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Bioengineering
Special Issues
Computed Tomography Techniques and Applications
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Computed Tomography Techniques and Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4107

Special Issue Editors

Dr. Matthias S. May

E-Mail Website
Guest Editor
Imaging Science Institute, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
Interests: computed tomography; intervention; artificial intelligence; dual energy; personalized medicine; imaging biomarkers; photon counting; radiation dose
Special Issues, Collections and Topics in MDPI journals
Dr. Tobias Penzkofer

E-Mail Website
Co-Guest Editor
Department of Radiology, Charité–Universitätsmedizin Berlin, Berlin, Germany
Interests: bioinformatics; medical image processing; clinical radiology
Dr. Andreas Michael Bucher

E-Mail Website
Co-Guest Editor
Department of Radiology, University Hospital Frankfurt Main, Goethe University Frankfurt, Frankfurt am Main, Germany
Interests: machine learning; uroradiology; oncological imaging

Special Issue Information

Dear Colleagues,

Computed tomography is considered the workhorse of thin-slice cross-sectional imaging in clinical routine and produces gigabytes of attenuation data within seconds. Radiologists are reading these data to distinguish pathologies from alterations within the expected variance. Simple quantitative measurements, like distances or attenuation values, often support this subjective assessment. Conversely, most CT datasets contain extensive concealed data that are not yet routinely utilized. For example, radiomics feature extractions are rarely performed in the standard setting because of the additional workload, missing software implementation, abstract interpretation, limited reproducibility, and critical evidence. However, scientific evaluations provide promising results for advanced information assessment, like radio-genomics [1], radio-pathological correlations [2], and clinical outcome prediction [3]. Histogram-based, texture-based, model-based, transform-based, and shape-based analysis are only some basic class examples from the extensive Image Biomarker Standardization Initiative white paper lists [4]. This Special Issue aims to publish all sorts of high-quality efforts to improve input data, image segmentation, data processing, feature extraction, workflow integration, knowledge distribution, quality control, and clinical evaluation. Technical studies, experimental trials, and clinical assessment will be considered.

References

1. Perez-Johnston, R.; Araujo-Filho, J.A.; Connolly, J.G.; Caso, R.; Whiting, K.; Tan, K.S.; Zhou, J.; Gibbs, P.; Rekhtman, N.; Ginsberg, M.S.; et al. CT-based Radiogenomic Analysis of Clinical Stage I Lung Adenocarcinoma with Histopathologic Features and Oncologic Outcomes. Radiology 2022, 303, 664–672, https://doi.org/10.1148/radiol.211582.

2. Cayet, S.; Pasco, J.; Dujardin, F.; Besson, M.; Orain, I.; De Muret, A.; Miquelestorena-Standley, E.; Thiery, J.; Genet, T.; Le Bayon, A.-G. Diagnostic performance of contrast-enhanced CT-scan in sinusoidal obstruction syndrome induced by chemotherapy of colorectal liver metastases: Radio-pathological correlation. Eur. J. Radiol. 2017, 94, 180–190, https://doi.org/10.1016/j.ejrad.2017.06.025.

3. Xu, X.; Zhang, H.L.; Liu, Q.P.; Sun, S.W.; Zhang, J.; Zhu, F.P.; Yang, G.; Yan, X.; Zhang, Y.D.; Liu, X.S. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J. Hepatol. 2019, 70, 1133–1144. https://doi.org/10.1016/j.jhep.2019.02.023

4. Zwanenburg, A.; Leger, S.; Vallieres, M.; Lock, S. Image biomarker standardisation initiative. arXiv 2016. arXiv:1612.07003.

Dr. Matthias S. May
Dr. Tobias Penzkofer
Dr. Andreas Michael Bucher
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • computed tomography
  • imaging biomarker
  • radiomics
  • standardization
  • image quality

Published Papers (4 papers)

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Research

11 pages, 4601 KiB  
Article
Comparison of Virtual Non-Contrast and True Non-Contrast CT Images Obtained by Dual-Layer Spectral CT in COPD Patients
by Manuel Steinhardt, Alexander W. Marka, Sebastian Ziegelmayer, Marcus Makowski, Rickmer Braren, Markus Graf and Joshua Gawlitza
Bioengineering 2024, 11(4), 301; https://doi.org/10.3390/bioengineering11040301 - 22 Mar 2024
Viewed by 797
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of [...] Read more.
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of −950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (−950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete. Full article
(This article belongs to the Special Issue Computed Tomography Techniques and Applications)
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14 pages, 1789 KiB  
Article
Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19—A RACOON Project
by Andrea Steuwe, Benedikt Kamp, Saif Afat, Alena Akinina, Schekeb Aludin, Elif Gülsah Bas, Josephine Berger, Evelyn Bohrer, Alexander Brose, Susanne Martina Büttner, Constantin Ehrengut, Mirjam Gerwing, Sergio Grosu, Alexander Gussew, Felix Güttler, Andreas Heinrich, Petra Jiraskova, Christopher Kloth, Jonathan Kottlors, Marc-David Kuennemann, Christian Liska, Nora Lubina, Mathias Manzke, Felix G. Meinel, Hans-Jonas Meyer, Andreas Mittermeier, Thorsten Persigehl, Lars-Patrick Schmill, Manuel Steinhardt, The RACOON Study Group, Gerald Antoch and Birte Valentinadd Show full author list remove Hide full author list
Bioengineering 2024, 11(3), 207; https://doi.org/10.3390/bioengineering11030207 - 22 Feb 2024
Viewed by 929
Abstract
CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and [...] Read more.
CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions. Full article
(This article belongs to the Special Issue Computed Tomography Techniques and Applications)
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13 pages, 600 KiB  
Article
Lightweight Techniques to Improve Generalization and Robustness of U-Net Based Networks for Pulmonary Lobe Segmentation
by Armin A. Dadras, Achref Jaziri, Eric Frodl, Thomas J. Vogl, Julia Dietz and Andreas M. Bucher
Bioengineering 2024, 11(1), 21; https://doi.org/10.3390/bioengineering11010021 - 25 Dec 2023
Cited by 1 | Viewed by 986
Abstract
Lung lobe segmentation in chest CT is relevant to a wide range of clinical applications. However, existing segmentation pipelines often exhibit vulnerabilities and performance degradations when applied to external datasets. This is usually attributed to the size of the available dataset or model. [...] Read more.
Lung lobe segmentation in chest CT is relevant to a wide range of clinical applications. However, existing segmentation pipelines often exhibit vulnerabilities and performance degradations when applied to external datasets. This is usually attributed to the size of the available dataset or model. We show that it is possible to enhance generalizability without huge resources by carefully curating the dataset and combining machine learning with medical expertise. Multiple machine learning techniques (self-supervision (SSL), attention (A), and data augmentation (DA)) are used to train a fast and fully-automated lung lobe segmentation model based on 2D U-Net. Our study involved evaluating these techniques on a diverse dataset collected under the RACOON project, encompassing 100 CT chest scans from patients with bacterial, viral, or SARS-CoV2 infections. We compare our model to a baseline U-Net trained on the same dataset. Our approach significantly improved segmentation accuracy (Dice score of 92.8% vs. 82.3%, p < 0.001). Moreover, our model achieved state-of-the-art performance (Dice score of 92.8% vs. 90.8% for the literature’s state-of-the-art, p = 0.102) with reduced training examples (69 vs. 231 CT Scans). Among the techniques, data augmentation with expert knowledge displayed the most significant impact, enhancing the Dice score by +0.056. Notably, these enhancements are not limited to lobe segmentation but can be seamlessly integrated into various medical imaging segmentation tasks, demonstrating their versatility and potential for broader applications. Full article
(This article belongs to the Special Issue Computed Tomography Techniques and Applications)
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12 pages, 2125 KiB  
Article
Diagnostic Accuracy and Performance Analysis of a Scanner-Integrated Artificial Intelligence Model for the Detection of Intracranial Hemorrhages in a Traumatology Emergency Department
by Jonas Kiefer, Markus Kopp, Theresa Ruettinger, Rafael Heiss, Wolfgang Wuest, Patrick Amarteifio, Armin Stroebel, Michael Uder and Matthias Stefan May
Bioengineering 2023, 10(12), 1362; https://doi.org/10.3390/bioengineering10121362 - 28 Nov 2023
Viewed by 1013
Abstract
Intracranial hemorrhages require an immediate diagnosis to optimize patient management and outcomes, and CT is the modality of choice in the emergency setting. We aimed to evaluate the performance of the first scanner-integrated artificial intelligence algorithm to detect brain hemorrhages in a routine [...] Read more.
Intracranial hemorrhages require an immediate diagnosis to optimize patient management and outcomes, and CT is the modality of choice in the emergency setting. We aimed to evaluate the performance of the first scanner-integrated artificial intelligence algorithm to detect brain hemorrhages in a routine clinical setting. This retrospective study includes 435 consecutive non-contrast head CT scans. Automatic brain hemorrhage detection was calculated as a separate reconstruction job in all cases. The radiological report (RR) was always conducted by a radiology resident and finalized by a senior radiologist. Additionally, a team of two radiologists reviewed the datasets retrospectively, taking additional information like the clinical record, course, and final diagnosis into account. This consensus reading served as a reference. Statistics were carried out for diagnostic accuracy. Brain hemorrhage detection was executed successfully in 432/435 (99%) of patient cases. The AI algorithm and reference standard were consistent in 392 (90.7%) cases. One false-negative case was identified within the 52 positive cases. However, 39 positive detections turned out to be false positives. The diagnostic performance was calculated as a sensitivity of 98.1%, specificity of 89.7%, positive predictive value of 56.7%, and negative predictive value (NPV) of 99.7%. The execution of scanner-integrated AI detection of brain hemorrhages is feasible and robust. The diagnostic accuracy has a high specificity and a very high negative predictive value and sensitivity. However, many false-positive findings resulted in a relatively moderate positive predictive value. Full article
(This article belongs to the Special Issue Computed Tomography Techniques and Applications)
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