دورية أكاديمية
Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm.
العنوان: | Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm. |
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المؤلفون: | Dreesen HJH; Department of Radiology, University Regensburg, Franz-Josef-Strauss Allee 11, 93053, Regensburg, Germany. Hendrik.dreesen@web.de.; Department of Radiology, Neuroradiology and Nuclear Medicine, Klinikum Nürnberg, Paracelsus Medical University, Nuremberg, Germany. Hendrik.dreesen@web.de., Stroszczynski C; Department of Radiology, University Regensburg, Franz-Josef-Strauss Allee 11, 93053, Regensburg, Germany., Lell MM; Department of Radiology, Neuroradiology and Nuclear Medicine, Klinikum Nürnberg, Paracelsus Medical University, Nuremberg, Germany. |
المصدر: | Journal of imaging informatics in medicine [J Imaging Inform Med] 2024 Aug; Vol. 37 (4), pp. 1548-1556. Date of Electronic Publication: 2024 Mar 04. |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: [Cham, Switzerland] : Springer Nature, [2024]- |
مواضيع طبية MeSH: | Deep Learning* , Computed Tomography Angiography*/methods , Algorithms* , Coronary Angiography*/methods, Humans ; Female ; Male ; Middle Aged ; Aged ; Artifacts ; Image Processing, Computer-Assisted/methods ; Multidetector Computed Tomography/methods ; Coronary Vessels/diagnostic imaging |
مستخلص: | Coronary computed tomography angiography (CCTA) is an essential part of the diagnosis of chronic coronary syndrome (CCS) in patients with low-to-intermediate pre-test probability. The minimum technical requirement is 64-row multidetector CT (64-MDCT), which is still frequently used, although it is prone to motion artifacts because of its limited temporal resolution and z-coverage. In this study, we evaluate the potential of a deep-learning-based motion correction algorithm (MCA) to eliminate these motion artifacts. 124 64-MDCT-acquired CCTA examinations with at least minor motion artifacts were included. Images were reconstructed using a conventional reconstruction algorithm (CA) and a MCA. Image quality (IQ), according to a 5-point Likert score, was evaluated per-segment, per-artery, and per-patient and was correlated with potentially disturbing factors (heart rate (HR), intra-cycle HR changes, BMI, age, and sex). Comparison was done by Wilcoxon-Signed-Rank test, and correlation by Spearman's Rho. Per-patient, insufficient IQ decreased by 5.26%, and sufficient IQ increased by 9.66% with MCA. Per-artery, insufficient IQ of the right coronary artery (RCA) decreased by 18.18%, and sufficient IQ increased by 27.27%. Per-segment, insufficient IQ in segments 1 and 2 decreased by 11.51% and 24.78%, respectively, and sufficient IQ increased by 10.62% and 18.58%, respectively. Total artifacts per-artery decreased in the RCA from 3.11 ± 1.65 to 2.26 ± 1.52. HR dependence of RCA IQ decreased to intermediate correlation in images with MCA reconstruction. The applied MCA improves the IQ of 64-MDCT-acquired images and reduces the influence of HR on IQ, increasing 64-MDCT validity in the diagnosis of CCS. (© 2024. The Author(s).) |
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فهرسة مساهمة: | Keywords: 64-Detector row computed tomography; Coronary computed tomography angiography; Deep learning-based algorithm; Motion artifact reduction; Motion correction algorithm; Single-source computed tomography |
تواريخ الأحداث: | Date Created: 20240304 Date Completed: 20240806 Latest Revision: 20240808 |
رمز التحديث: | 20240808 |
مُعرف محوري في PubMed: | PMC11300758 |
DOI: | 10.1007/s10278-024-01033-w |
PMID: | 38438697 |
قاعدة البيانات: | MEDLINE |
تدمد: | 2948-2933 |
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DOI: | 10.1007/s10278-024-01033-w |