# Deep Parallel MRI Reconstruction Network Without Coil Sensitivities

arXiv: https://arxiv.org/abs/2008.01410 · HTML: https://mlsanity.com/paper/2008.01410/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset](https://mlsanity.com/paper/2110.00715/) (2021)
- [An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities](https://mlsanity.com/paper/2109.09738/) (2021)
- [Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning](https://mlsanity.com/paper/2403.19966/) (2024)
- [Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction](https://mlsanity.com/paper/2104.12939/) (2021)
- [A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning](https://mlsanity.com/paper/2406.02626/) (2024)
- [Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors](https://mlsanity.com/paper/2505.00044/) (2025)
- [Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization](https://mlsanity.com/paper/2505.11518/) (2025)

## Abstract (arXiv)

We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data. The proposed network learns to adaptively combine the multi-coil images from incomplete pMRI data into a single image with homogeneous contrast, which is then passed to a nonlinear encoder to efficiently extract sparse features of the image. Unlike most of existing deep image reconstruction networks, our network does not require knowledge of sensitivity maps, which can be difficult to estimate accurately, and have been a major bottleneck of image reconstruction in real-world pMRI applications. The experimental results demonstrate the promising performance of our method on a variety of pMRI imaging data sets.