Abstract
BackgroundAtherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention. This study tests whether intravascular magnetic resonance imaging (IVMRI) measures of relaxation times (T1, T2) and proton density (PD) in a clinical 3 Tesla scanner could characterize vessel disease, and evaluates a practical strategy for accelerated quantification.MethodsIVMRI was performed in fresh human artery segments and swine vessels in vivo, using fast multi-parametric sequences, 1–2 mm diameter loopless antennae and 200–300 μm resolution. T1, T2 and PD data were used to train a machine learning classifier (support vector machine, SVM) to automatically classify normal vessel, and early or advanced disease, using histology for validation. Disease identification using the SVM was tested with receiver operating characteristic curves. To expedite acquisition of T1, T2 and PD data for vessel characterization, the linear algebraic method (‘SLAM’) was modified to accommodate the antenna’s highly-nonuniform sensitivity, and used to provide average T1, T2 and PD measurements from compartments of normal and pathological tissue segmented from high-resolution images at acceleration factors of R ≤ 18-fold. The results were validated using compartment-average measures derived from the high-resolution scans.ResultsThe SVM accurately classified ~80% of samples into the three disease classes. The ‘area-under-the-curve’ was 0.96 for detecting disease in 248 samples, with T1 providing the best discrimination. SLAM T1, T2 and PD measures for R ≤ 10 were indistinguishable from the true means of segmented tissue compartments.ConclusionHigh-resolution IVMRI measures of T1, T2 and PD with a trained SVM can automatically classify normal, early and advanced atherosclerosis with high sensitivity and specificity. Replacing relaxometric MRI with SLAM yields good estimates of T1, T2 and PD an order-of-magnitude faster to facilitate IVMRI-based characterization of vessel disease.
Highlights
Atherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention
We found that the combination of all three parameters, Spin-lattice relaxation time (T1), Spin-spin relaxation time (T2) and proton density (PD), provided the most accurate disease classification in 30°; and a three-dimensional (3D) ‘feature space’ (Table 1, Figs. 3 and 5)
Our studies indicate that T1, T2, and PD may suffice to discriminate atherosclerotic lesion stage, and that a three-parameter support vector machine (SVM) classifier could do so automatically
Summary
Atherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention. Because X-ray cannot detect vessel wall, non-calciferous lesion morphology or early-stage lesions with mild stenosis [3], it is insensitive to most of the AHA metrics that define disease stage. While both intravascular ultrasound (IVUS) and optical coherence tomography (OCT) can provide transluminal imaging [4], they require X-ray guidance and may be confounded by calcification [5, 6], or penetration depth and the need for a blood-free environment [4], respectively. Advances in all of these modalities are promising [7], but no single technique is presently suited for minimally-invasive assessment of all of the relevant AHA disease characteristics needed to monitor plaque progression or regression [8] or to document the efficacy of therapy or lifestyle changes
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