Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction
This work addresses the challenge of accelerating dynamic MRI for cardiac applications, offering a solution to data scarcity and inter-slice artifacts, but it appears incremental as it builds on existing deep learning methods for MRI reconstruction.
The paper tackled the problem of reconstructing dynamic simultaneous multi-slice MRI images from undersampled data, proposing a deep learning framework that combines data transformation and network design with a physics-guided transfer learning strategy to address data scarcity, resulting in improved reconstruction performance as demonstrated through comparisons with baseline methods.
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.