Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR

Abstract

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 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.

Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI

“…To achieve this, precise segmentation and nonlinear registration of the myocardium among the frames (the cardiac phases) in the cine stack would be required. Unfortunately, at present due to BOLD contrast variations, classical approaches to segmentation [15] and registration [14] fail to reach sufficient accuracy.…”

Unsupervised Myocardial Segmentation for Cardiac BOLD

“…However, these patterns are subtle and changes occurring due to disease cannot be directly visualized [2]. In fact, identifying them requires significant post-processing, including myocardial segmentation and registration [5], prior to computer aided diagnosis via simple [3] or sophisticated pattern recognition methods [4]. This paper presents a segmentation method tailored to CP-BOLD MRI data, which is unsupervised and fully automated.…”

“…1A shows) it can significantly affect segmentation performance. Locally these temporal variations influence registration performance [5], which results in under performance of Atlas-based techniques. Early on approaches tailored for BOLD MRI myocardial segmentation were semi-automated and relied on boundary tracking [8].…”

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

“…A similar strategy was considered in [44] for brain segmentation in CT images. Finally, a dictionary of features was learned in [45] and an SSD measure was used for the registration of cardiac MRI.…”