K‐ CC ‐ MoCo : A Fast k ‐Space‐Based Respiratory Motion Correction for Highly Accelerated First‐Pass Perfusion Cardiovascular MR

ABSTRACT Purpose First‐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. Methods We 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. Results The 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. Conclusion K‐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.