Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction

TL;DR

This paper introduces a deep learning framework for dynamic SMS MRI reconstruction that effectively handles inter-slice artifacts and data scarcity, improving image quality in cardiac imaging.

Contribution

It presents a novel combination of data transformation, network design, and physics-guided transfer learning tailored for dynamic SMS MRI reconstruction.

Findings

Outperforms baseline methods in reconstruction quality.

Effectively mitigates inter-slice artifacts.

Addresses data scarcity with transfer learning.

Abstract

Dynamic Magnetic Resonance Imaging (dMRI) is widely used to assess various cardiac conditions such as cardiac motion and blood flow. To accelerate MR acquisition, techniques such as undersampling and Simultaneous Multi-Slice (SMS) are often used. Special reconstruction algorithms are needed to reconstruct multiple SMS image slices from the entangled information. Deep learning (DL)-based methods have shown promising results for single-slice MR reconstruction, but the addition of SMS acceleration raises unique challenges due to the composite k-space signals and the resulting images with strong inter-slice artifacts. Furthermore, many dMRI applications lack sufficient data for training reconstruction neural networks. In this study, we propose a novel DL-based framework for dynamic SMS reconstruction. Our main contributions are 1) a combination of data transformation steps and network design that effectively leverages the unique characteristics of undersampled dynamic SMS data, and 2) an MR physics-guided transfer learning strategy that addresses the data scarcity issue. Thorough comparisons with multiple baseline methods illustrate the strengths of our proposed methods.