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Motion compensated reconstruction for myocardial perfusion MRI
© Lingala et al; licensee BioMed Central Ltd. 2012
- Published: 1 February 2012
- Heart Rate Variability
- Temporal Resolution
- Motion Blur
- Saturation Recovery
- Motion Content
Classical acceleration schemes for myocardial perfusion MRI like view sharing, k-t BLAST [Tsao et al. ‘03] are sensitive to motion artifacts which could arise in data with inconsistent gating and/or breathing motion. A natural approach to be robust to this is to estimate the motion and compensate for it during the recovery; to this end we proposed a novel joint motion estimation and reconstruction scheme in [Lingala et al. ‘11]. One goal of this work is to further validate it by recovering such data at considerable accelerations (R). In a 2nd goal, we apply it to recover images acquired with an ungated protocol [DiBella et al. ‘11]. This obtains data at a rapid rate without any gating to provide robustness to beat-beat variability. It also offers a high temporal resolution (~50ms), which ensures maximal information is obtained during the brief first pass. This resolution also means the cardiac motion is captured, akin to cine imaging. Here, we aim to benefit the reconstructions by compensating for this motion. We show its utility by comparisons with standard gridding reconstruction (GR) and sliding window (SW) algorithm.
From fig 2, the images recovered with the proposed scheme were robust to motion blur and streaking artifacts seen respectively with the SW and the GR methods. Apart from its use in the recovery, the motion estimates here could be used for subsequent post-processing. For instance, to improve the temporal resolution and/or the SNR of the ungated images or to implement self-gating.
A novel motion compensation scheme for recovering perfusion images with significant motion content was demonstrated. The experiments with both inconsistent gated, breathing and ungated data show promise; further studies on multiple sets are required to thoroughly evaluate the method.
NSF AWARD CCF-0844812 and in part by NIH R01EB006155.
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