TY  - CHAP
T2  - Medical Image Computing and Computer-Assisted Intervention. MICCAI 2015
A1  - Oksuz, Ilkay
A1  - Mukhopadhyay, Anirban
A1  - Bevilacqua, Marco
A1  - Dharmakumar, Rohan
A1  - Tsaftaris, Sotirios A.
SP  - 205
UR  - http://link.springer.com/chapter/10.1007%2F978-3-319-24571-3_25#
AV  - none
Y1  - 2015///
M1  - 9350
ID  - eprints2770
KW  - Registration; Dictionary Learning; Similarity Metric; CP-BOLD MR; CINE MR
TI  - Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR
PB  - Springer
T3  - Lecture Notes in Computer Science
EP  - 213
SN  - 978-3-319-24570-6
N2  - Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest. The precise registration among the cardiac phases in this cine type acquisition is essential for automating the analysis of images of this technique, since it can potentially lead to better specificity of ischemia detection. However, inconsistency in myocardial intensity patterns and the changes in myocardial shape due to the heart?s motion lead to low registration performance for state-of-the-art methods. This low accuracy can be explained by the lack of distinguishable features in CP-BOLD and inappropriate metric definitions in current intensity-based registration frameworks. In this paper, the sparse representations, which are defined by a discriminative dictionary learning approach for source and target images, are used to improve myocardial registration. This method combines appearance with Gabor and HOG features in a dictionary learning framework to sparsely represent features in a low dimensional space. The sum of absolute differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed approach is validated on a dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canines.
ER  -