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

Abstract

Cardiac Phase-resolved Blood-Oxygen-Level Dependent (CP-BOLD) MRI provides a unique opportunity to image an ongoing ischemia at rest. However, it requires post-processing to evaluate the extent of ischemia. To address this, here we propose an unsupervised ischemia detection (UID) method which relies on the inherent spatio-temporal correlation between oxygenation and wall motion to formalize a joint learning and detection problem based on dictionary decomposition. Considering input data of a single subject, it treats ischemia as an anomaly and iteratively learns dictionaries to represent only normal observations (corresponding to myocardial territories remote to ischemia). Anomaly detection is based on a modified version of One-class Support Vector Machines (OCSVM) to regulate directly the margins by incorporating the dictionary-based representation errors. A measure of ischemic extent (IE) is estimated, reflecting the relative portion of the myocardium affected by ischemia. For visualization purposes an ischemia likelihood map is created by estimating posterior probabilities from the OCSVM outputs, thus obtaining how likely the classification is correct. UID is evaluated on synthetic data and in a 2D CP-BOLD data set from a canine experimental model emulating acute coronary syndromes. Comparing early ischemic territories identified with UID against infarct territories (after several hours of ischemia), we find that IE, as measured by UID, is highly correlated (Pearson's r=0.84) with respect to infarct size. When advances in automated registration and segmentation of CP-BOLD images and full coverage 3D acquisitions become available, we hope that this method can enable pixel-level assessment of ischemia with this truly non-invasive imaging technique.

Fig. 1

BOLD signal intensity time series (as segmental averages of six different radial segments across the images, ie., frames, of the cine movie) extracted from rest CP–BOLD MRI data of the same subject, at baseline (left) and under ischemia (right). (CP–BOLD is ECG-triggered and first and last time points correspond to diastole. Time series have been normalized according to the process described in Section II-B for ease of visualization.)

Fig. 2

Workflow of the proposed unsupervised ischemia detection (UID).

Fig. 3

Effect of registration on intensity time series.

Fig. 4

Example of “functional time series” from rest CP–BOLD MRI of a subject under baseline (left) and ischemia (right) conditions.

Fig. 5

Examples of synthetic data sets with simulated remote (green) and ischemic (red) time series, for two different ischemic extents (IE) considered.

Fig. 6

Visual representation of the evolution of the classification labels at each iteration of DDAD (green for normal, red for anomaly) from an initial state (top row). Bottom row represents ground-truth (GT) assignments.

Fig. 7

Examples of intensity and functional time series under ischemia (left) and related circulant patterns (right), learned at the first (dashed line) and last iteration (solid line) of UID.

Fig. 8

Ischemia classification maps related to one subject obtained with several methods (overlaid on an image in diastole from the CP–BOLD image sequence) compared with the corresponding LGE image obtained after 3hrs of ischemia and during reperfusion.

Fig. 9

An ischemia likelihood map as obtained by UID for another subject, color-coded and overlaid on the original CP–BOLD image in diastole (a). Together we also show a six-segment bulls-eye plot of likelihood for the same case (b); the color bar shown refers to both (a) and (b). In (c) the corresponding LGE image is reported.