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Cardiac Imaging in Cardiovascular Disorders

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A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: 20 January 2027 | Viewed by 3060

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Dr. Leif-Christopher Engel

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Department of Cardiovascular Diseases, German Heart Center Munich, Technical University of Munich (TUM), University Hospital, Munich, Germany
Interests: cardiology; cardiac imaging; angiography

Special Issue Information

Dear Colleagues,

Cardiovascular disorders remain the leading cause of morbidity and mortality worldwide. Accurate diagnosis and risk stratification are crucial for effective management and improved patient outcomes. Cardiac imaging plays a pivotal role in this process, providing non-invasive, detailed insights into the heart’s structure and function. From established techniques like echocardiography, cardiac computed tomography (CT) and cardiac magnetic resonance (CMR), to specialized modalities such as myocardial perfusion imaging, these tools have transformed our ability to detect disease, monitor progression, and guide therapeutic decisions.

This Special Issue aims to present the latest advancements and clinical applications of cardiac imaging in various cardiovascular disorders. We invite original research and reviews that highlight the impact of these powerful diagnostic tools on patient care.

Dr. Leif-Christopher Engel
Guest Editor

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Keywords

coronary CT angiography
cardiac magnetic resonance imaging
molecular imaging
echocardiography
stress echocardiography
myocardial perfusion imaging

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Research

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11 pages, 781 KB

Open AccessArticle

Relationship Between Perivascular Fat Inflammation and Coronary Atherosclerotic Plaque Composition

by Leif-Christopher Engel, Rafael Adolf, Salvatore Cassese, Erion Xhepa, Adnan Kastrati, Michael Joner, Heribert Schunkert, Martin Hadamitzky and Philipp Nicol

J. Clin. Med. 2026, 15(4), 1652; https://doi.org/10.3390/jcm15041652 - 22 Feb 2026

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Abstract

Background: Perivascular fat attenuation index (FAI) derived from coronary CT angiography (CCTA) has emerged as a quantitative biomarker of vascular inflammation, with potential to improve risk stratification in coronary artery disease (CAD) patients. This study aimed to evaluate plaque characteristics of coronary atherosclerotic lesions in patients with high (≥−70.1 HU) or low FAI of pericoronary adipose tissue. Methods: In a retrospective analysis, patients with suspected or confirmed CAD who underwent coronary CTA were included. Coronary lesions were classified into two groups based on their perivascular inflammation as assessed by CCTA: high perivascular FAI phenotype (≥−70.1 HU) versus low FAI phenotype (<−70.1 HU). Both groups were compared with respect to various patient- and lesion-specific characteristics. Results: A total of 247 coronary lesions were analyzed in this study. Of these, 36 (14.6%) lesions were associated with high perivascular inflammation (high FAI phenotpye) and 211 (85.4%) were associated with low perivascular inflammation (low FAI phenotype). Lesions with a high FAI phenotype demonstrated a significantly higher amount of non-calcified plaque volume (NCPV) compared to lesions with a low FAI phenotype [(111.8 mm³ (69.4–184.2) versus 87.7 mm³ (44.6–143.0), p < 0.003]. NCPV emerged as a consistent and significant predictor of fat attenuation positive plaque in both univariate (OR 1.030 [95% CI, 1.010–1.050], p = 0.003); and multivariate logistic regression analyses (OR 1.028 [95% CI, 1.008–1.050]. p = 0.007). Additionally, lesions with a high FAI phenotype less frequently exhibited homogeneous calcification than their low FAI phenotype counterparts (25% versus 46.9%, p = 0.014). Conclusions: Coronary lesions associated with a high FAI phenotype on coronary CCTA consist predominantly of non-calcified plaques. Conversely, lesions characterized by a low perivascular FAI phenotype are primarily calcified and seem to be more homogeneous by visual assessment. Further prospective studies are warranted to validate these associations and explore the underlying pathophysiological mechanisms. Full article

(This article belongs to the Special Issue Cardiac Imaging in Cardiovascular Disorders)

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Review

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27 pages, 12101 KB

Open AccessReview

A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation

by Madallah Alruwaili and Mahmood A. Mahmood

J. Clin. Med. 2026, 15(13), 5314; https://doi.org/10.3390/jcm15135314 - 7 Jul 2026

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Abstract

Background: Myocardial perfusion scintigraphy (MPS) is widely used for noninvasive assessment of coronary artery disease, but publicly available datasets suitable for reproducible deep learning segmentation studies remain limited. This paper proposes CardioProto-SegNet, an image-only 3D anatomy-directed segmentation framework for myocardial region delineation using the public Myocardial Perfusion Scintigraphy Image Database v1.0.0 from PhysioNet, which contains 83 patient studies. Methods: The model is implemented as a 3D U-Net-like residual encoder–decoder network enhanced with squeeze-and-excitation channel recalibration and compact prototype-memory refinement at the bottleneck. Because the public dataset does not provide structured clinical variables, all reported results correspond to image-only myocardium segmentation. Results: Experimental evaluation demonstrated reliable segmentation performance on the available public dataset. CardioProto-SegNet achieved a Dice score of 0.7402 on the holdout test split. In five-fold cross-validation, the model obtained a mean Dice of 0.8239, mean IoU of 0.6870, mean accuracy of 0.9943, mean ROC-AUC of 0.9867, and mean PR-AUC of 0.8561. Since confirmed ischemia or infarction labels were not available, an exploratory image-derived subgroup analysis was additionally performed based on myocardial ROI uptake heterogeneity to examine model behavior in lower- and higher-heterogeneity cases. The ablation study showed that residual connections were important for stable segmentation performance, while the deeper variant achieved the highest tested performance, with a Dice score of 0.8290, IoU of 0.7096, and PR-AUC of 0.8831. Conclusions: Overall, the findings suggest that CardioProto-SegNet provides a reproducible public dataset benchmark for myocardium segmentation in MPS and may serve as a foundation for future downstream quantitative and CAD-oriented analysis when larger datasets with clinical labels become available. Full article

(This article belongs to the Special Issue Cardiac Imaging in Cardiovascular Disorders)

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22 pages, 3533 KB

Open AccessReview

Cardiac CT in the Era of Precision Cardiology: From Calcium Scoring to Comprehensive Risk Profiling

by Gianluigi Napoli, Donatella Tansella, Maria Teresa Savo, Abdulrahman Alsergani, Laura Fusini, Saima Mushtaq, Andrea Baggiano, Fabio Fazzari, Gianluca Pontone, Michele Davide Latorre, Eduardo Urgesi, Maria Cristina Carella, Raffaella Motta, Andrea Igoren Guaricci and Valeria Pergola

J. Clin. Med. 2026, 15(13), 5313; https://doi.org/10.3390/jcm15135313 - 7 Jul 2026

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Abstract

Cardiac computed tomography (CT) has evolved into a pivotal tool in precision cardiology, enabling comprehensive, non-invasive evaluation of coronary anatomy, plaque composition, vascular function, and inflammation. From calcium scoring to advanced physiological imaging, CT now integrates multiple layers of cardiovascular information within a unified diagnostic framework. Coronary artery calcium (CAC) quantification provides a robust, reproducible measure of atherosclerotic burden and refines risk estimation beyond traditional algorithms, particularly in asymptomatic individuals with an intermediate likelihood. Building upon this anatomical foundation, coronary CT angiography (CCTA) extends evaluation to the anatomical and morphological characterization of coronary artery disease (CAD), identifying both obstructive and non-obstructive plaques with high prognostic accuracy. The addition of CT-derived fractional flow reserve (FFR-CT) and stress perfusion CT (CTP) bridges anatomy and physiology, improving identification of flow-limiting stenoses and guiding revascularization decisions while reducing unnecessary invasive procedures. Beyond luminal assessment, CT-derived biomarkers such as the perivascular fat attenuation index (pFAI) have introduced a new dimension of vascular inflammation imaging, revealing residual risk even in patients without significant stenosis and suggesting novel pathways for individualized therapeutic targeting. Driven by advances in artificial intelligence and photon-counting detector technology, cardiac CT is transitioning from a purely diagnostic modality to an integrative platform for cardiovascular phenotyping. Taken as a whole, this integration of structural, functional, and biological data provides a genuinely holistic view of coronary health. In practical terms, it shifts clinical decision-making from population-based risk models toward precision-guided patient-specific strategies. Full article

(This article belongs to the Special Issue Cardiac Imaging in Cardiovascular Disorders)

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