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Diagnostics
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New Perspectives in Cardiac Imaging
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New Perspectives in Cardiac Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1760

Special Issue Editor

Prof. Dr. George Koulaouzidis

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Guest Editor
Department of Biochemical Sciences, Pomeranian Medical University in Szczecin, Szczecin, Poland
Interests: telemedicine; artificial intelligence; heart failure; coronary artery calcification; cardiac imaging; long QT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cardiac imaging is a cornerstone of modern cardiology, providing essential insights into the structure and function of the heart. Recent advancements in this field have revolutionized diagnostic and therapeutic strategies, enhancing our ability to manage cardiovascular diseases.

Echocardiography, the most widely used cardiac imaging modality, has seen significant advancements. Speckle-tracking echocardiography (STE) is one such innovation, providing detailed information on myocardial deformation and strain. This technique enhances our ability to detect subclinical myocardial dysfunction, which is particularly useful in patients undergoing chemotherapy or those with early-stage heart failure. Three-dimensional echocardiography (3DE) has also become increasingly prevalent, offering more accurate volumetric measurements and better visualization of complex cardiac structures. This is particularly beneficial in evaluating valvular heart diseases and congenital heart defects, where precise anatomical details are crucial for surgical planning.

Cardiac magnetic resonance imaging (CMR) has emerged as the gold standard for non-invasive myocardial tissue characterization. Recent developments in CMR include T1 and T2 mapping techniques, which allow for the quantification of myocardial fibrosis and edema. These techniques provide critical information for diagnosing and managing conditions such as myocarditis, cardiomyopathies, and myocardial infarction. Furthermore, the advent of four-dimensional flow CMR (4D flow CMR) enables comprehensive visualization of blood flow patterns within the heart and great vessels. This is particularly useful for assessing complex congenital heart diseases and understanding hemodynamic changes in various cardiac conditions.

Computed tomography (CT) has long been a key player in cardiac imaging, primarily for coronary artery assessment. The introduction of high-resolution and dual-energy CT has significantly improved image quality and diagnostic accuracy. These advancements allow for better visualization of coronary plaques and characterization of their composition, aiding in the risk stratification of patients with coronary artery disease.

This Special Issue explores some of the most promising new perspectives in cardiac imaging, focusing on technological innovations and their clinical implications.

Prof. Dr. George Koulaouzidis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cardiac imaging
  • echocardiography
  • cardiac magnetic resonance imaging
  • computed tomography

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Published Papers (2 papers)

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Research

11 pages, 879 KiB  
Article
Left Ventricular Longitudinal Strain Detects Ischemic Dysfunction at Rest, Reflecting Significant Coronary Artery Disease
by George Koulaouzidis, Panagiota Kleitsioti, Maria Kalaitzoglou, Christos Tzimos, Dafni Charisopoulou, Panagiotis Theodorou, Ioannis Bostanitis, Adam Tsaousidis, Vasileios Tzalamouras, Pinelopi Giannakopoulou, Aggeliki D. Mavrogianni, Michael Y. Henein and John Zarifis
Diagnostics 2025, 15(9), 1102; https://doi.org/10.3390/diagnostics15091102 - 26 Apr 2025
Viewed by 159
Abstract
Background/Objectives: The role of speckle-tracking echocardiography in the diagnosis of stable coronary artery disease (CAD) remains controversial. The aim of this study was to assess the diagnostic accuracy of global longitudinal strain (GLS) in predicting significant CAD. Methods: In this prospective study, 103 [...] Read more.
Background/Objectives: The role of speckle-tracking echocardiography in the diagnosis of stable coronary artery disease (CAD) remains controversial. The aim of this study was to assess the diagnostic accuracy of global longitudinal strain (GLS) in predicting significant CAD. Methods: In this prospective study, 103 symptomatic patients referred for invasive coronary angiography were enrolled. All patients underwent resting echocardiography with GLS assessment prior to angiography. Exclusion criteria included acute coronary syndrome, known history of CAD, and the presence of left ventricular wall motion abnormalities. Significant CAD was defined as ≥50% stenosis in at least one major epicardial coronary artery. Results: The mean patient age was 63.8 ± 9.3 years, with 78.6% being male. Hypertension was present in 63.1% of patients, dyslipidemia in 77.7%, diabetes mellitus in 22.3%, smoking history in 71.9%, and a family history of premature CAD in 24.3%. Significant CAD was identified in 45.6% (n = 47), while the remaining 54.3% (n = 56) had non-significant or no coronary artery disease. Patients with significant CAD exhibited significantly lower GLS values compared to those without (−15.73 ± 2.64% vs. −17.6 ± 1.85%, p = 0.001). A GLS threshold of >−16.3 predicted significant CAD with 66% sensitivity and 73.2% specificity (AUC = 0.692, p = 0.001). GLS demonstrated diagnostic accuracy in identifying disease in individual coronary territories, with AUCs of 0.754 for the left anterior descending artery (LAD), 0.714 for the left circumflex artery (LCx), and 0.723 for the right coronary artery (RCA). Diagnostic performance improved when GLS was combined across all three territories (AUC = 0.796). Conclusions: Resting myocardial GLS is accurate in detecting ischemic myocardial dysfunction and can accurately predict significant stenosis of the respective coronary branch subtending the segments. Full article
(This article belongs to the Special Issue New Perspectives in Cardiac Imaging)
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24 pages, 7818 KiB  
Article
Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images
by Ahmad Alenezi, Ali Mayya, Mahdi Alajmi, Wegdan Almutairi, Dana Alaradah and Hamad Alhamad
Diagnostics 2024, 14(24), 2865; https://doi.org/10.3390/diagnostics14242865 - 20 Dec 2024
Viewed by 1259
Abstract
Background: Myocardial perfusion imaging (MPI) is a type of single-photon emission computed tomography (SPECT) used to evaluate patients with suspected or confirmed coronary artery disease (CAD). Detection and diagnosis of CAD are complex processes requiring precise and accurate image processing. Proper segmentation is [...] Read more.
Background: Myocardial perfusion imaging (MPI) is a type of single-photon emission computed tomography (SPECT) used to evaluate patients with suspected or confirmed coronary artery disease (CAD). Detection and diagnosis of CAD are complex processes requiring precise and accurate image processing. Proper segmentation is critical for accurate diagnosis, but segmentation issues can pose significant challenges, leading to diagnostic difficulties. Machine learning (ML) algorithms have demonstrated superior performance in addressing segmentation problems. Methods: In this study, a deep learning (DL) algorithm, U-Net, was employed to enhance segmentation accuracy for image segmentation in MPI. Data were collected from 1100 patients who underwent MPI studies at Al-Jahra Hospital between 2015 and 2024. To train the U-Net model, 100 studies were segmented by nuclear medicine (NM) experts to create a ground truth (gold-standard coordinates). The dataset was divided into a training set (n = 100 images) and a validation set (n = 900 images). The performance of the U-Net model was evaluated using multiple cross-validation metrics, including accuracy, precision, intersection over union (IOU), recall, and F1 score. Result: A dataset of 4560 images and corresponding masks was generated. Both holdout and k-fold (k = 5) validation strategies were applied, utilizing cross-entropy and Dice score as evaluation metrics. The best results were achieved with the holdout split and cross-entropy loss function, yielding a test accuracy of 98.9%, a test IOU of 89.6%, and a test Dice coefficient of 94%. The k-fold validation scenario provided a more balanced true positive and false positive rate. The U-Net segmentation results were comparable to those produced by expert nuclear medicine technologists, with no significant difference (p = 0.1). Conclusions: The findings demonstrate that the U-Net model effectively addresses some segmentation challenges in MPI, facilitating improved diagnosis and analysis of mega data. Full article
(This article belongs to the Special Issue New Perspectives in Cardiac Imaging)
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