Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies

Autor: Pil-Hyun Jeon, Sang-Hyun Jeon, Donghee Ko, Giyong An, Hackjoon Shim, Chuluunbaatar Otgonbaatar, Kihong Son, Daehong Kim, Sung Min Ko, Myung-Ae Chung
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Diagnostics, Vol 13, Iss 11, p 1862 (2023)
Druh dokumentu: article
ISSN: 2075-4418
DOI: 10.3390/diagnostics13111862
Popis: Background: In coronary computed tomography angiography (CCTA), the main issue of image quality is noise in obese patients, blooming artifacts due to calcium and stents, high-risk coronary plaques, and radiation exposure to patients. Objective: To compare the CCTA image quality of deep learning-based reconstruction (DLR) with that of filtered back projection (FBP) and iterative reconstruction (IR). Methods: This was a phantom study of 90 patients who underwent CCTA. CCTA images were acquired using FBP, IR, and DLR. In the phantom study, the aortic root and the left main coronary artery in the chest phantom were simulated using a needleless syringe. The patients were classified into three groups according to their body mass index. Noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured for image quantification. A subjective analysis was also performed for FBP, IR, and DLR. Results: According to the phantom study, DLR reduced noise by 59.8% compared to FBP and increased SNR and CNR by 121.4% and 123.6%, respectively. In a patient study, DLR reduced noise compared to FBP and IR. Furthermore, DLR increased the SNR and CNR more than FBP and IR. In terms of subjective scores, DLR was higher than FBP and IR. Conclusion: In both phantom and patient studies, DLR effectively reduced image noise and improved SNR and CNR. Therefore, the DLR may be useful for CCTA examinations.
Databáze: Directory of Open Access Journals
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