RT info:eu-repo/semantics/article
T1 K‐CC‐MoCo: A Fast k‐Space‐Based Respiratory Motion Correction for Highly Accelerated First‐Pass Perfusion Cardiovascular MR
A1 Moya Saez, Elisa
A1 Menchon Lara, Rosa María
A1 Sánchez González, Javier
A1 Carvalho, Catarina N.
A1 Gaspar, Andreia S.
A1 Real, Carlos
A1 Galán Arriola, Carlos
A1 Nunes, Rita G.
A1 Ibáñez, Borja
A1 Correia, Teresa M.
A1 Alberola López, Carlos
K1 Resonancia Magnética Cardiovascular
K1 Espacio k
K1 Procesado de imágenes
K1 Corrección del movimiento
K1 Perfusión miocárdica de primer paso
K1 Movimiento respiratorio
K1 Registro rígido
K1 3307 Tecnología Electrónica
K1 1203 Ciencia de Los Ordenadores
AB PurposeFirst-pass perfusion cardiovascular MR (FPP-CMR) enables the non-invasive diagnosis of microcirculation and coronary artery disease. In free-breathing FPP-CMR, motion correction is usually performed in the image domain, requiring an initial reconstruction. This fact hinders its use in model-based and deep learning reconstructions, which present remarkable performance in obtaining high-quality images from highly accelerated acquisitions. We address this challenge by estimating and correcting respiratory motion in free-breathing FPP-CMR directly in k-space.MethodsWe propose K-CC-MoCo, an inter-frame rigid motion correction approach formulated exclusively in k-space that handles dynamic contrast through a specifically targeted design of the normalized cross-correlation (CC) objective function to deal with the dynamic contrast. In addition, an ROI-based coil-compression approach was employed to focus the optimization on the heart region. The proposed method was compared to state-of-the-art image-based registration using a digital phantom and real free-breathing acquisitions with different accelerations.ResultsThe proposed k-space-based method is approximately 2× faster and can correct respiratory motion even at high acceleration factors (up to 50×), where the image-based method fails due to severe undersampling artifacts. Notably, after K-CC-MoCo, the time-averaged images are visibly less blurred. Quantitative metrics (SSIM, etc.) support this conclusion.ConclusionK-CC-MoCo outperforms image-based correction in free-breathing FPP-CMR acquisitions accelerated up to 50×. Respiratory motion is estimated and corrected in k-space, enabling its use for model-based and/or deep learning reconstructions from highly accelerated scans.
PB Wiley
SN 0740-3194
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/82754
UL https://uvadoc.uva.es/handle/10324/82754
LA eng
NO Magnetic Resonance in Medicine, 2026 (in press)
NO Producción Científica
DS UVaDOC
RD 13-feb-2026