Advanced motion correction and image reconstruction for cardiac magnetic resonance (2013)

Ab stractCardiovascular magnetic resonance is a noninvasive imaging tool that provides highresolution anatomical and functional images without exposure to ionizing radiation.The unique ability to manipulate contrast and encode functional information into the imaging signal allows for a broad range of clinical application to assess morphological and hemodynamic disorders.With coronary artery disease being the leading cause of death in Western societies, the diagnosis of stenosis in coronary arteries and their hemodynamic relevance play an important role in therapeutic decisions.Long scan duration and artifacts from physiological motion, however, are still major obstacles for the transition into clinical practice.In the present thesis, new technical developments are presented for scan acceleration and artifact removal in whole-heart coronary magnetic resonance angiography, threedimensional first-pass myocardial perfusion and cine imaging.In the first study, a retrospective motion correction scheme is proposed for whole-heart coronary scans with interleaved acquisition of motion information.A 3D nonrigid motion model is derived from 2D motion scouts acquired interleaved with the coronary scan without imposing a scan time penalty.Image reconstruction and motion correction were performed simultaneously by inversion of a forward model describing the transformation from the motion-free image to corrupted k-space data.In a study with ten healthy volunteers, the retrospective nonrigid correction scheme allowed for respiratory gate-free acquisition and hence a reduction in scan time by a factor of two compared to the conventional respiratory gating and acquisition at end-expiration.Three-dimensional first-pass perfusion imaging is often impaired by respiratory motion artifacts.An iterative k-t principal component analysis reconstruction with nonrigid correction of frame-to-frame respiratory motion is proposed in the second part.Motion information was extracted using shape-constrained image registration of the composite of 8 Abstract training and k-t undersampled data.The validity of the approach was tested for 10-fold k-t undersampling using simulation data and in-vivo measurements.The third study presents a novel reconstruction technique employing nonlinear transform domains for image reconstruction of undersampled k-space data.The nonlinear mapping from the image domain into a high-dimensional feature space is performed implicitly by kernel principal component analysis.Thereby, corrupted data are projected onto the main principal components in the dot product feature space.The principal components are derived from the surrounding of image blocks.Image reconstruction is performed iteratively with interleaved steps for nonlinear projection and gradient updates to ensure consistency with acquired k-space data.The algorithm was evaluated using undersampled simulation data and in vivo two-dimensional cine and three-dimensional whole-heart coronary data and allows for higher acceleration factors compared to standard compressed sensing reconstruction.In conclusion, the presented methodological improvements described in this thesis allow for scan time efficient compensation of respiratory motion artifacts in relevant cardiovascular imaging applications including 3D coronary and first-pass myocardial perfusion imaging.Dedicated image reconstruction algorithms have been proposed to push further scan acceleration by exploiting data correlations in both linear and nonlinear transform domain.