Quantitative analysis of SPECT and PET has become a major part of nuclear cardiology practice. Current software tools can automatically segment the left ventricle, quantify function, establish myocardial perfusion maps, and estimate global and local measures of stress/rest perfusion, all with minimal user input. State-of-the-art automated techniques have been shown to offer high diagnostic accuracy for detecting coronary artery disease, as well as predict prognostic outcomes. This article briefly reviews these techniques, highlights several challenges, and discusses the latest developments.

In the context of myocardial perfusion imaging (MPI) with cardiac magnetic resonance (CMR), there is ongoing debate on the merits of using technically complex acquisition methods to achieve whole-heart spatial coverage, rather than conventional 3-slice acquisition. An adequately powered comparative study is difficult to achieve given the requirement for two separate stress CMR studies in each patient. The aim of this work is to draw relevant conclusions from SPECT MPI by comparing whole-heart versus simulated 3-slice coverage in a large existing dataset. SPECT data from 651 patients with suspected coronary artery disease who underwent invasive angiography were analyzed. A computational approach was designed to model 3-slice MPI by retrospective subsampling of whole- heart data. For both whole-heart and 3-slice approaches, the diagnostic performance and the stress total perfusion deficit (TPD) score-a measure of ischemia extent/severity-were quantified and compared. Diagnostic accuracy for the 3-slice and whole-heart approaches were similar (area under the curve: 0.843 vs. 0.855, respectively; P = 0.07). The majority (54%) of cases missed by 3-slice imaging had primarily apical ischemia. Whole-heart and 3-slice TPD scores were strongly correlated (R² = 0.93, P < 0.001) but 3-slice TPD showed a small yet significant bias compared to whole-heart TPD (- 1.19%; P < 0.0001) and the 95% limits of agreement were relatively wide (- 6.65% to 4.27%). Incomplete ventricular coverage typically acquired in 3-slice CMR MPI does not significantly affect the diagnostic accuracy. However, 3-slice MPI may fail to detect severe apical ischemia and underestimate the extent/severity of perfusion defects. Our results suggest that caution is required when comparing the ischemic burden between 3-slice and whole-heart datasets, and corroborate the need to establish prognostic thresholds specific to each approach.

Background

It has recently been shown that magnetic resonance (MR) "native T1" mapping is capable of characterizing abnormal microcirculation in patients with obstructive coronary artery disease (CAD). In studies involving women with signs and symptoms of ischemia and no obstructive CAD (INOCA), however, the potential role of native T1 as an imaging marker and its association with indices of diastolic function or vasodilator-induced myocardial ischemia have not been explored. We investigated whether native T1 in INOCA is associated with reduced myocardial perfusion reserve index (MPRI) or with diastolic dysfunction.

Methods

Twenty-two female patients with INOCA and twelve female reference controls with matching age and body-mass index were studied. The patients had evidence of vasodilator-induced ischemia without obstructive CAD or any prior infarction. All 34 subjects underwent stress/rest MR including native T1 mapping (MOLLI 5(3)3) at 1.5-Tesla.

Results

Compared with controls, patients had similar morphology/function. As expected, MPRI was significantly reduced in patients compared to controls (1.78 ± 0.39 vs. 2.49 ± 0.41, p < 0.0001). Native T1 was significantly elevated in patients (1040.1 ± 29.3 ms vs. 1003.8 ± 18.5 ms, p < 0.001) and the increased T1 showed a significant inverse correlation with MPRI (r = -0.481, p = 0.004), but was not correlated with reduced diastolic strain rate.

Conclusions

Symptomatic women with INOCA have elevated native T1 compared to matched reference controls and there is a significant association between elevated native T1 and impaired MPRI, considered a surrogate measure of ischemia severity in this cohort. Future studies in a larger cohort are needed to elucidate the mechanism underlying this inverse relationship.

Aims

Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics.

Methods and results

The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001).

Conclusions

Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone.