Purpose

To extend the MR MultiTasking-based Multidimensional Assessment of Cardiovascular System (MT-MACS) technique with larger spatial coverage and water-fat separation for comprehensive aortocardiac assessment.

Methods

MT-MACS adopts a low-rank tensor image model for 7D imaging, with three spatial dimensions for volumetric imaging, one cardiac motion dimension for cine imaging, one respiratory motion dimension for free-breathing imaging, one T2-prepared inversion recovery time dimension for multi-contrast assessment, and one T2*-decay time dimension for water-fat separation. Nine healthy subjects were recruited for the 3T study. Overall image quality was scored on bright-blood (BB), dark-blood (DB), and gray-blood (GB) contrasts using a 4-point scale (0-poor to 3-excellent) by two independent readers, and their interreader agreement was evaluated. Myocardial wall thickness and left ventricular ejection fraction (LVEF) were quantified on DB and BB contrasts, respectively. The agreement in these metrics between MT-MACS and conventional breath-held, electrocardiography-triggered 2D sequences were evaluated.

Results

MT-MACS provides both water-only and fat-only images with excellent image quality (average score = 3.725/3.780/3.835/3.890 for BB/DB/GB/fat-only images) and moderate to high interreader agreement (weighted Cohen's kappa value = 0.727/0.668/1.000/1.000 for BB/DB/GB/fat-only images). There were good to excellent agreements in myocardial wall thickness measurements (intraclass correlation coefficients [ICC] = 0.781/0.929/0.680/0.878 for left atria/left ventricle/right atria/right ventricle) and LVEF quantification (ICC = 0.716) between MT-MACS and 2D references. All measurements were within the literature range of healthy subjects.

Conclusion

The refined MT-MACS technique provides multi-contrast, phase-resolved, and water-fat imaging of the aortocardiac systems and allows evaluation of anatomy and function. Clinical validation is warranted.

Background

Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially obtainable. It remains unclear whether other widespread tests such as transthoracic echocardiograms (TTEs) can be used to predict CAC.

Methods

Using a data set of 2,881 TTE videos paired with coronary calcium CTs, we trained a video-based artificial intelligence convolutional neural network to predict CAC scores from parasternal long-axis views. We evaluated the model's ability to classify patients from a held-out sample as well as an external site sample into zero CAC and high CAC (CAC ≥ 400 Agatston units) groups by receiver operating characteristic and precision-recall curves. We also investigated whether such classifications prognosticated significant differences in 1-year mortality rates by the log-rank test of Kaplan-Meier curves.

Results

Transthoracic echocardiogram artificial intelligence models had high discriminatory abilities in predicting zero CAC (receiver operating characteristic area under the curve [AUC] = 0.81 [95% CI, 0.74-0.88], F1 score = 0.95) and high CAC (AUC = 0.74 [0.68-0.8], F1 score = 0.74). This performance was confirmed in an external test data set of 92 TTEs (AUC = 0.75 [0.65-0.85], F1 score = 0.77; and AUC = 0.85 [0.76-0.93], F1 score = 0.59, respectively). Risk stratification by TTE-predicted CAC performed similarly to CT CAC scores in prognosticating significant differences in 1-year survival in high-CAC patients (CT CAC ≥ 400 vs CT CAC < 400, P = .03; TTE-predicted CAC ≥ 400 vs TTE-predicted CAC < 400, P = .02).

Conclusions

A video-based deep learning model successfully used TTE videos to predict zero CAC and high CAC with high accuracy. Transthoracic echocardiography-predicted CAC prognosticated differences in 1-year survival similar to CT CAC. Deep learning of TTEs holds promise for future adjunctive coronary artery disease risk stratification to guide preventive therapies.

Background

Hepatic disease is linked to cardiovascular events but the independent association between hepatic and cardiovascular disease remains unclear, given shared risk factors.

Methods

This was a retrospective study of consecutive patients with a clinical cardiac MRI (CMR) and a serological marker of hepatic fibrosis, the FIB-4 score, within one year of clinical imaging. We assessed the relations between FIB-4 scores grouped based on prior literature: low (< 1.3), moderate (1.3-3.25), and high (>3.25), and abnormalities detected by comprehensive CMR grouped into 4 domains: cardiac structure (end diastolic volumes, atrial dimensions, wall thickness); cardiac function (ejection fractions, wall motion abnormalities, cardiac output); vascular structure (ascending aortic and pulmonary arterial sizes); and cardiac composition (late gadolinium enhancement, T1 and T2 times). We used Poisson regression to examine the association between the conventionally defined FIB-4 category (low <1.3, moderate 1.3-3.25, and high >3.25) and any CMR abnormality while adjusting for demographics and traditional cardiovascular risk factors.

Results

Of the 1668 patients studied (mean age: 55.971 ± 7.28, 901 [54%] male), 85.9% had ≥1 cardiac abnormality with increasing prevalence seen within the low (82.0%) to moderate (88.8%) to high (92.3%) FIB-4 categories. Multivariable analyses demonstrated the presence of any cardiac abnormality was significantly associated with having a high-range FIB-4 (prevalence ratio 1.07, 95% CI: 1.01-1.13); notably, the presence of functional cardiac abnormalities were associated with being in the high FIB-4 range (1.41, 1.21-1.65) and any vascular abnormalities with being in the moderate FIB-4 range (1.22, 1.01-1.47).

Conclusions

Elevated FIB-4 was associated with cardiac functional and vascular abnormalities even after adjustment for shared risk factors in a cohort of patients with clinically referred CMR. These CMR findings indicate that cardiovascular abnormalities exist in the presence of subclinical hepatic fibrosis, irrespective of shared risk factors, underscoring the need for further studies of the heart-liver axis.

Artificial intelligence (AI) has been developed for echocardiography^1-3, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of -10.4%, 95% confidence interval: -13.2% to -7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of -0.96%, 95% confidence interval: -1.34% to -0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.