Three-dimensional quantification of myocardial perfusion during regadenoson stress computed tomography

[Type 2 diabetes mellitus and coronary artery disease: features of perfusion volume computed tomography of the heart in a pharmacological test with adenosine triphosphate]

AIM

To study myocardial perfusion in patients with coronary artery disease (CAD) with and without type 2 diabetes mellitus (DM) using volumetric computed tomography (VCT) of the heart with a pharmacological test with adenosine triphosphate (ATP).

MATERIALS AND METHODS

The study included 93 patients, of which 18 had CAD with DM, and 50 had CAD without DM. All patients underwent one of the stress tests, cardiac VCT with ATP test, invasive coronary angiography, or CT coronary angiography. Left ventricle (LV) myocardial perfusion was evaluated for hypoperfusion zones and the calculation of semi-quantitative indices: decrease of LV myocardial density, LV myocardial perfusion index, transmural perfusion coefficient, and our proposed new indicator - myocardial perfusion reserve (MPR).

RESULTS

The MPR index value in the hypoperfusion zones in patients with CAD and DM was 0.64 [0.62-0.66], in patients with CAD without diabetes 0.65 [0.63-0.66]; p=0.4; the value of the transmural perfusion coefficient in the areas of abnormal LV myocardial perfusion in patients with CAD and DM was 0.81 [0.80-0.86] versus 0.83 [0.80-0.85] in patients with CAD without DM (p=0.6). More hypoperfusion segments were observed in patients with CAD and DM (33.3%) compared to those without DM (14%; p=0.029). The MPR index in the hypoperfusion zones in patients with CAD with intact coronary arteries (CA) and DM was 0.56 [0.54-0.60] versus 0.55 [0.54-0.62] in patients with CAD with intact CA without DM; p=0.2.

CONCLUSION

In patients with CAD and type 2 DM, according to the VCT with ATP test, more foci hypoperfusion areas were detected, regardless of the severity of coronary artery involvement, compared with patients with CAD without DM, which may be due to the microangiopathy in the myocardium. The similarity of the MPR parameters in the hypoperfusion zones associated with hemodynamic stenosis of the CA and with intact CAs indicates the ischemic genesis of these zones. For citation: Soboleva GN, Minasyan AA, Gaman SA, Rogoza AN, Molina LP, Soboleva TV, Shariya MA, Ternovoy SK, Karpov YuA. Type 2 diabetes mellitus and coronary artery disease: features of perfusion volume computed tomography of the heart in a pharmacological test with adenosine triphosphate. Terapevticheskii Arkhiv (Ter. Arkh.). 2023;95(4):309-315. DOI: 10.26442/00403660.2023.04.202158.

Fusion of Three-Dimensional Echocardiographic Regional Myocardial Strain with Cardiac Computed Tomography for Noninvasive Evaluation of the Hemodynamic Impact of Coronary Stenosis in Patients with Chest Pain

BACKGROUND

Combined evaluation of coronary stenosis and the extent of ischemia is essential in patients with chest pain. Intermediate-grade stenosis on computed tomographic coronary angiography (CTCA) frequently triggers downstream nuclear stress testing. Alternative approaches without stress and/or radiation may have important implications. Myocardial strain measured from echocardiographic images can be used to detect subclinical dysfunction. The authors recently tested the feasibility of fusion of three-dimensional (3D) echocardiography-derived regional resting longitudinal strain with coronary arteries from CTCA to determine the hemodynamic significance of stenosis. The aim of the present study was to validate this approach against accepted reference techniques.

METHODS

Seventy-eight patients with chest pain referred for CTCA who also underwent 3D echocardiography and regadenoson stress computed tomography were prospectively studied. Left ventricular longitudinal strain data (TomTec) were used to generate fused 3D displays and detect resting strain abnormalities (RSAs) in each coronary territory. Computed tomographic coronary angiographic images were interpreted for the presence and severity of stenosis. Fused 3D displays of subendocardial x-ray attenuation were created to detect stress perfusion defects (SPDs). In patients with stenosis >25% in at least one artery, fractional flow reserve was quantified (HeartFlow). RSA as a marker of significant stenosis was validated against two different combined references: stenosis >50% on CTCA and SPDs seen in the same territory (reference standard A) and fractional flow reserve < 0.80 and SPDs in the same territory (reference standard B).

RESULTS

Of the 99 arteries with no stenosis >50% and no SPDs, considered as normal, 19 (19%) had RSAs. Conversely, with stenosis >50% and SPDs, RSAs were considerably more frequent (17 of 24 [71%]). The sensitivity, specificity, and accuracy of RSA were 0.71, 0.81, and 0.79, respectively, against reference standard A and 0.83, 0.81, and 0.82 against reference standard B.

CONCLUSIONS

Fusion of CTCA and 3D echocardiography-derived resting myocardial strain provides combined displays, which may be useful in determination of the hemodynamic or functional impact of coronary abnormalities, without additional ionizing radiation or stress testing.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

INTRODUCTION

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.