Keywords: Motion Corruption, Fast MRI, Undersampling, AI-Based Reconstruction
TL;DR: We investigate the effect of motion on MRI reconstruction, and demonstrate that finetuning by training with synthetic motion corrupted data can enhance the motion robustness of existing deep learning based reconstruction approaches.
Abstract: MRI can be accelerated via (AI-based) reconstruction by undersampling k-space. Current methods typically ignore intra-scan motion, although even a few millimeters of motion can introduce severe blurring and ghosting artifacts that necessitate reacquisition. In this short paper we investigate the effects of rigid-body motion on AI-based reconstructions. Leveraging the Bloch equations we simulate motion corrupted MRI acquisitions with a linear interleaved scanning protocol including spin history effects, and investigate i) the effect on reconstruction quality, and ii) if this corruption can be mitigated by introducing motion-corrupted data during training. We observe an improvement from 0.819 to 0.867 in terms of SSIM when motion-corrupted brain data is included during training, demonstrating that training with motion-corrupted data can partially compensate for motion corruption. Inclusion of spin-history effects did not influence the results.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Image Acquisition and Reconstruction
Secondary Subject Area: Image Synthesis
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