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A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks
File | Description | Size | Format | |
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1810.02201v1.pdf | Accepted version | 1.81 MB | Adobe PDF | View/Open |
Title: | A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks |
Authors: | Tarroni, G Oktay, O Sinclair, M Bai, W Schuh, A Suzuki, H De Marvao, A O'Regan, D Cook, S Rueckert, D |
Item Type: | Conference Paper |
Abstract: | In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath at the same position, the acquired image stack will be affected by inter-slice respiratory motion and will not correctly represent the cardiac volume, introducing potential errors in the following analyses and visualisations. We propose an approach to automatically correct inter-slice respiratory motion in SA CMR image stacks. Our approach makes use of probabilistic segmentation maps (PSMs) of the left ventricular (LV) cavity generated with decision forests. PSMs are generated for each slice of the SA stack and rigidly registered in-plane to a target PSM. If long axis (LA) images are available, PSMs are generated for them and combined to create the target PSM; if not, the target PSM is produced from the same stack using a 3D model trained from motion-free stacks. The proposed approach was tested on a dataset of SA stacks acquired from 24 healthy subjects (for which anatomical 3D cardiac images were also available as reference) and compared to two techniques which use LA intensity images and LA segmentations as targets, respectively. The results show the accuracy and robustness of the proposed approach in motion compensation. |
Editors: | Frangi, AF Schnabel, JA Davatzikos, C AlberolaLopez, C Fichtinger, G |
Issue Date: | 26-Sep-2018 |
Date of Acceptance: | 1-Sep-2018 |
URI: | http://hdl.handle.net/10044/1/75876 |
DOI: | 10.1007/978-3-030-00928-1_31 |
ISBN: | 978-3-030-00927-4 |
ISSN: | 0302-9743 |
Publisher: | SPRINGER INTERNATIONAL PUBLISHING AG |
Start Page: | 268 |
End Page: | 276 |
Journal / Book Title: | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT I |
Volume: | 11070 |
Copyright Statement: | © Springer Nature Switzerland AG 2018. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-00928-1_31 |
Conference Name: | 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) |
Keywords: | Science & Technology Technology Computer Science, Theory & Methods Imaging Science & Photographic Technology Computer Science FRAMEWORK Science & Technology Technology Computer Science, Theory & Methods Imaging Science & Photographic Technology Computer Science FRAMEWORK cs.CV cs.CV Artificial Intelligence & Image Processing |
Publication Status: | Published |
Start Date: | 2018-09-16 |
Finish Date: | 2018-09-20 |
Conference Place: | Granada, SPAIN |
Online Publication Date: | 2018-09-26 |
Appears in Collections: | Computing Department of Brain Sciences Department of Brain Sciences |