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Abstract #0029

K-CC-MoCo: A fast respiratory motion correction in coil-compressed K-space for highly accelerated first-pass perfusion cardiac MRI

Elisa Moya-Sáez1, Rosa-María Menchón-Lara1, Javier Sanchez-Gonzalez2, Rita G. Nunes3, Carlos Real4, Carlos Galán-Arriola4, Borja Ibanez4, Teresa M. Correia5,6, and Carlos Alberola-López1
1University of Valladolid, Valladolid, Spain, 2Philips Healthcare Iberia, Madrid, Spain, 3Institute for Systems and Robotics – Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, 4Spanish National Centre for Cardiovascular Research, Madrid, Spain, 5Center of Marine Sciences-CCMAR, Faro, Portugal, 6School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Synopsis

Keywords: Motion Correction, Perfusion

Motivation: Motion correction in free-breathing first-pass perfusion cardiac MRI (FPP-CMR) is usually done in the image domain, requiring initial reconstruction. This hinders its use in model-based and deep-learning reconstructions from highly accelerated acquisitions.

Goal(s): To estimate and correct respiratory motion in free-breathing FPP-CMR directly in k-space.

Approach: We propose an inter-frame rigid motion correction formulated in k-space with the normalized-cross-correlation objective function. An ROI-based coil-compression approach was employed to focus the optimization on the heart. The method was tested using a digital phantom and real free-breathing acquisitions with different accelerations.

Results: The proposed approach outperforms image-based correction in acquisitions with accelerations up to 50x.

Impact: The k-space-based motion correction outperforms image-based correction in free-breathing FPP-CMR acquisitions accelerated up to 50x. This method can estimate/correct respiratory motion in k-space without an initial reconstruction, thereby enabling its use for model-based and/or deep-learning reconstructions from highly accelerated scans.

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Keywords