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Background  Atherosclerosis 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 (T 1 , T 2 ) and proton density (PD) in a clinical 3 Tesla scanner could characterize vessel disease, and evaluates a practical strategy for accelerated quantification.    Methods  IVMRI 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. T 1 , T 2  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 T 1 , T 2  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 T 1 , T 2  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.    Results  The 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 T 1  providing the best discrimination. SLAM T 1 , T 2  and PD measures for  R  ≤ 10 were indistinguishable from the true means of segmented tissue compartments.    Conclusion  High-resolution IVMRI measures of T 1 , T 2  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 T 1 , T 2  and PD an order-of-magnitude faster to facilitate IVMRI-based characterization of vessel disease.    Electronic supplementary material  The online version of this article (doi:10.1186/s12968-017-0399-6) contains supplementary material, which is available to authorized users.

High-resolution and accelerated multi-parametric mapping with automated characterization of vessel disease using intravascular MRI