Abstract
We present a retrospective joint motion correction and reconstruction scheme for magnetic resonance imaging to reduce the imprint of subject motion on the reconstructed images. In multi-contrast imaging, reconstructions pertaining to distinct acquisition sequences (e.g., T1 or T2 weighted images) might not be equally affected by motion, due to the sequential nature of the data acquisition process or the specific sequence design. To avoid repeating the corrupted scan, we can leverage the uncorrupted reconstructions to post-process the contrasts that are most severely affected by motion, by assuming a shared underlying anatomy. Only rigid motion is considered here, but no further assumptions are required. Classical motion correction schemes are combined with weighted total-variation regularization, whose weight is defined by the structure of the well-resolved contrasts. We will particularly focus on brain imaging with conventional Cartesian sampling. We envision a practical workflow that can easily fit into the existing clinical practice without the need for changing the MRI acquisition protocols.
Original language | English |
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Pages (from-to) | 490-504 |
Number of pages | 15 |
Journal | IEEE Transactions on Computational Imaging |
Volume | 8 |
DOIs | |
Publication status | Published - 20 Jun 2022 |
Bibliographical note
Funding Information:This work was supported in part by the Netherlands Organization for Health Research and Development (ZonMW) through Project Reducing re-scans in clinical MRI exams under Project 104022007, of the research program IMDI, Technologie voor bemensbare zorg:Doorbraakprojecten, and in part by Philips Medical Systems Netherlands BV.
Publisher Copyright:
© 2015 IEEE.
Funding
This work was supported in part by the Netherlands Organization for Health Research and Development (ZonMW) through Project Reducing re-scans in clinical MRI exams under Project 104022007, of the research program IMDI, Technologie voor bemensbare zorg:Doorbraakprojecten, and in part by Philips Medical Systems Netherlands BV.
Keywords
- compressed sensing
- Magnetic resonance imaging
- motion correction
- structure-guided regularization