Abstract
Recent dose reduction techniques have made retrospective computed tomography (CT) scans more applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. However, hyperparameters of generic image intensity-based registration techniques, which are used for tracking motion, have not been systematically optimised for this modality. There is limited work on their validation for measuring regional strains from retrospective gated CCT images and open-source software for motion analysis is not widely available. We calculated strain using our open-source platform by applying an image registration warping field to a triangulated mesh of the left ventricular endocardium. We optimised hyperparameters of two registration methods to track the wall motion. Both methods required a single semi-automated segmentation of the left ventricle cavity at end-diastolic phase. The motion was characterised by the circumferential and longitudinal strains, as well as local area change throughout the cardiac cycle from a dataset of 24 patients. The derived motion was validated against manually annotated anatomical landmarks and the calculation of strains were verified using idealised problems. Optimising hyperparameters of registration methods allowed tracking of anatomical measurements with a mean error of 6.63% across frames, landmarks, and patients, comparable to an intra-observer error of 7.98%. Both registration methods differentiated between normal and dyssynchronous contraction patterns based on circumferential strain (p_1=0.0065, p_2=0.0011). To test whether a typical 10 temporal frames sampling of retrospective gated CCT datasets affects measuring cardiac mechanics, we compared motion tracking results from 10 and 20 frames datasets and found a maximum error of 8.51pm 0.8%. Our findings show that intensity-based registration techniques with optimal hyperparameters are able to accurately measure regional strains from CCT in a very short amount of time. Furthermore, sufficient sensitivity can be achieved to identify heart failure patients and left ventricle mechanics can be quantified with 10 reconstructed temporal frames. Our open-source platform will support increased use of CCT for quantifying cardiac mechanics.
Highlights
Recent dose reduction techniques have made retrospective computed tomography (CT) scans more applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible
Bending energy regularisation 0 is applied between neighbouring control points within each free-form deformation (FFD) level to encourage a grouped sparse solution
Our contributions include optimisation of two registration methods for tracking regional motion using manually annotated datasets, and deriving validated circumferential and longitudinal strains using large strain theory in addition to area, which has been the main measure examined in the literature[8,9,12,28]
Summary
Recent dose reduction techniques have made retrospective computed tomography (CT) scans more applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. In order to establish the correlation between regional function estimates from CMR and CCT, Pourmorteza et al.[8,9] tracked the motion of the left ventricular endocardium to measure regional deformation They calculated local area change and labelled it “SQUEEZ”. Their method relied on a non-rigid point registration algorithm termed coherent point drift (CPD) for warping surfaces representing end-diastole to end-systole[10] They did not attempt to measure circumferential or longitudinal strains, nor was their tracking validated against manual annotations. Vigneault et al.[12] adapted this approach, used the simultaneous subdivision surface registration (SiSSR) method to measure SQUEEZ in 13 canine hearts, and compared the results against the CPD technique Their method was not applied to clinical datasets and their selected registration method requires binary segmentation of the blood pool and mesh generation from every phase of the cardiac cycle. They used thresholding and morphological techniques to extract these meshes, which can be potentially a subjective and time consuming task for cardiologists, limiting the clinical translation of the technique
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