Abstract
Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers through undersampled dMRI. To generate more comprehensive raw dMRI, generative adversarial network based methods are proposed to include b-values and b-vectors as conditions, but they are limited by unstable training and less desirable diversity. The emerging diffusion model (DM) promises to improve generative performance. However, it remains challenging to include essential information in conditioning DM for more relevant generation, i.e., the physical principles of dMRI and white matter tract structures. In this study, we propose a physics-guided diffusion model to generate high-quality dMRI. Our model introduces the physical principles of dMRI in the noise evolution in the diffusion process and introduces a query-based conditional mapping within the diffusion model. In addition, to enhance the anatomical fine details of the generation, we introduce the XTRACT atlas as a prior of white matter tracts by adopting an adapter technique. Our experiment results show that our method outperforms other state-of-the-art methods and has the potential to advance dMRI enhancement.
J. Zhang and R. Yan—Contribute equally in this work.
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References
Bau, D., Zhu, J.Y., Wulff, J., Peebles, W., Strobelt, H., Zhou, B., Torralba, A.: Seeing what a gan cannot generate (2019), https://arxiv.org/abs/1910.11626
Berard, H., Gidel, G., Almahairi, A., Vincent, P., Lacoste-Julien, S.: A closer look at the optimization landscapes of generative adversarial networks. arXiv preprint arXiv:1906.04848 (2019)
Chen, G., Jiang, H., Liu, J., Ma, J., Cui, H., Xia, Y., Yap, P.T.: Hybrid Graph Transformer for Tissue Microstructure Estimation with Undersampled Diffusion MRI Data. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2022. pp. 113–122. Lecture Notes in Computer Science, Springer Nature Switzerland, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_11
Crowson, K., Baumann, S.A., Birch, A., Abraham, T.M., Kaplan, D.Z., Shippole, E.: Scalable high-resolution pixel-space image synthesis with hourglass diffusion transformers (2024), https://arxiv.org/abs/2401.11605
Dhariwal, P., Nichol, A.: Diffusion Models Beat GANs on Image Synthesis (Jun 2021). https://doi.org/10.48550/arXiv.2105.05233, http://arxiv.org/abs/2105.05233, arXiv:2105.05233 [cs, stat]
Essen, D.C.V., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K.: The WU-Minn Human Connectome Project: An overview. NeuroImage 80, 62–79 (2013). https://doi.org/10.1016/j.neuroimage.2013.05.041, https://www.sciencedirect.com/science/article/pii/S1053811913005351
Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L.: Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54(1), 313–327 (2011). https://doi.org/10.1016/j.neuroimage.2010.07.033, https://www.sciencedirect.com/science/article/pii/S1053811910010062
Gibbons, E.K., Hodgson, K.K., Chaudhari, A.S., Richards, L.G., Majersik, J.J., Adluru, G., DiBella, E.V.: Simultaneous noddi and gfa parameter map generation from subsampled q-space imaging using deep learning. Magnetic Resonance in Medicine 81(4), 2399–2411 (2019). https://doi.org/10.1002/mrm.27568, https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27568
Golkov, V., Dosovitskiy, A., Sperl, J.I., Menzel, M.I., Czisch, M., Sämann, P., Brox, T., Cremers, D.: q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. IEEE Transactions on Medical Imaging 35(5), 1344–1351 ( 2016). https://doi.org/10.1109/TMI.2016.2551324, https://ieeexplore.ieee.org/document/7448418, conference Name: IEEE Transactions on Medical Imaging
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. arXiv:2006.11239 (2020)
Jiang, L., Mao, Y., Chen, X., Wang, X., Li, C.: Cola-diff: Conditional latent diffusion model for multi-modal mri synthesis (2023), https://arxiv.org/abs/2303.14081
Karimi, D., Gholipour, A.: Atlas-powered deep learning (adl) – application to diffusion weighted mri (2022), https://arxiv.org/abs/2205.03210
Le Bihan, D., Johansen-Berg, H.: Diffusion MRI at 25: Exploring brain tissue structure and function. NeuroImage 61(2), 324–341 ( 2012). https://doi.org/10.1016/j.neuroimage.2011.11.006, https://www.sciencedirect.com/science/article/pii/S1053811911012857
Li, C., Wei, Y., Chen, X., Schonlieb, C.B.: Brainnetgan: Data augmentation of brain connectivity using generative adversarial network for dementia classification (2021), https://arxiv.org/abs/2103.08494
Li, H., Yang, Y., Chang, M., Chen, S., Feng, H., Xu, Z., Li, Q., Chen, Y.: Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing 479, 47–59 (2022)
Liu, P., Li, C., Schönlieb, C.B.: Ganredl: Medical image enhancement using a generative adversarial network with real-order derivative induced loss functions. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. pp. 110–117. Springer International Publishing, Cham (2019)
Mao, Y., Jiang, L., Chen, X., Li, C.: Disc-diff: Disentangled conditional diffusion model for multi-contrast mri super-resolution (2023), https://arxiv.org/abs/2303.13933
Mou, C., Wang, X., Xie, L., Wu, Y., Zhang, J., Qi, Z., Shan, Y., Qie, X.: T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models (2023), https://arxiv.org/abs/2302.08453
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)
Ren, M., Kim, H., Dey, N., Gerig, G.: Q-space conditioned translation networks for directional synthesis of diffusion weighted images from multi-modal structural mri (2021), https://arxiv.org/abs/2106.13188
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2022), https://arxiv.org/abs/2112.10752
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation (2015), https://arxiv.org/abs/1505.04597
Shazeer, N.: Glu variants improve transformer (2020), https://arxiv.org/abs/2002.05202
Warrington, S., Bryant, K.L., Khrapitchev, A.A., Sallet, J., Charquero-Ballester, M., Douaud, G., Jbabdi, S., Mars, R.B., Sotiropoulos, S.N.: XTRACT - Standardised protocols for automated tractography in the human and macaque brain. NeuroImage 217, 116923 ( 2020). https://doi.org/10.1016/j.neuroimage.2020.116923, https://www.sciencedirect.com/science/article/pii/S1053811920304092
Wei, Y., Chen, X., Zhu, L., Zhang, L., Schönlieb, C.B., Price, S., Li, C.: Multi-modal learning for predicting the genotype of glioma. IEEE Transactions on Medical Imaging 42(11), 3167–3178 (2023)
Wei, Y., Li, C., Price, S.J.: Quantifying structural connectivity in brain tumor patients. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. pp. 519–529. Springer International Publishing, Cham (2021)
Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4), 1000–1016 ( 2012). https://doi.org/10.1016/j.neuroimage.2012.03.072, https://www.sciencedirect.com/science/article/pii/S1053811912003539
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation (2018), https://arxiv.org/abs/1807.10165
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Zhang, J., Yan, R., Perelli, A., Chen, X., Li, C. (2024). Phy-Diff: Physics-Guided Hourglass Diffusion Model for Diffusion MRI Synthesis. 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_33
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