# MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI

arXiv: https://arxiv.org/abs/1812.08115 · HTML: https://mlsanity.com/paper/1812.08115/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPI](https://mlsanity.com/paper/2303.10436/) (2023)
- [Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond](https://mlsanity.com/paper/2103.14528/) (2021)
- [Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction](https://mlsanity.com/paper/2308.05103/) (2023)
- [Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction](https://mlsanity.com/paper/2202.02814/) (2022)
- [Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence](https://mlsanity.com/paper/2210.11388/) (2022)
- [Motion-Informed Deep Learning for Brain MR Image Reconstruction Framework](https://mlsanity.com/paper/2405.17756/) (2024)
- [Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning](https://mlsanity.com/paper/2510.15400/) (2025)
- [FINDER: Zero-Shot Field-Integrated Network for Distortion-free EPI Reconstruction in Diffusion MRI](https://mlsanity.com/paper/2603.26117/) (2026)

## Abstract (arXiv)

We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. The proposed algorithm is a generalization of existing MUSSELS algorithm with similar performance but with significantly reduced computational complexity. In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency. The multichannel filter bank projects the data to the signal subspace thus exploiting the phase relations between shots. Due to the high computational complexity of self-learned filter bank, we propose to replace it with a convolutional neural network (CNN) whose parameters are learned from exemplary data. The proposed CNN is a hybrid model involving a multichannel CNN in the k-space and another CNN in the image space. The k-space CNN exploits the phase relations between the shot images, while the image domain network is used to project the data to an image manifold. The experiments show that the proposed scheme can yield reconstructions that are comparable to state of the art methods while offering several orders of magnitude reduction in run-time.