Paper Info
Title
A Comprehensive Approach for Learning-Based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks.
Abstract
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breathholds. 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.
Year
DOI
Venue
2018
10.1007/978-3-030-00928-1_31
Lecture Notes in Computer Science
DocType
Volume
ISSN
Journal
11070
0302-9743
Citations 
PageRank 
References 
0
0.34
7
Authors
10
Name
Order
Citations
PageRank
Giacomo Tarroni1528.26
Ozan Oktay228020.15
Matthew Sinclair3448.23
Wenjia Bai444535.84
Andreas Schuh5786.89
Hideaki Suzuki65812.02
Antonio de Marvao7604.27
Declan P. O'Regan825816.33
Stuart A Cook91118.45
Daniel Rueckert109338637.58