The clinical value and safety of ECG-gated dipyridamole myocardial perfusion imaging in patients with aortic stenosis

Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT

PURPOSE

Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, previously published methods have depended on polar maps, required manual correction, and normal database. In this study, we propose a polar map-free 3D DL algorithm to predict obstructive disease.

METHODS

We included 1861 subjects who underwent MPI using cadmium-zinc-telluride camera and subsequent coronary angiography. The subjects were divided into parameterization and external validation groups. We implemented a fully automatic algorithm to segment myocardium, perform registration, and apply normalization. We further flattened the image based on spherical coordinate system transformation. The proposed model consisted of a component to predict patent arteries and a component to predict disease in each vessel. The model was cross-validated in the parameterization group, and then further tested using the external validation group. The performance was assessed by area under receiver operating characteristic curves (AUCs) and compared with TPD.

RESULTS

Our algorithm preprocessed all images accurately as confirmed by visual inspection. In patient-based analysis, the AUC of the proposed model was significantly higher than that for stress-TPD (0.84 vs 0.76, p < 0.01). In vessel-based analysis, the proposed model also outperformed regional stress-TPD (AUC = 0.80 vs 0.72, p < 0.01). The addition of quantitative images did not improve the performance.

CONCLUSIONS

Our proposed polar map-free 3D DL algorithm to predict obstructive CAD from MPI outperformed TPD and did not require manual correction or a normal database.

Integration of quantitative absolute myocardial blood flow estimates from dynamic CZT-SPECT improves the detection of coronary artery disease

BACKGROUND

Balanced ischemia with multi-vessel coronary artery disease (CAD) is difficult to diagnose with semiquantitative single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Dynamic cardiac SPECT provides quantitative estimations of stenosis severity and ischemic burden by assessing myocardial flow reserve (MFR) and myocardial blood flow (MBF). The aim of this study was to evaluate the incremental value of dynamic SPECT in multi-vessel coronary artery disease (CAD).

METHODS

Patients with suspected CAD who underwent dynamic ECG-gated dipyridamole MPI and coronary angiography within 6 months were retrospectively reviewed. The performance of summed stress, rest and difference scores (SSS, SRS, SDS), post-stress and resting MBF (MBFs, MBFr) and MFR were compared at both patient level and vessel level.

RESULTS

In 32 patients with 39 stenotic vessels, 12 had three-vessel disease (38%). Globally increased SSS and impaired MBF values were significantly associated with significant CAD at the patient level, but SDS and MFR were not. Regional increases in SSS and reductions in both MBFs and MBFr were significantly associated with stenotic vessels. The best cutoff value of global MBFs to predict CAD was 3.5 ml·g-1·min-1 (area under the curve, AUC = .84, P = .002). The best cutoff value of regional MBFs to detect significant stenosis was 3.6 ml·g-1·min-1 (AUC = .74, P < .001). However, the best possible cut-off values of MFR were not found. Sex-difference in both global and regional MBFr but MBFs was found, which might result in the non-significance in MFR.

CONCLUSIONS

This study validated a clinically available method to quantify MFR using dynamic CZT-SPECT. This method improved the detectability of multi-vessel CAD, and absolute MBFs was superior to MFR and other semiquantitative MPI parameters.

Coronary Microcirculation in Aortic Stenosis: Pathophysiology, Invasive Assessment, and Future Directions

With the increasing prevalence of aortic stenosis (AS) due to a growing elderly population, a proper understanding of its physiology is paramount to guide therapy and define severity. A better understanding of the microvasculature in AS could improve clinical care by predicting left ventricular remodeling or anticipate the interplay between epicardial stenosis and myocardial dysfunction. In this review, we combine five decades of literature regarding microvascular, coronary, and aortic valve physiology with emerging insights from newly developed invasive tools for quantifying microcirculatory function. Furthermore, we describe the coupling between microcirculation and epicardial stenosis, which is currently under investigation in several randomized trials enrolling subjects with concomitant AS and coronary disease. To clarify the physiology explained previously, we present two instructive cases with invasive pressure measurements quantifying coexisting valve and coronary stenoses. Finally, we pose open clinical and research questions whose answers would further expand our knowledge of microvascular dysfunction in AS. These trials were registered with NCT03042104, NCT03094143, and NCT02436655.