Influence of deep learning-based super-resolution reconstruction on Agatston score.
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| Title: | Influence of deep learning-based super-resolution reconstruction on Agatston score. |
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| Authors: | Morikawa, Tomoro1 (AUTHOR), Tanabe, Yuki1 (AUTHOR) yuki.tanabe.0225@gmail.com, Suekuni, Hiroshi1 (AUTHOR), Fukuyama, Naoki1 (AUTHOR), Toshimori, Wataru1 (AUTHOR), Toritani, Hidetaka1 (AUTHOR), Sawada, Shun1 (AUTHOR), Matsuda, Takuya1 (AUTHOR), Nakano, Shota2 (AUTHOR), Kido, Teruhito1 (AUTHOR) |
| Source: | European Radiology. Sep2025, Vol. 35 Issue 9, p5604-5614. 11p. |
| Subjects: | Deep learning, Coronary artery calcification, Image enhancement (Imaging systems), High resolution imaging, Image quality analysis, Computed tomography |
| Abstract: | Objective: To evaluate the impact of deep learning-based super-resolution reconstruction (DLSRR) on image quality and Agatston score. Methods: Consecutive patients who underwent cardiac CT, including unenhanced CT for Agatston scoring, were enrolled. Four types of non-contrast CT images were reconstructed using filtered back projection (FBP) and three strengths of DLSRR. Image quality was assessed by measuring image noise, signal-to-noise ratio (SNR) of the aorta, contrast-to-noise ratio (CNR), and edge rise slope (ERS) of coronary artery calcium (CAC). Agatston score and CAC volume were also measured. These results were compared among the four CT datasets. Patients were categorized into four risk levels based on the Coronary Artery Calcium Data and Reporting System (CAC-DRS), and the concordance rate between FBP and DLSRR classifications was evaluated. Results: For the 111 patients enrolled, DLSRR significantly reduced image noise (p < 0.001) and improved SNR and CNR (p < 0.001), with stronger effects at higher DLSRR strengths (p < 0.01). ERS was significantly enhanced using DLSRR compared with FBP (p < 0.001), whereas there was no significant difference among the three strengths of DLSRR (p = 0.90–0.98). Agatston score and CAC volume were not significantly affected by DLSRR (p = 0.952 and 0.901, respectively). The concordance rate of CAC-DRS classification between FBP and DLSRR was 93%. Conclusion: DLSRR significantly improves image quality by reducing noise and enhancing sharpness without significantly altering Agatston scores or CAC volumes. The concordance rate of CAC-DRS classification with FBP was high, although some reclassifications were observed. Key Points: QuestionThe utility of deep learning-based super-resolution reconstruction (DLSRR) in coronary CT angiography is well known, but its impact on the Agatston score remains unclear. FindingsDLSRR significantly improved image quality without altering the Agatston scores, but some reclassifications of Coronary Artery Calcium Data and Reporting System (CAC-DRS) were observed. Clinical relevanceDLSRR should be cautiously used in clinical settings owing to the occurrence of some cases of CAC-DRS reclassification. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Objective: To evaluate the impact of deep learning-based super-resolution reconstruction (DLSRR) on image quality and Agatston score. Methods: Consecutive patients who underwent cardiac CT, including unenhanced CT for Agatston scoring, were enrolled. Four types of non-contrast CT images were reconstructed using filtered back projection (FBP) and three strengths of DLSRR. Image quality was assessed by measuring image noise, signal-to-noise ratio (SNR) of the aorta, contrast-to-noise ratio (CNR), and edge rise slope (ERS) of coronary artery calcium (CAC). Agatston score and CAC volume were also measured. These results were compared among the four CT datasets. Patients were categorized into four risk levels based on the Coronary Artery Calcium Data and Reporting System (CAC-DRS), and the concordance rate between FBP and DLSRR classifications was evaluated. Results: For the 111 patients enrolled, DLSRR significantly reduced image noise (p < 0.001) and improved SNR and CNR (p < 0.001), with stronger effects at higher DLSRR strengths (p < 0.01). ERS was significantly enhanced using DLSRR compared with FBP (p < 0.001), whereas there was no significant difference among the three strengths of DLSRR (p = 0.90–0.98). Agatston score and CAC volume were not significantly affected by DLSRR (p = 0.952 and 0.901, respectively). The concordance rate of CAC-DRS classification between FBP and DLSRR was 93%. Conclusion: DLSRR significantly improves image quality by reducing noise and enhancing sharpness without significantly altering Agatston scores or CAC volumes. The concordance rate of CAC-DRS classification with FBP was high, although some reclassifications were observed. Key Points: QuestionThe utility of deep learning-based super-resolution reconstruction (DLSRR) in coronary CT angiography is well known, but its impact on the Agatston score remains unclear. FindingsDLSRR significantly improved image quality without altering the Agatston scores, but some reclassifications of Coronary Artery Calcium Data and Reporting System (CAC-DRS) were observed. Clinical relevanceDLSRR should be cautiously used in clinical settings owing to the occurrence of some cases of CAC-DRS reclassification. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09387994 |
| DOI: | 10.1007/s00330-025-11506-3 |
