2 A comparison of epicardial adipose tissue quantification between cardiac magnetic resonance and computed tomography in people with and without type 2 diabetes

<h3>Introduction</h3> Several cardiac MRI (CMR) techniques exist for epicardial adipose tissue (EAT) quantification, many of which require additional sequence acquisition with lengthy post-processing analysis. However, EAT may readily be quantified on routinely acquired CMR cine images. We compared cine CMR EAT measurements against a reference standard technique of computed tomography (CT),^1–3 and determined whether this could distinguish EAT expansion in people with and without type 2 diabetes (T2D). <h3>Materials and Methods</h3> Participants with and without T2D, free of prevalent cardiovascular disease, were prospectively enrolled. Subjects underwent same-day non-contrast cardiac CT and 3T CMR. CT EAT whole heart volumetric analysis was performed using a fully-automated deep learning algorithm.^4–5 CMR EAT quantification was performed on 4-chamber long-axis bSSFP cines at end-systole. We compared multiple CMR EAT thickness measures and CMR EAT area measured around the right ventricle (RV-EAT) and biventricular (BV-EAT). EAT measures were indexed to height. For CMR, intra-observer variability was evaluated on 20 scans by repeat analysis a minimum of two weeks apart, and test-retest reproducibility in 29 participants who underwent repeat scanning at 1–2 weeks. <h3>Results</h3> This analysis included 217 participants: 185 with T2D [age 63±7 years, male 59%, BMI 30 (27,33) kg/m², HbA1c 7.1 (6.6, 8)%] and 32 controls [age 59±8 years, male 66%, BMI 26 (24.8,30)kg/m², HbA1c 5.4 (5.4, 5.6)%]. T2D patients had greater BMI, waist circumference and prevalence of hypertension and hypercholesterolaemia compared to controls (p&lt;0.001 for all). All EAT measures were significantly higher in T2D compared to controls, in particular: CT-EAT volume 74 (57, 100)cm³/m vs 41 (30, 60)cm³/m (p &lt;0.001), CMR right atrioventricular groove EAT thickness 16±6 mm/m vs 12±5 mm/m, (p=0.002) and CMR biventricular (BV)-EAT area 6±2 cm²/m vs 5±2 cm²/m (p=0.021). CT-EAT volume had the greatest statistically significant correlation with CMR BV-EAT area which was of moderate strength (r=0.69, p&lt;0.001). Intra-observer and test-retest repeatability was good for CMR BV-EAT area with ICC 0.88 (95% CI 0.73–0.95) and ICC 0.83 (95% CI 0.66,0.92) respectively. <h3>Conclusion</h3> Both CT EAT volume and CMR BV-EAT area are higher in people with T2D than those without, with moderate inter-modality correlation. CMR BV-EAT area has good intra-observer and test-retest reproducibility, making it a feasible technique for EAT measurement. <h3>References</h3> Balcer B, Rassaf T, Mahabadi AA. Computed tomography imaging of epicardial adipose tissue. 2020 [cited 2024 Aug 1];55–70. Available from: https://link.springer.com/chapter/10.1007/978-3-030-40570-0_6 Eisenberg E, McElhinney PA, Commandeur F, Chen X, Cadet S, Goeller M, <i>et al</i>. Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects. <i>Circ Cardiovasc Imaging</i> [Internet]. 2020 Feb 1 [cited 2024 Aug 1];<b>13</b>(2):E009829. Available from: https://www.ahajournals.org/doi/10.1161/CIRCIMAGING.119.009829 Commandeur F, Goeller M, Razipour A, Cadet S, Hell MM, Kwiecinski J, <i>et al</i>. Fully automated CT quantification of epicardial adipose tissue by deep learning: a multicenter study. <i>Radiol Artif Intell</i> [Internet]. 2019 Nov 1 [cited 2024 Aug 1];<b>1</b>(6). Available from: https://pubs.rsna.org/doi/10.1148/ryai.2019190045 Ayton SL, Yeo JL, Gulsin GS, Dattani A, Bilak J, Deshpande A, <i>et al</i>. Association of epicardial adipose tissue with early structural and functional cardiac changes in Type 2 diabetes. <i>Eur J Radiol.</i> 2024 May 1;<b>174</b>:111400. Commandeur F, Goeller M, Betancur J, Cadet S, Doris M, Chen X, <i>et al</i>. Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT. <i>IEEE Trans Med Imaging.</i> 2018 Aug 1;<b>37</b>(8):1835–46.