2018). A rewriting system for convex optimization problems. Journal of Control and Decision, 5(1), 42-60.
https://doi.org/10.1080/23307706.2017.1397554 [doi]
15. ApS, M. (2020). Mosek modeling cookbook.
https://www.mosek.com/documentation.
16. Reymbaut, A., Mezzani, P., de Almeida Martins, J. P., & Topgaard, D. (2020). Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms. NMR in Biomedicine, 33(12), e4267.
https://doi.org/10.1002/nbm.4267 [doi]
17. Karan, P., Reymbaut, A., Gilbert, G., & Descoteaux, M. (2022). Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor‐valued diffusion MRI. Medical Image Analysis, 79, 102476.
https://doi.org/10.1016/j.media.2022.102476 [doi]
18. Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., ... & Dipy Contributors. (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in neuroinformatics, 8, 8.
https://doi.org/10.3389/fninf.2014.00008 [doi]