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Subject motion is one of the major challenges in clinical routine MR imaging. Despite ongoing research, motion correction has remained a complex problem without a universal solution. In advanced quantitative MR techniques, such as MR Fingerprinting, motion does not only affect a single image, like in single-contrast MRI, but disrupts the entire temporal evolution of the magnetization and causes parameter quantification errors due to a mismatch between the acquired and simulated signals. In this work, we present a deep learning-empowered retrospective motion correction for rapid 3D whole-brain multiparametric MRI based on Quantitative Transient-state Imaging (QTI). We propose a patch-based 3D multiscale convolutional neural network (CNN) that learns the residual error, i.e. after initial navigator-based correction, between motion-affected quantitative T1, T2 and proton density maps and their motion-free counterparts. For efficient model training despite limited data availability, we propose a physics-informed simulation to apply continuous motion-patterns to motion-free data. We evaluate the performance of the residual CNN on 1.5T and 3T MRI data of ten healthy volunteers. We analyze the generalizability of the model when applied to real clinical cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that image quality can be significantly improved after correcting for subject motion. This has important implications in clinical setups where large amounts of motion affected data must be discarded.
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Thursday 8th July
H1-3 (long): Image Acquisition and Reconstruction - 16:00 - 16:30 (UTC+2)
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