Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

Citation

BibTex format

@inproceedings{Tarroni:2018:10.1007/978-3-030-00928-1_31,
author = {Tarroni, G and Oktay, O and Sinclair, M and Bai, W and Schuh, A and Suzuki, H and de, Marvao A and O'Regan, D and Cook, S and Rueckert, D},
doi = {10.1007/978-3-030-00928-1_31},
pages = {268--276},
publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
title = {A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks},
url = {http://dx.doi.org/10.1007/978-3-030-00928-1_31},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Tarroni,G
AU  - Oktay,O
AU  - Sinclair,M
AU  - Bai,W
AU  - Schuh,A
AU  - Suzuki,H
AU  - de,Marvao A
AU  - O'Regan,D
AU  - Cook,S
AU  - Rueckert,D
DO  - 10.1007/978-3-030-00928-1_31
EP  - 276
PB  - SPRINGER INTERNATIONAL PUBLISHING AG
PY  - 2018///
SN  - 0302-9743
SP  - 268
TI  - A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks
UR  - http://dx.doi.org/10.1007/978-3-030-00928-1_31
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000477770600031&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-00928-1_31
UR  - http://hdl.handle.net/10044/1/75876
ER  -
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