# DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging

arXiv: https://arxiv.org/abs/2405.03159 · HTML: https://mlsanity.com/paper/2405.03159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 100 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models](https://mlsanity.com/paper/2208.06677/) (2022)
- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [Online Learning Rate Adaptation with Hypergradient Descent](https://mlsanity.com/paper/1703.04782/) (2017)
- [Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm](https://mlsanity.com/paper/1804.03728/) (2018)
- [Learning Regularization Parameters of Inverse Problems via Deep Neural Networks](https://mlsanity.com/paper/2104.06594/) (2021)
- [SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning](https://mlsanity.com/paper/2002.01031/) (2020)
- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)
- [DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues](https://mlsanity.com/paper/2102.02463/) (2021)
- [Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI](https://mlsanity.com/paper/1609.09144/) (2016)
- [Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes](https://mlsanity.com/paper/1706.06682/) (2017)
- [High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models](https://mlsanity.com/paper/2306.08630/) (2023)
- [SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces](https://mlsanity.com/paper/2009.06600/) (2020)

## Built on by (most cited first, 1 of 1)

- [3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging](https://mlsanity.com/paper/2502.17933/) (2025)

## Abstract (arXiv)

Deep learning has emerged as a promising approach for learning the nonlinear mapping between diffusion-weighted MR images and tissue parameters, which enables automatic and deep understanding of the brain microstructures. However, the efficiency and accuracy in estimating multiple microstructural parameters derived from multiple diffusion models are still limited since previous studies tend to estimate parameter maps from distinct models with isolated signal modeling and dense sampling. This paper proposes DeepMpMRI, an efficient framework for fast and high-fidelity multiple microstructural parameter estimation from multiple models using highly sparse sampled q-space data. DeepMpMRI is equipped with a newly designed tensor-decomposition-based regularizer to effectively capture fine details by exploiting the high-dimensional correlation across microstructural parameters. In addition, we introduce a Nesterov-based adaptive learning algorithm that optimizes the regularization parameter dynamically to enhance the performance. DeepMpMRI is an extendable framework capable of incorporating flexible network architecture. Experimental results on the HCP dataset and the Alzheimer's disease dataset both demonstrate the superiority of our approach over 5 state-of-the-art methods in simultaneously estimating multi-model microstructural parameter maps for DKI and NODDI model with fine-grained details both quantitatively and qualitatively, achieving 4.5 - 15 × acceleration compared to the dense sampling of a total of 270 diffusion gradients.