@inbook{34f106e16f864ea9aebe05d52ac6394d,
title = "Optimisation of Left Atrial Feature Tracking Using Retrospective Gated Computed Tomography Images",
abstract = "Retrospective gated cardiac computed tomography (CCT) images can provide high contrast and resolution images of the heart throughout the cardiac cycle. Feature tracking in retrospective CCT images using the temporal sparse free-form deformations (TSFFDs) registration method has previously been optimised for the left ventricle (LV). However, there is limited work on optimising nonrigid registration methods for feature tracking in the left atria (LA). This paper systematically optimises the sparsity weight (SW) and bending energy (BE) as two hyperparameters of the TSFFD method to track the LA endocardium from end-diastole (ED) to end-systole (ES) using 10-frame retrospective gated CCT images. The effect of two different control point (CP) grid resolutions was also investigated. TSFFD optimisation was achieved using the average surface distance (ASD), directed Hausdorff distance (DHD) and Dice score between the registered and ground truth surface meshes and segmentations at ES. For baseline comparison, the configuration optimised for LV feature tracking gave errors across the cohort of 0.826 ± 0.172 mm ASD, 5.882 ± 1.524 mm DHD, and 0.912 ± 0.033 Dice score. Optimising the SW and BE hyperparameters improved the TSFFD performance in tracking LA features, with case specific optimisations giving errors across the cohort of 0.750 ± 0.144 mm ASD, 5.096 ± 1.246 mm DHD, and 0.919 ± 0.029 Dice score. Increasing the CP resolution and optimising the SW and BE further improved tracking performance, with case specific optimisation errors of 0.372 ± 0.051 mm ASD, 2.739 ± 0.843 mm DHD and 0.949 ± 0.018 Dice score across the cohort. We therefore show LA feature tracking using TSFFDs is improved through a chamber-specific optimised configuration.",
keywords = "Atrial fibrosis, Left atrial feature tracking, Retrospective gated computed tomography",
author = "Charles Sillett and Orod Razeghi and Marina Strocchi and Roney, {Caroline H.} and Hugh O{\textquoteright}Brien and Ennis, {Daniel B.} and Ulrike Haberland and Ronak Rajani and Rinaldi, {Christopher A.} and Niederer, {Steven A.}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021 ; Conference date: 21-06-2021 Through 25-06-2021",
year = "2021",
doi = "10.1007/978-3-030-78710-3_8",
language = "English",
isbn = "9783030787097",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "71--83",
editor = "Ennis, {Daniel B.} and Perotti, {Luigi E.} and Wang, {Vicky Y.}",
booktitle = "Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings",
address = "Germany",
}