ArticleIEEE transactions on medical imaging2020
MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 52 citations in OpenAlex.
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- Diffusion-weighted magnetic resonance imaging of the pancreas: A narrative review.World journal of radiology · 2025Review
- Navigator-free multi-shot diffusion MRI via non-local low-rank reconstruction.Magnetic resonance in medicine · 2025Article
- Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace.Medical image analysis · 2025Article
- 3D distortion-free, reduced FOV diffusion-prepared gradient echo at 3 T.Magnetic resonance in medicine · 2025Article
- DIMOND: DIffusion Model OptimizatioN with Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction.NeuroImage · 2023Article
- 3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolutionMagnetic resonance in medicine · 2023Article
- ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms.IEEE transactions on medical imaging · 2023Article
- Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions.Medical image analysis · 2023Article
- Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver.Magnetic resonance in medicine · 2023Article
- Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping.Magnetic resonance in medicine · 2023Article
- Wave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction.Bioengineering (Basel, Switzerland) · 2022Article
- Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients.Magnetic resonance in medicine · 2021Article
- Distortion-free, high-isotropic-resolution diffusion MRI with gSlider BUDA-EPI and multicoil dynamic BMagnetic resonance in medicine · 2021Article
- A multi-inversion multi-echo spin and gradient echo echo planar imaging sequence with low image distortion for rapid quantitative parameter mapping and synthetic image contrasts.Magnetic resonance in medicine · 2021Article
- Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2021Article
- RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors.Magnetic resonance in medicine · 2021Article
- Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR).IEEE transactions on medical imaging · 2020Article
- J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction.IEEE journal of selected topics in signal processing · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MR images. The proposed algorithm is a generalization of the existing MUSSELS algorithm with similar performance but 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 annihilation relations between shots. Due to the high computational complexity of the self-learned filter bank, we propose replacing 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 annihilation 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.
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