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Diagnostics
Special Issues
Diagnosis of Cardio-Thoracic Diseases
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Diagnosis of Cardio-Thoracic Diseases

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 2024 | Viewed by 1691

Special Issue Editor

Dr. Tilman Emrich

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Guest Editor
Department of Diagnostic and Interventional Radiology, University Medical Center of Johannes Gutenberg-University, 55131 Mainz, Germany
Interests: cardiac imaging, magnetic resonance imaging, cardiothoracic CT; spectral imaging

Special Issue Information

Dear Colleagues,

Cardiothoracic diseases are a leading cause of death worldwide and their early and accurate diagnosis is crucial for effective treatment and management. Non-invasive imaging has revolutionized the way cardiothoracic diseases are diagnosed and evaluated. The use of imaging techniques such as echocardiography, computed tomography (CT), and magnetic resonance imaging (MRI) has greatly improved the accuracy and speed of diagnosis, and therefore patient management and outcomes.

The articles in this Special Issue will provide valuable insights for clinicians, researchers, and technicians involved in the field of cardiothoracic imaging. It is a pleasure to invite you to contribute to this Special Issue entitled “Diagnosis of Cardio-Thoracic Diseases” with original contributions and review articles focused on imaging to demonstrate the continued progress being made in this area and highlight the importance of utilizing these techniques for improving patient outcomes.

We are looking forward to receiving your submissions.

Dr. Tilman Emrich
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 disease
  • aortic disease
  • lung disease
  • computed tomography
  • magnetic resonance imaging
  • echocardiography
  • quantitative imaging
  • multiparametric imaging

Published Papers (2 papers)

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Research

12 pages, 3068 KiB  
Article
Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT
by Nicola Fink, Basel Yacoub, U. Joseph Schoepf, Emese Zsarnoczay, Daniel Pinos, Milan Vecsey-Nagy, Saikiran Rapaka, Puneet Sharma, Jim O’Doherty, Jens Ricke, Akos Varga-Szemes and Tilman Emrich
Diagnostics 2024, 14(9), 866; https://doi.org/10.3390/diagnostics14090866 - 23 Apr 2024
Viewed by 354
Abstract
This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced [...] Read more.
This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists’ efficiency in clinical practice. Full article
(This article belongs to the Special Issue Diagnosis of Cardio-Thoracic Diseases)
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13 pages, 2488 KiB  
Article
Optimization of the Reconstruction Settings for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lungs
by Dirk Graafen, Moritz C. Halfmann, Tilman Emrich, Yang Yang, Michael Kreuter, Christoph Düber, Roman Kloeckner, Lukas Müller and Tobias Jorg
Diagnostics 2023, 13(23), 3522; https://doi.org/10.3390/diagnostics13233522 - 24 Nov 2023
Viewed by 786
Abstract
Photon-counting detector computed tomography (PCD-CT) yields improved spatial resolution. The combined use of PCD-CT and a modern iterative reconstruction method, known as quantum iterative reconstruction (QIR), has the potential to significantly improve the quality of lung CT images. In this study, we aimed [...] Read more.
Photon-counting detector computed tomography (PCD-CT) yields improved spatial resolution. The combined use of PCD-CT and a modern iterative reconstruction method, known as quantum iterative reconstruction (QIR), has the potential to significantly improve the quality of lung CT images. In this study, we aimed to analyze the impacts of different slice thicknesses and QIR levels on low-dose ultra-high-resolution (UHR) PCD-CT imaging of the lungs. Our study included 51 patients with different lung diseases who underwent unenhanced UHR-PCD-CT scans. Images were reconstructed using three different slice thicknesses (0.2, 0.4, and 1.0 mm) and three QIR levels (2–4). Noise levels were determined in all reconstructions. Three raters evaluated the delineation of anatomical structures and conspicuity of various pulmonary pathologies in the images compared to the clinical reference reconstruction (1.0 mm, QIR-3). The highest QIR level (QIR-4) yielded the best image quality. Reducing the slice thickness to 0.4 mm improved the delineation and conspicuity of pathologies. The 0.2 mm reconstructions exhibited lower image quality due to high image noise. In conclusion, the optimal reconstruction protocol for low-dose UHR-PCD-CT of the lungs includes a slice thickness of 0.4 mm, with the highest QIR level. This optimized protocol might improve the diagnostic accuracy and confidence of lung imaging. Full article
(This article belongs to the Special Issue Diagnosis of Cardio-Thoracic Diseases)
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