Abstract
Diffusion MRI (dMRI) is an advanced imaging technique characterizing tissue microstructure and white matter structural connectivity of the human brain. The demand for high-quality dMRI data is growing, driven by the need for better resolution and improved tissue contrast. However, acquiring high-quality dMRI data is expensive and time-consuming. In this context, deep generative modeling emerges as a promising solution to enhance image quality while minimizing acquisition costs and scanning time. In this study, we propose a novel generative approach to perform dMRI generation using deep diffusion models. It can generate high dimension (4D) and high resolution data preserving the gradients information and brain structure. We demonstrated our method through an image mapping task aimed at enhancing the quality of dMRI images from 3T to 7T. Our approach demonstrates highly enhanced performance in generating dMRI images when compared to the current state-of-the-art (SOTA) methods. This achievement underscores a substantial progression in enhancing dMRI quality, highlighting the potential of our novel generative approach to revolutionize dMRI imaging standards.
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References
Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophysical journal 66(1), 259–267 (1994)
Pannek, K., Scheck, S.M., Colditz, P.B., Boyd, R.N., Rose, S.E.: Magnetic resonance diffusion tractography of the preterm infant brain: a systematic review. Developmental Medicine & Child Neurology 56(2), 113–124 (2014)
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R.E., Yeh, C.H., Zhao, T., O’Donnell, L.J.: Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review. Neuroimage 249, 118870 (2022)
Chilla, G.S., Tan, C.H., Xu, C., Poh, C.L.: Diffusion weighted magnetic resonance imaging and its recent trend-a survey. Quantitative imaging in medicine and surgery 5(3), 407 (2015)
Sotiropoulos, S.N., Hernández-Fernández, M., Vu, A.T., Andersson, J.L., Moeller, S., Yacoub, E., Lenglet, C., Ugurbil, K., Behrens, T.E., Jbabdi, S.: Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project. Neuroimage 134, 396–409 (2016)
Ramos-Llordén, G., Ning, L., Liao, C., Mukhometzianov, R., Michailovich, O., Setsompop, K., Rathi, Y.: High-fidelity, accelerated whole-brain submillimeter in vivo diffusion MRI using gSlider-spherical ridgelets (gSlider-SR). Magnetic resonance in medicine 84(4), 1781–1795 (2020)
Vu, A.T., Auerbach, E., Lenglet, C., Moeller, S., Sotiropoulos, S.N., Jbabdi, S., Andersson, J., Yacoub, E., Ugurbil, K.: High resolution whole brain diffusion imaging at 7T for the Human Connectome Project. Neuroimage 122, 318–331 (2015)
Alexander, D.C., Zikic, D., Zhang, J., Zhang, H., Criminisi, A.: Image quality transfer via random forest regression: applications in diffusion mri. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. pp. 225–232. Springer (2014)
Nedjati-Gilani, G.L., Schneider, T., Hall, M.G., Wheeler-Kingshott, C.A., Alexander, D.C.: Machine learning based compartment models with permeability for white matter microstructure imaging. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. pp. 257–264. Springer (2014)
Tanno, R., Worrall, D.E., Ghosh, A., Kaden, E., Sotiropoulos, S.N., Criminisi, A., Alexander, D.C.: Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution. In: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017. pp. 611–619. Springer (2017)
Cetin Karayumak, S., Kubicki, M., Rathi, Y.: Harmonizing Diffusion MRI Data Across Magnetic Field Strengths. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018. pp. 116–124. Springer (2018)
Hirte, A.U., Platscher, M., Joyce, T., Heit, J.J., Tranvinh, E., Federau, C.: Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models. Magnetic Resonance Imaging 81, 60–66 (2021)
Jha, R.R., Kumar, B.R., Pathak, S.K., Bhavsar, A., Nigam, A.: TrGANet: Transforming 3T to 7T dMRI using Trapezoidal Rule and Graph based Attention Modules. Medical Image Analysis 87, 102806 (2023)
Kazerouni, A., Aghdam, E.K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., Merhof, D.: Diffusion models in medical imaging: A comprehensive survey. Medical Image Analysis p. 102846 (2023)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33, 6840–6851 (2020)
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
Wolleb, J., Bieder, F., Sandkühler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: International Conference on Medical image computing and computer-assisted intervention. pp. 35–45. Springer (2022)
Ozturkler, B., Liu, C., Eckart, B., Mardani, M., Song, J., Kautz, J.: Smrd: Sure-based robust mri reconstruction with diffusion models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 199–209. Springer (2023)
Pinaya, W.H., Tudosiu, P.D., Dafflon, J., Da Costa, P.F., Fernandez, V., Nachev, P., Ourselin, S., Cardoso, M.J.: Brain imaging generation with latent diffusion models. In: MICCAI Workshop on Deep Generative Models. pp. 117–126. Springer (2022)
Xiang, T., Yurt, M., Syed, A.B., Setsompop, K., Chaudhari, A.: DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models. arXiv preprint arXiv:2302.03018 (2023)
Mirzaalian, H., Ning, L., Savadjiev, P., Pasternak, O., Bouix, S., Michailovich, O., Grant, G., Marx, C.E., Morey, R.A., Flashman, L.A., et al.: Inter-site and inter-scanner diffusion MRI data harmonization. NeuroImage 135, 311–323 (2016)
Karayumak, S.C., Bouix, S., Ning, L., James, A., Crow, T., Shenton, M., Kubicki, M., Rathi, Y.: Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. Neuroimage 184, 180–200 (2019)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K., Consortium, W.M.H., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., et al.: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical Q-ball imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 58(3), 497–510 (2007)
De Luca, A., Karayumak, S.C., Leemans, A., Rathi, Y., Swinnen, S., Gooijers, J., Clauwaert, A., Bahr, R., Sandmo, S.B., Sochen, N., et al.: Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH). NeuroImage 259, 119439 (2022)
Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. Advances in neural information processing systems 30 (2017)
Imambi, S., Prakash, K.B., Kanagachidambaresan, G.: PyTorch. Programming with TensorFlow: Solution for Edge Computing Applications pp. 87–104 (2021)
Pinaya, W.H., Graham, M.S., Kerfoot, E., Tudosiu, P.D., Dafflon, J., Fernandez, V., Sanchez, P., Wolleb, J., da Costa, P.F., Patel, A., et al.: Generative AI for medical imaging: extending the monai framework. arXiv preprint arXiv:2307.15208 (2023)
Acknowledgments
This work is in part supported by the National Key R&D Program of China (No. 2023YFE0118600), the National Natural Science Foundation of China (No. 62371107), and the National Institutes of Health (R01MH125860, R01MH119222, R01MH132610, R01NS125781).
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Zhu, X., Zhang, W., Li, Y., O’Donnell, L.J., Zhang, F. (2024). When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15002. Springer, Cham. https://doi.org/10.1007/978-3-031-72069-7_50
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