دورية أكاديمية
أعرض في EDS

Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm.

التفاصيل البيبلوغرافية
العنوان: Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm.
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
الوصف
تدمد:2948-2933
DOI:10.1007/s10278-024-01033-w