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Diagnostics
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Updates in Cardiothoracic Imaging
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Updates in Cardiothoracic Imaging

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 10510

Special Issue Editor

Dr. Hamid Chalian

E-Mail Website
Guest Editor
Division of Cardiothoracic Imaging, Department of Radiology, University of Washington, Seattle, WA 98105, USA
Interests: cardiac; vascular; thoracic; imaging; MRI; CT

Special Issue Information

Dear Colleagues,

Over the past decade, we have witnessed rapid development of a variety of novel technologies in the field of cardiothoracic imaging. Emerging artificial intelligence technologies are also showing promising results in this field, and some have already been added to our daily clinical practice. Screening and follow-up guidelines for the ideal management of patients with cardiac and lung diseases have undergone several revisions. All these necessitate updating our knowledge to better serve patients.

We are pleased to invite you to contribute to this Special Issue to cover these updates. This Special Issue seeks submissions to expand the understanding and highlight the clinical importance of the emerging or changing technologies, guidelines, and expert recommendations in the field of cardiothoracic imaging.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Artificial intelligence in cardiothoracic imaging;
  • Novel techniques for imaging of the chest;
  • Vascular imaging;
  • Cardiac MRI and CT imaging;
  • Update on guidelines related to cardiac and thoracic imaging;
  • Lung cancer screening;
  • Health care disparities in cardiothoracic radiology.

I look forward to receiving your contributions.

Dr. Hamid Chalian
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
  • chest
  • thoracic
  • vascular
  • MRI
  • CT
  • imaging

Published Papers (5 papers)

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Research

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14 pages, 4203 KiB  
Article
Age Matters: A Comparative Study of RF Heating of Epicardial and Endocardial Electronic Devices in Pediatric and Adult Phantoms during Cardiothoracic MRI
by Fuchang Jiang, Kaylee R. Henry, Bhumi Bhusal, Pia Sanpitak, Gregory Webster, Andrada Popescu, Christina Laternser, Daniel Kim and Laleh Golestanirad
Diagnostics 2023, 13(17), 2847; https://doi.org/10.3390/diagnostics13172847 - 2 Sep 2023
Viewed by 1063
Abstract
This study focused on the potential risks of radiofrequency-induced heating of cardiac implantable electronic devices (CIEDs) in children and adults with epicardial and endocardial leads of varying lengths during cardiothoracic MRI scans. Infants and young children are the primary recipients of epicardial CIEDs, [...] Read more.
This study focused on the potential risks of radiofrequency-induced heating of cardiac implantable electronic devices (CIEDs) in children and adults with epicardial and endocardial leads of varying lengths during cardiothoracic MRI scans. Infants and young children are the primary recipients of epicardial CIEDs, though the devices have not been approved as MR conditional by the FDA due to limited data, leading to pediatric hospitals either refusing the MRI service to most pediatric CIED patients or adopting a scan-all strategy based on results from adult studies. The study argues that risk–benefit decisions should be made on an individual basis. We used 120 clinically relevant epicardial and endocardial device configurations in adult and pediatric anthropomorphic phantoms to determine the temperature rise during RF exposure at 1.5 T. The results showed that there was significantly higher RF heating of epicardial leads than endocardial leads in the pediatric phantom, but not in the adult phantom. Additionally, body size and lead length significantly affected RF heating, with RF heating up to 12 °C observed in models based on younger children with short epicardial leads. The study provides evidence-based knowledge on RF-induced heating of CIEDs and highlights the importance of making individual risk–benefit decisions when assessing the potential risks of MRI scans in pediatric CIED patients. Full article
(This article belongs to the Special Issue Updates in Cardiothoracic Imaging)
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9 pages, 4820 KiB  
Article
Pleuroparenchymal Fibroelastosis-like Lesions in Clinical Practice: A Rare Entity? Review of a Radiological Database
by Francesco Gentili, Vito Di Martino, Marta Forestieri, Francesco Mazzei, Susanna Guerrini, Elena Bargagli, Antonietta Gerardina Sisinni, Luca Volterrani and Maria Antonietta Mazzei
Diagnostics 2023, 13(9), 1627; https://doi.org/10.3390/diagnostics13091627 - 4 May 2023
Viewed by 1805
Abstract
Background: Pleuroparenchymal Fibroelastosis (PPFE) is a rare disease that consists of elastofibrosis that involves the pleura and subpleural lung parenchyma; it is an unusual pulmonary disease with unique clinical, radiological and pathological characteristics. According to recent studies, PPFE may not be a definite [...] Read more.
Background: Pleuroparenchymal Fibroelastosis (PPFE) is a rare disease that consists of elastofibrosis that involves the pleura and subpleural lung parenchyma; it is an unusual pulmonary disease with unique clinical, radiological and pathological characteristics. According to recent studies, PPFE may not be a definite disease but a form of chronic lung injury. The aim of this retrospective study is to determine the incidence and to evaluate the distribution, severity and progression of this radiological entity on high-resolution CT (HRCT) exams of the chest, performed in routine clinical practice. In total, 1514 HRCT exams performed in the period January 2016–June 2018 were analyzed. For each exam, the presence of PPFE was evaluated and a quantitative score was assigned (from 0 to 7 points, based on the maximum depth of fibrotic involvement of the parenchyma). When available, two exams with a time interval of at least 6 months were compared for each patient in order to evaluate progression (defined as the increase in the disease score). Patients were divided into different groups according to exposure and their associated diseases. Statistical analysis was performed by using the Wilcoxon test and Kruskal–Wallis test. Results: PPFE was detected in 174 out of 1514 patients (11.6%), with a mean score of 6.1 ± 3.9 (range 1–14). In 106 out of 174 patients (60.9%), a previous CT scan was available and an evolution of PPFE was detected in 19 of these (11.5%). Among these 19 patients with worsening PPFE, 4 had isolated PPFE that was associated with chronic exposure or connective tissue disorders, and the other 15 had an associated lung disease and/or a chronic exposure. In this group, it was found that the ventral segments of the upper lobes, fissures and apical segments of the lower lobes had a greater statistically significant involvement in the progression of the disease compared to the non-progressive group. In 16 of 174 patients (9.2%, 7 of which belonged to the radiological progression group) a biopsy through video-assisted thoracoscopic surgery or apicoectomy confirmed PPFE. Conclusion: PPFE-like lesions are not uncommon on HRCT exams in routine clinical practice, and are frequently found in patients with different forms of chronic lung injury. Further studies are necessary to explain why the disease progresses in some cases, while in most, it remains stationary over time. Full article
(This article belongs to the Special Issue Updates in Cardiothoracic Imaging)
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14 pages, 4869 KiB  
Article
Handheld Echocardiography Measurements Concordance and Findings Agreement: An Exploratory Study
by Mariam Haji-Hassan, Bogdan Duțu and Sorana D. Bolboacă
Diagnostics 2023, 13(5), 853; https://doi.org/10.3390/diagnostics13050853 - 23 Feb 2023
Cited by 2 | Viewed by 1765
Abstract
The professional association has already developed guidelines on the appropriate use of handheld ultrasound devices, especially in an emergency setting. Handheld ultrasound devices are seen as the ‘stethoscope of the future’ to assist in physical examination. Our exploratory study evaluated whether the measurements [...] Read more.
The professional association has already developed guidelines on the appropriate use of handheld ultrasound devices, especially in an emergency setting. Handheld ultrasound devices are seen as the ‘stethoscope of the future’ to assist in physical examination. Our exploratory study evaluated whether the measurements of cardiovascular structures and the agreement in identifying aortic, mitral, and tricuspid valve pathology made by a resident with a handheld device (HH, Kosmos Torso-One) reach the results reported by an experienced examiner who used a high-end device (STD). Patients referred for cardiology examination in a single center from June to August 2022 were eligible for the study. Patients who agreed to participate underwent two heart ultrasound examinations scanned by the same two operators. A cardiology resident performed the first examination with a HH ultrasound device, and an experienced examiner performed the second examination with an STD device. Forty-three consecutive patients were eligible, and forty-two were included in the study. One obese patient was excluded because none of the examiners succeeded in performing the heart examination. The measurements obtained with HH were generally higher than those obtained with STD, with the highest mean difference of 0.4 mm, but without significant differences (all 95% confidence intervals of the differences contain the value of 0). For valvular disease, the lowest agreement was observed for mitral valve regurgitation (26/42, with a Kappa concordance coefficient of 0.5321), which was missed in almost half of the patients with mild regurgitation and underestimated in half of the patients with moderate mitral regurgitation. The measurements performed by the resident with the handheld Kosmos Torso-One device showed high concordance with those conducted by the experienced examiner with a larger high-end ultrasound device. The learning curve of the resident could explain the limited performance in identifying valvular pathologies between examiners. Full article
(This article belongs to the Special Issue Updates in Cardiothoracic Imaging)
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Review

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15 pages, 2058 KiB  
Review
Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review
by Bardia Khosravi, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Sanaz Vahdati, Elham Mahmoudi, Hamid Chalian and Bradley J. Erickson
Diagnostics 2022, 12(10), 2512; https://doi.org/10.3390/diagnostics12102512 - 17 Oct 2022
Cited by 2 | Viewed by 2217
Abstract
Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have [...] Read more.
Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption. Full article
(This article belongs to the Special Issue Updates in Cardiothoracic Imaging)
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12 pages, 1428 KiB  
Review
Relationship between Coronary Arterial Geometry and the Presence and Extend of Atherosclerotic Plaque Burden: A Review Discussing Methodology and Findings in the Era of Cardiac Computed Tomography Angiography
by Georgios Rampidis, Vasileios Rafailidis, Konstantinos Kouskouras, Andjoli Davidhi, Angeliki Papachristodoulou, Athanasios Samaras, George Giannakoulas, Antonios Ziakas, Panagiotis Prassopoulos and Haralambos Karvounis
Diagnostics 2022, 12(9), 2178; https://doi.org/10.3390/diagnostics12092178 - 9 Sep 2022
Cited by 4 | Viewed by 2651
Abstract
Coronary artery disease (CAD) represents a modern pandemic associated with significant morbidity and mortality. The multi-faceted pathogenesis of this entity has long been investigated, highlighting the contribution of systemic factors such as hyperlipidemia and hypertension. Nevertheless, recent research has drawn attention to the [...] Read more.
Coronary artery disease (CAD) represents a modern pandemic associated with significant morbidity and mortality. The multi-faceted pathogenesis of this entity has long been investigated, highlighting the contribution of systemic factors such as hyperlipidemia and hypertension. Nevertheless, recent research has drawn attention to the importance of geometrical features of coronary vasculature on the complexity and vulnerability of coronary atherosclerosis. Various parameters have been investigated so far, including vessel-length, coronary artery volume index, cross-sectional area, curvature, and tortuosity, using primarily invasive coronary angiography (ICA) and recently non-invasive cardiac computed tomography angiography (CCTA). It is clear that there is correlation between geometrical parameters and both the haemodynamic alterations augmenting the atherosclerosis-prone environment and the extent of plaque burden. The purpose of this review is to discuss the currently available literature regarding this issue and propose a potential non-invasive imaging biomarker, the geometric risk score, which could be of importance to allow the early detection of individuals at increased risk of developing CAD. Full article
(This article belongs to the Special Issue Updates in Cardiothoracic Imaging)
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