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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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Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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