Dictionary learning based image descriptor for myocardial registration of CP-BOLD MR

Ilkay Oksuz, Anirban Mukhopadhyay, Marco Bevilacqua, Rohan Dharmakumar, Sotirios A. Tsaftaris

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

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 au-tomating 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 defini-tions in current intensity-based registration frameworks. In this paper, the sparse representations, which are defined by a discriminative dictio-nary 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 repre-sent features in a low dimensional space. The sum of squared differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed descriptor is validated on a challenging dataset of CP-BOLD MR and standard CINE MR ac-quired in baseline and ischemic condition across 10 canines.
Original languageEnglish
Pages (from-to)205-213
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Early online date20 Nov 2015
DOIs
Publication statusPublished - 2015

Keywords

  • CINE MR
  • CP-BOLD MR
  • Dictionary Learning
  • Registration
  • Similarity Metric

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