Synthetic Generation of Myocardial Blood–Oxygen-Level-Dependent MRI Time Series Via Structural Sparse Decomposition Modeling

Abstract

This paper aims to identify approaches that generate appropriate synthetic data (computer generated) for Cardiac Phase-resolved Blood-Oxygen-Level-Dependent (CP–BOLD) MRI. CP–BOLD MRI is a new contrast agent- and stress-free approach for examining changes in myocardial oxygenation in response to coronary artery disease. However, since signal intensity changes are subtle, rapid visualization is not possible with the naked eye. Quantifying and visualizing the extent of disease relies on myocardial segmentation and registration to isolate the myocardium and establish temporal correspondences and ischemia detection algorithms to identify temporal differences in BOLD signal intensity patterns. If transmurality of the defect is of interest pixel-level analysis is necessary and thus a higher precision in registration is required. Such precision is currently not available affecting the design and performance of the ischemia detection algorithms. In this work, to enable algorithmic developments of ischemia detection irrespective to registration accuracy, we propose an approach that generates synthetic pixel-level myocardial time series. We do this by (a) modeling the temporal changes in BOLD signal intensity based on sparse multi-component dictionary learning, whereby segmentally derived myocardial time series are extracted from canine experimental data to learn the model; and (b) demonstrating the resemblance between real and synthetic time series for validation purposes. We envision that the proposed approach has the capacity to accelerate development of tools for ischemia detection while markedly reducing experimental costs so that cardiac BOLD MRI can be rapidly translated into the clinical arena for the noninvasive assessment of ischemic heart disease.

Dictionary-Driven Ischemia Detection From Cardiac Phase-Resolved Myocardial BOLD MRI at Rest

“…To this end, we envision that it would be advantageous to use all images from the CP–BOLD image sequence for a better identification of ischemic regions. Recent experiments on properly generated synthetic data [6] have shown that an independent component analysis (ICA) approach adopted from fMRI [7] outperformed S/D. However, ICA cannot accommodate time shifts present in BOLD time series, which are likely due to physiological differences between different myocardial territories [8].…”

“…However, ICA cannot accommodate time shifts present in BOLD time series, which are likely due to physiological differences between different myocardial territories [8]. This shifting in time characteristic CP–BOLD effect , was suspected by Tsaftaris et al [3] and was statistically shown by Rusu et al [6], using a circulant dictionary model.…”

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

“…In disease this effect is not present. However, visualizing and quantifying such patterns requires significant post-processing, including myocardial registration to establish pixel-precise time series for identifying such patterns [12]. Such spatio-temporal intensity variations of the myocardial BOLD effect cause the methods developed for standard CINE MR registration to under-perform.…”

“…As a result, no CP-BOLD MR myocardial registration algorithms exist and due to this absence either segmental information [11] or synthetic data sets are used [12], to obtain pixel-wise time series. We assume that it is due to lack of proper similarity criteria.…”

Unsupervised Myocardial Segmentation for Cardiac MRI

“…Fully supervised myocardial segmentation (i.e., separating myocardium from the rest of the anatomy) developed for standard CINE MR, however, underperform in the case of CP-BOLD MR due to the spatio-temporal intensity variations of the myocardial BOLD effect [14,19]. Thus, in addition to violating shape invariance (as with standard CINE MR), the principal assumption of appearance invariance (consistent intensity [12]) is violated in CP-BOLD MR as well.…”