Alexander, D. C., Dyrby, T. B., Nilsson, M. & Zhang, H. Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. 32, 1–26 (2019).
Lampinen, B. et al. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. NeuroImage 282, 120338 (2023).
Jelescu, I. O., Palombo, M., Bagnato, F. & Schilling, K. G. Challenges for biophysical modeling of microstructure. J. Neurosci. Methods 344, 108861 (2020).
Novikov, D. S., Fieremans, E., Jespersen, S. N. & Kiselev, V. G. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. NMR Biomed. 32, 1–53 (2019).
Westin, C.-F. et al. Measurement tensors in diffusion mri: Generalizing the concept of diffusion encoding. In Golland, P., Hata, N., Barillot, C., Hornegger, J. & Howe, R. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, 209–216 (Springer International Publishing, Cham, 2014).
Alotaibi, A. et al. Investigating microstructural changes in white matter in multiple sclerosis: A systematic review and meta-analysis of neurite orientation dispersion and density imaging. Brain Sci. 11, 1151 (2021).
Parker, T. D. et al. Cortical microstructure in young-onset alzheimer’s disease using neurite orientation dispersion and density imaging. Hum. Brain Mapp. 39, 3005–3017 (2018).
Kim, D.-H., Laun, D. H., Kamesh, N. B. et al. Diffusion microstructure imaging of the substantia nigra in Parkinson’s disease using mean apparent propagator MRI. NeuroImage Clin. 12, 451–459 (2016).
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, 1000–1016 (2012).
Tournier, J.-D., Calamante, F. & Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472 (2007).
Jelescu, I. O., Veraart, J., Fieremans, E. & Novikov, D. S. Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue. NMR Biomed. 29, 33–47 (2016).
Tournier, J. D., Calamante, F. & Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472 (2007).
Jeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A. & Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014).
Novikov, D. S., Veraart, J., Jelescu, I. O. & Fieremans, E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. NeuroImage 174, 518–538 (2018).
Coelho, S. et al. Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems. NeuroImage257 (2022).
Reisert, M., Kellner, E., Dhital, B., Hennig, J. & Kiselev, V. G. Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach. NeuroImage 147, 964–975 (2017).
Kaden, E., Kelm, N. D., Carson, R. P., Does, M. D. & Alexander, D. C. Multi-compartment microscopic diffusion imaging. NeuroImage 139, 346–359 (2016).
de Almeida Martins, J. P. et al. Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter. NeuroImage 244 (2021).
Diao, Y. & Jelescu, I. Parameter estimation for wmti-watson model of white matter using encoder-decoder recurrent neural network. Magn. Reson. Med. 89, 1193–1206 (2023).
Liao, Y. et al. Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI. Imaging Neurosci. 2, 1–17 (2024).
Gyori, N. G., Palombo, M., Clark, C. A., Zhang, H. & Alexander, D. C. Training data distribution significantly impacts the estimation of tissue microstructure with machine learning. Magn. Reson. Med. 87, 932–947 (2022).
Hendriks, T., Vilanova, A. & Chamberland, M. Neural spherical harmonics for structurally coherent continuous representation of diffusion MRI signal. In International Workshop on Computational Diffusion MRI, 1–12 (Springer, 2023).
Consagra, W., Ning, L. & Rathi, Y. Neural orientation distribution fields for estimation and uncertainty quantification in diffusion MRI. Med. Image Anal. 93, 103105 (2024).
Hendriks, T., Vilanova, A. & Chamberland, M. Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of diffusion MRI. Imaging Neuroscience 3, imag_a_00501 (2025).
Girard, G. et al. Ax t ract: Toward microstructure-informed tractography. Hum. Brain Mapp. 38, 5485–5500 (2017).
Reisert, M., Kiselev, V. G., Dihtal, B., Kellner, E. & Novikov, D. S. Mesoft: Unifying diffusion modelling and fiber tracking. In Golland, P., Hata, N., Barillot, C., Hornegger, J. & Howe, R. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, 201–208 (Springer International Publishing, Cham, 2014).
París, G. et al. Thermal noise lowers the accuracy of rotationally invariant harmonics of diffusion MRI data and their robustness to experimental variations. Magn. Reson. Med. https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.70035 (2025).
Bammer, R. et al. Analysis and generalized correction of the effect of spatial gradient field distortions in diffusion-weighted imaging. Magn. Reson. Med. 50, 560–569 (2003).
Daducci, A. et al. Accelerated microstructure imaging via convex optimization (amico) from diffusion MRI data. NeuroImage 105, 32–44 (2015).
Harms, R., Fritz, F., Tobisch, A., Goebel, R. & Roebroeck, A. Robust and fast nonlinear optimization of diffusion MRI microstructure models. NeuroImage 155, 82–96 (2017).
Mesri, H. Y., David, S., Viergever, M. A. & Leemans, A. The adverse effect of gradient nonlinearities on diffusion MRI: From voxels to group studies. NeuroImage 205, 116127 (2020).
Mohammadi, S. et al. The effect of local perturbation fields on human DTI: Characterisation, measurement and correction. NeuroImage 60, 562–570 (2012).
Morez, J., Sijbers, J., Vanhevel, F. & Jeurissen, B. Constrained spherical deconvolution of nonspherically sampled diffusion MRI data. Hum. Brain Mapp. 42, 521–538 (2021).
Jones, D. K. et al. Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI (2018).
Coelho, S. et al. What if each voxel were measured with a different diffusion protocol? https://arxiv.org/abs/2506.22650 2506.22650 (2025).
Eichner, C. et al. Real diffusion-weighted MRI enabling true signal averaging and increased diffusion contrast. NeuroImage 122, 373–384 (2015).
Koay, C. G., Özarslan, E. & Pierpaoli, C. Probabilistic identification and estimation of noise (PIESNO): A self-consistent approach and its applications in MRI. J. Magn. Reson. 199, 94–103 (2009).
Álvaro, P. et al. Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning. Med. Image Anal. 94, 103134 (2024).
Parker, C. et al. Rician likelihood loss for quantitative MRI with self-supervised deep learning. NMR Biomed. 38, e70136 (2025).
Kunz, N., da Silva, A. R. & Jelescu, I. O. Intra- and extra-axonal axial diffusivities in the white matter: Which one is faster? NeuroImage 181, 314–322 (2018).
Dhital, B., Reisert, M., Kellner, E. & Kiselev, V. G. Intra-axonal diffusivity in brain white matter. NeuroImage 189, 543–550 (2019).
Coelho, S., Pozo, J. M., Jespersen, S. N., Jones, D. K. & Frangi, A. F. Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding. Magn. Reson. Med. 82, 395–410 (2019).
Consagra, W., Ning, L. & Rathi, Y. A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI. Med. Image Anal. 103537 (2025).
Jallais, M. & Palombo, M. Introducing μguide for quantitative imaging via generalized uncertainty-driven inference using deep learning. eLife 13, RP101069 (2024).
Tax, C. M. et al. Measuring compartmental t2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-t2 correlation MRI. Neuroimage 236, 117967 (2021).
Veraart, J., Novikov, D. S. & Fieremans, E. Te dependent diffusion imaging (teddi) distinguishes between compartmental t2 relaxation times. NeuroImage 182, 360–369 (2018).
Palombo, M. et al. Sandi: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. NeuroImage 215, 116835 (2020).
Daducci, A., Dal Palù, A., Lemkaddem, A. & Thiran, J.-P. Commit: Convex optimization modeling for microstructure informed tractography. IEEE Trans. Med. Imaging 34, 246–257 (2015).
Li, Z. et al. DIMOND: DIffusion Model OptimizatioN with Deep Learning. Adv. Sci. (2024).
Tancik, M. et al. Learned initializations for optimizing coordinate-based neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2846–2855 (2021).
Wang, Y. et al. Scarf: Scalable continual learning framework for memory-efficient multiple neural radiance fields. In Computer graphics forum, vol. 43, e15255 (Wiley Online Library, 2024).
Dwedari, M. M. et al. Estimating neural orientation distribution fields on high-resolution diffusion MRI scans. In Linguraru, M. G. et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, 307–317 (Springer Nature Switzerland, Cham,2024).
Spears, T. & Fletcher, P. T. Learning spatially-continuous fiber orientation functions. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5 (IEEE, 2024).
Jespersen, S. N., Kroenke, C. D., Østergaard, L., Ackerman, J. J. & Yablonskiy, D. A. Modeling dendrite density from magnetic resonance diffusion measurements. NeuroImage 34, 1473–1486 (2007).
Kroenke, C. D., Ackerman, J. J. & Yablonskiy, D. A. On the nature of the Na+ diffusion attenuated MR signal in the central nervous system. Magn. Reson. Med. 52, 1052–1059 (2004).
Fieremans, E., Jensen, J. H. & Helpern, J. A. White matter characterization with diffusional kurtosis imaging. NeuroImage 58, 177–188 (2011).
Basser, P. J., Mattiello, J. & LeBihan, D. Mr diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267 (1994).
Lampinen, B. et al. Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding. Magn. Reson. Med. 84, 1605–1623 (2020).
Tancik, M. et al. Fourier features let networks learn high-frequency functions in low-dimensional domains. Adv. Neural Inf. Process. Syst. 33, 7537–7547 (2020).
Dugas, C., Bengio, Y., Bélisle, F., Nadeau, C. & Garcia, R. Incorporating second-order functional knowledge for better option pricing. Advances in Neural Inform. Process. Syst. 13 (2000).
Tournier, J.-D. et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202, 116137 (2019).
Gómez, P., Toftevaag, H. H. & Meoni, G. torchquad: Numerical integration in arbitrary dimensions with PyTorch. J. Open Source Softw. 6, 3439 (2021).
Veraart, J. et al. Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406 (2016).
Tian, Q. et al. Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mt/m gradients. figshare. Collection https://doi.org/10.6084/m9.figshare.c.5315474.v1 (2022).
Tian, Q. et al. Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients. Sci. Data 9 (2022).
Garyfallidis, E. et al. Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8 (2014).
Fischl, B. Freesurfer. NeuroImage 62, 774–781 (2012).
Sjölund, J. et al. Constrained optimization of gradient waveforms for generalized diffusion encoding. J. Magn. Reson. 261, 157–168 (2015).
Kellner, E., Dhital, B., Kiselev, V. G. & Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 76, 1574–1581 (2016).
Vos, S. B. et al. The importance of correcting for signal drift in diffusion MRI. Magn. Reson. Med. 77, 285–299 (2017).
Nilsson, M., Szczepankiewicz, F., van Westen, D. & Hansson, O. Extrapolation-based references improve motion and eddy-current correction of high b-value DWI data: Application in Parkinson’s disease dementia. PLOS ONE 10, 1–22 (2015).
Andersson, J. L., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20, 870–888 (2003).
Hendriks, T. et al. Simulated DWI files for experiments of: Implicit neural representations for accurate estimation of the standard model of white matter https://doi.org/10.5281/zenodo.17092773 (2025).