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Cardiothoracic Imaging: Diagnostics and Modern Techniques
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Cardiothoracic Imaging: Diagnostics and Modern Techniques

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 17568

Special Issue Editors

Prof. Dr. Edyta Szurowska

E-Mail Website
Guest Editor
2nd Department of Radiology, Faculty of Health Sciences with the Institute of Maritime and Tropical Medicine, Medical University of Gdansk, 80-214 Gdansk, Poland
Interests: neuroradiology (CT and MR diagnosis of CNS diseases—hypertension and changes in the brain; brain and spinal cord tumors); chronic liver diseases; liver tumors; pancreatic tumors; inflammatory bowel disease; inflammatory diseases of the digestive system; oncological imaging (e.g., oncological uroradiology); spiral computed tomography (CT) and Magnetic Resonance Imaging (MRI); diagnosis of parotid gland tumors; lung-cancer diagnosis; osteoarticular system; pediatric radiology; imaging of the heart and large vessels; use of mathematical models and methods of computer image processing in medicine; radiological–pathological correlations; virtual autopsy
Special Issues, Collections and Topics in MDPI journals
Dr. Joanna Pieńkowska

E-Mail Website
Co-Guest Editor
2nd Department of Radiology, Medical University of Gdansk, Gdansk, Poland
Interests: clinical significance of modern imaging methods (CT, MRI) in selected diffuse pancreatic diseases; clinical implications of ectopic adipose tissue accumulation in individual organs; tumors and inflammation of the pancreas and intestines; perfusion CT in patients with insulin-dependent diabetes; imaging of the heart and large vessels; cardiological diagnostics using CT and MRI; oncological diagnostics; diagnosis of diseases of the genitourinary system; lung cancer
Special Issues, Collections and Topics in MDPI journals
Dr. Joanna Bidzińska

E-Mail Website
Co-Guest Editor
2nd Department of Radiology, Medical University of Gdansk, Gdansk, Poland
Interests: oncology; chest imaging; lung cancer screening; drug development; ageing; neurodegenerative diseases; AI in medicine; radiomics; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade, imaging techniques have become increasingly complex, providing greater opportunities for disease understanding and enabling appropriate diagnoses to be made quickly and with greater precision. Cardiothoracic imaging using computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, and positron emission tomography (PET) plays a key role in the diagnosis of heart, great vessels, and lung diseases.

The therapeutic decision in patients with cardiovascular and pulmonary problems is often made in multidisciplinary teams, in which radiological evaluation plays an integral part. This Special Issue will focus on the role of a radiologist in diagnostic and therapeutic teams, and the importance of imaging studies in a comprehensive approach to treatment and follow-up. Current guidelines for the use of imaging studies in specific clinical situations and disease entities will be discussed, and the benefits of using new study protocols and sequences will be demonstrated, with examples provided. We will discuss both ischemic and non-ischemic heart disease and advances in cardiac imaging.

Regarding chest imaging, we will focus on lung-cancer screening (LCS) using low-dose CT and the directions of its development, such as the implementation of artificial intelligence (AI), including the use of machine learning (ML).

The main topics of the Special Issue include:

  1. How to accurately visualize cardiac lesions.
  2. How to improve the LCS program.
  3. How to cooperate in joint multidisciplinary teams.

Prof. Dr. Edyta Szurowska
Dr. Joanna Pieńkowska
Dr. Joanna Bidzińska
Guest Editors

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
  • chest imaging
  • diagnostics
  • MRI
  • CT
  • AI
  • lung cancer screening
  • heart disease
  • imaging techniques
  • COVID-19

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Published Papers (5 papers)

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Research

12 pages, 1308 KiB  
Article
Computed Tomography-Based Coronary Artery Calcium Score Calculation at a Reduced Tube Voltage Utilizing Iterative Reconstruction and Threshold Modification Techniques: A Feasibility Study
by Shirin Habibi, Mohammad Akbarnejad, Nahid Rezaeian, Alireza Salmanipour, Ali Mohammadzadeh, Kiara Rezaei-Kalantari, Hamid Chalian and Sanaz Asadian
Diagnostics 2023, 13(21), 3315; https://doi.org/10.3390/diagnostics13213315 - 26 Oct 2023
Viewed by 1383
Abstract
Background: The coronary artery calcium score (CACS) indicates cardiovascular health. A concern in this regard is the ionizing radiation from computed tomography (CT). Recent studies have tried to introduce low-dose CT techniques to assess CACS. We aimed to investigate the accuracy of iterative [...] Read more.
Background: The coronary artery calcium score (CACS) indicates cardiovascular health. A concern in this regard is the ionizing radiation from computed tomography (CT). Recent studies have tried to introduce low-dose CT techniques to assess CACS. We aimed to investigate the accuracy of iterative reconstruction (IR) and threshold modification while applying low tube voltage in coronary artery calcium imaging. Methods: The study population consisted of 107 patients. Each subject underwent an electrocardiogram-gated CT twice, once with a standard voltage of 120 kVp and then a reduced voltage of 80 kVp. The standard filtered back projection (FBP) reconstruction was applied in both voltages. Considering Hounsfield unit (HU) thresholds other than 130 (150, 170, and 190), CACS was calculated using the FBP-reconstructed 80 kVp images. Moreover, the 80 kVp images were reconstructed utilizing IR at different strength levels. CACS was measured in each set of images. The intraclass correlation coefficient (ICC) was used to compare the CACSs. Results: A 64% reduction in the effective dose was observed in the 80 kVp protocol compared to the 120 kVp protocol. Excellent agreement existed between CACS at high-level (strength level = 5) IR in low-kVp images and the standard CACS protocol in scores ≥ 11 (ICC > 0.9 and p < 0.05). Increasing the threshold density to 190 HU in FBP-reconstructed low-kVp images yielded excellent agreement with the standard protocol in scores ≥ 11 (ICC > 0.9 and p < 0.05) and good agreement in score zero (ICC = 0.84 and p = 0.02). Conclusions: The modification of the density threshold and IR provides an accurate calculation of CACS in low-voltage CT with the potential to decrease patient radiation exposure. Full article
(This article belongs to the Special Issue Cardiothoracic Imaging: Diagnostics and Modern Techniques)
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20 pages, 3943 KiB  
Article
A CAD System for Lung Cancer Detection Using Hybrid Deep Learning Techniques
by Ahmed A. Alsheikhy, Yahia Said, Tawfeeq Shawly, A. Khuzaim Alzahrani and Husam Lahza
Diagnostics 2023, 13(6), 1174; https://doi.org/10.3390/diagnostics13061174 - 19 Mar 2023
Cited by 15 | Viewed by 4184
Abstract
Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most [...] Read more.
Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models. Full article
(This article belongs to the Special Issue Cardiothoracic Imaging: Diagnostics and Modern Techniques)
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12 pages, 2701 KiB  
Article
Evaluating Cardiac Lateralization by MRI to Simplify Estimation of Cardiopulmonary Impairment in Pectus Excavatum
by Tariq Abu-Tair, Salmai Turial, Ines Willershausen, Muhannad Alkassar, Gundula Staatz and Christoph Kampmann
Diagnostics 2023, 13(5), 844; https://doi.org/10.3390/diagnostics13050844 - 23 Feb 2023
Viewed by 1698
Abstract
Background: The severity of pectus excavatum is classified by the Haller Index (HI) and/or Correction Index (CI). These indices measure only the depth of the defect and, therefore, impede a precise estimation of the actual cardiopulmonary impairment. We aimed to evaluate the MRI-derived [...] Read more.
Background: The severity of pectus excavatum is classified by the Haller Index (HI) and/or Correction Index (CI). These indices measure only the depth of the defect and, therefore, impede a precise estimation of the actual cardiopulmonary impairment. We aimed to evaluate the MRI-derived cardiac lateralization to improve the estimation of cardiopulmonary impairment in Pectus excavatum in connection with the Haller and Correction Indices. Methods: This retrospective cohort study included a total of 113 patients (mean age = 19.03 ± 7.8) with pectus excavatum, whose diagnosis was verified on cross-sectional MRI images using the HI and CI. For the development of an improved HI and CI index, the patients underwent cardiopulmonary exercise testing to assess the influence of the right ventricle’s position on cardiopulmonary impairment. The indexed lateral position of the pulmonary valve was utilized as a surrogate parameter for right ventricle localization. Results: In patients with PE, the heart’s lateralization significantly correlated with the severity of pectus excavatum (p ≤ 0.001). When modifying HI and CI for the individual’s pulmonary valve position, those indices are present with greater sensitivity and specificity regarding the maximum oxygen-pulse as a pathophysiological correlate of reduced cardiac function (χ2 10.986 and 15.862, respectively). Conclusion: The indexed lateral deviation of the pulmonary valve seems to be a valuable cofactor for HI and CI, allowing for an improved description of cardiopulmonary impairment in PE patients. Full article
(This article belongs to the Special Issue Cardiothoracic Imaging: Diagnostics and Modern Techniques)
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15 pages, 1293 KiB  
Article
Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures
by Yahia Said, Ahmed A. Alsheikhy, Tawfeeq Shawly and Husam Lahza
Diagnostics 2023, 13(3), 546; https://doi.org/10.3390/diagnostics13030546 - 2 Feb 2023
Cited by 40 | Viewed by 7205
Abstract
Lung cancer presents one of the leading causes of mortalities for people around the world. Lung image analysis and segmentation are one of the primary steps used for early diagnosis of cancer. Handcrafted medical imaging segmentation presents a very time-consuming task for radiation [...] Read more.
Lung cancer presents one of the leading causes of mortalities for people around the world. Lung image analysis and segmentation are one of the primary steps used for early diagnosis of cancer. Handcrafted medical imaging segmentation presents a very time-consuming task for radiation oncologists. To address this problem, we propose in this work to develop a full and entire system used for early diagnosis of lung cancer in CT scan imaging. The proposed lung cancer diagnosis system is composed of two main parts: the first part is used for segmentation developed on top of the UNETR network, and the second part is a classification part used to classify the output segmentation part, either benign or malignant, developed on top of the self-supervised network. The proposed system presents a powerful tool for early diagnosing and combatting lung cancer using 3D-input CT scan data. Extensive experiments have been performed to contribute to better segmentation and classification results. Training and testing experiments have been performed using the Decathlon dataset. Experimental results have been conducted to new state-of-the-art performances: segmentation accuracy of 97.83%, and 98.77% as classification accuracy. The proposed system presents a new powerful tool to use for early diagnosing and combatting lung cancer using 3D-input CT scan data. Full article
(This article belongs to the Special Issue Cardiothoracic Imaging: Diagnostics and Modern Techniques)
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8 pages, 1842 KiB  
Article
Assessment of Calcium Score Cutoff Point for Clinically Significant Aortic Stenosis on Lung Cancer Screening Program Low-Dose Computed Tomography—A Cross-Sectional Analysis
by Kaja Klein-Awerjanow, Witold Rzyman, Robert Dziedzic, Jadwiga Fijalkowska, Piotr Spychalski, Edyta Szurowska and Marcin Fijalkowski
Diagnostics 2023, 13(2), 246; https://doi.org/10.3390/diagnostics13020246 - 9 Jan 2023
Cited by 1 | Viewed by 2376
Abstract
Low-dose computed tomography (LDCT) is predominantly applied in lung cancer screening programs. Tobacco smoking is the main risk factor for developing lung cancer but is also common for cardiovascular diseases, including aortic stenosis (AS). Consequently, an increased prevalence of cardiovascular diseases is expected [...] Read more.
Low-dose computed tomography (LDCT) is predominantly applied in lung cancer screening programs. Tobacco smoking is the main risk factor for developing lung cancer but is also common for cardiovascular diseases, including aortic stenosis (AS). Consequently, an increased prevalence of cardiovascular diseases is expected in lung cancer screenees. Therefore, initial aortic valve calcification evaluation should be additionally performed on LDCT. The aim of this study was to estimate a calcium score (CS) cutoff point for clinically significant AS diagnosis based on LDCT, confirmed by echocardiographic examination. The study included 6631 heavy smokers who participated in a lung cancer screening program (MOLTEST BIS). LDCTs were performed on all individuals and were additionally assessed for aortic valve calcification with the use of CS according to the Agatston method. Patients with CS ≥ 900 were referred for echocardiography to confirm the diagnosis of AS and to evaluate its severity. Of 6631 individuals, 54 met the inclusion criteria and underwent echocardiography for confirmation and assessment of AS. Based on that data, receiver operating characteristic (ROC) curves of CS were plotted, and cutoff points for clinically significant AS diagnosis were established: A CS of 1758 for at least moderate AS had 85.71% (CI 65.36–95.02%) sensitivity and 75.76% (CI 58.98–87.17%) specificity; a CS of 2665 for severe AS had 87.5% (CI 73.89–94.54%) sensitivity and 76.92% (CI 49.74–91.82%) specificity. This is the first study to assess possible CS cutoff points for diagnosing clinically significant AS detected by LDCT in lung cancer screening participants. LDCT with CS assessment could enable early detection of patients with clinically significant AS and therefore identify patients who require appropriate treatment. Full article
(This article belongs to the Special Issue Cardiothoracic Imaging: Diagnostics and Modern Techniques)
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