title: Motion-compensated MR valve imaging with COMB tag tracking and super-resolution enhancement.
abstract: Understanding the morphology and function of heart valves is important to the study of underlying causes of heart failure. Existing techniques such as those based on echocardiography are limited by the relatively low signal-to-noise ratio (SNR), attenuation artefacts, and restricted access. The alternative of cardiovascular MR imaging offers versatility and accuracy in 3D localisation, but is hampered by large movements of the valves throughout the cardiac cycle. This paper presents a motion-compensated adaptive imaging approach for MR valve imaging. To illustrate its clinical potential, 3D motion of the aortic valve plane is first captured through a single breath-hold COMB tag pre-scan and then tracked in real-time with an automatic method based on multi-resolution image registration. Motion-compensated coverage of the aortic valve is then acquired prospectively, thus allowing its clear 3D reconstruction and visualisation. To provide isotropic voxel coverage of the imaging volume, retrospective projection onto convex sets (POCS) super-resolution enhancement is applied to the slice-select direction. In vivo results demonstrate the effectiveness of the proposed motion-compensation and super-resolution schemes for depicting the structure of the valve leaflets throughout the cardiac cycle. The proposed method fundamentally changes the way MR imaging is performed by transforming it from a spatially to materially localised imaging method. This also has important implications for quantifying blood flow and myocardial perfusion, as well as tracking anatomy and function of the heart.
authors.affiliation: Institute of Biomedical Engineering, Imperial College London SW7 2AZ, UK. a.w.dowsey@imperial.ac.uk
authors.forename: Andrew W
authors.initials: AW
authors.lastname: Dowsey
authors.forename: Jennifer
authors.initials: J
authors.lastname: Keegan
authors.forename: Mirna
authors.initials: M
authors.lastname: Lerotic
authors.forename: Simon
authors.initials: S
authors.lastname: Thom
authors.forename: David
authors.initials: D
authors.lastname: Firmin
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003971:Diastole; D006351:Heart Valves / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013599:Systole