期刊
EUROPEAN RADIOLOGY
卷 33, 期 6, 页码 3839-3847出版社
SPRINGER
DOI: 10.1007/s00330-022-09300-6
关键词
Coronary artery disease; Deep learning; Tomography; X-ray computed; Image processing; Computer-assisted
This study investigates the performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using a deep learning-based denoising technique. The results show that CACS from LDCT images tend to be underestimated compared to those from calcium CT. However, there is excellent agreement between LDCT datasets and calcium CT, with DL and IR showing higher accuracy in CACS classification than 3-mm LDCT.
Objective To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)-based denoising technique. Methods This retrospective, intra-individual comparative study consisted of four image datasets of 131 participants who underwent LDCT and calcium CT on the same day between January and February 2020; 1-mm LDCT with DL, 1-mm LDCT with iterative reconstruction (IR), 3-mm LDCT, and calcium CT. CACS from calcium CT were considered as reference and CACS were categorized as 0, 1-10, 11-100, 101-400, and > 400. We compared CACS from LDCTs with that from calcium CT. Results Mean CACS was 104.8 & PLUSMN; 249.1 and proportion of positive CACS was 45% (59/131). CACS from LDCT images tended to be underestimated than those from calcium CT: 1-mm LDCT with DL (93.5 & PLUSMN; 249.6, p = 0.002), 1-mm LDCT with IR (94.7 & PLUSMN; 249.9, p < 0.001), and 3-mm LDCT (90.3 & PLUSMN; 245.3, p = 0.004). All LDCT datasets showed excellent agreement with calcium CT: intraclass correlation coefficient (ICC) = 0.961 (95% confidence interval (CI), 0.945-0.972) for DL, 0.969 (95% CI, 0.956-0.978) for IR, and 0.952 (95% CI, 0.932-0.966) for 3-mm LDCT; weighted kappa for CACS classification, 0.930 (95% CI, 0.893-0.966) for 1-mm LDCT with DL, 0.908 (95% CI, 0.866-0.950) for 1-mm LDCT with IR, and 0.846 (95% CI, 0.780-0.912) for 3-mm LDCT. The accuracy of CACS classification of 1-mm LDCT with DL (90%) tended to be better than 1-mm LDCT with IR (87%) and 3-mm LDCT (84.7%) (p = 0.10). Conclusion DL-based noise reduction algorithm can offer reliable calcium scores in 1-mm LDCT reconstructed with sharp kernel.
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