Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI
Reconstruction
- URL: http://arxiv.org/abs/2301.01355v1
- Date: Tue, 3 Jan 2023 21:09:51 GMT
- Title: Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI
Reconstruction
- Authors: Daniel H. Pak and Xiao Chen and Eric Z. Chen and Yikang Liu and
Terrence Chen and Shanhui Sun
- Abstract summary: 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.
- Score: 8.02450593595801
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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