Cross-organ all-in-one parallel compressed sensing magnetic resonance imaging

TL;DR

CAPNet is a unified deep learning framework for multi-organ MRI reconstruction that leverages structural prompts and artifact features, achieving state-of-the-art results with a single model across different organs.

Contribution

The paper introduces CAPNet, a novel all-in-one deep unfolding network that generalizes MRI reconstruction across multiple organs using structural prompts and artifact feature integration.

Findings

CAPNet outperforms existing methods on cross-organ MRI datasets.

A single model effectively reconstructs images for multiple organs.

Incorporating artifact features improves reconstruction quality.

Abstract

Recent advances in deep learning-based parallel compressed sensing magnetic resonance imaging (p-CSMRI) have significantly improved reconstruction quality. However, current p-CSMRI methods often require training separate deep neural network (DNN) for each organ due to anatomical variations, creating a barrier to developing generalized medical image reconstruction systems. To address this, we propose CAPNet (cross-organ all-in-one deep unfolding p-CSMRI network), a unified framework that implements a p-CSMRI iterative algorithm via three specialized modules: auxiliary variable module, prior module, and data consistency module. Recognizing that p-CSMRI systems often employ varying sampling ratios for different organs, resulting in organ-specific artifact patterns, we introduce an artifact generator, which extracts and integrates artifact features into the data consistency module to enhance the discriminative ability of the overall network. For the prior module, we design an organ structure-prompt generation submodule that leverages structural features extracted from the segment anything model (SAM) to create cross-organ prompts. These prompts are strategically incorporated into the prior module through an organ structure-aware Mamba submodule. Comprehensive evaluations on a cross-organ dataset confirm that CAPNet achieves state-of-the-art reconstruction performance across multiple anatomical structures using a single unified model. Our code will be published at https://github.com/shibaoshun/CAPNet.