冠状动脉计算机断层血管造影术的深度学习超分辨率重建评估冠状动脉和支架内腔：初步经验。

Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience.

机构信息

Department of Radiology, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Japan.

Center for Radiological Science, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Japan.

出版信息

BMC Med Imaging. 2023 Oct 30;23(1):171. doi: 10.1186/s12880-023-01139-7.

DOI:10.1186/s12880-023-01139-7

PMID:37904089

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10617195/

Abstract

A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed tomography (UHRCT) has the potential to provide better image quality of coronary arteries on the whole-heart, single-rotation cardiac coverage on a 320-detector row CT scanner. However, the advantages of SR-DLR at coronary computed tomography angiography (CCTA) have not been fully investigated. The present study aimed to compare the image quality of the coronary arteries and in-stent lumen between SR-DLR and model-based iterative reconstruction (MBIR). We prospectively enrolled 70 patients (median age, 69 years; interquartile range [IQR], 59-75 years; 50 men) who underwent CCTA using a 320-detector row CT scanner between January and August 2022. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the proximal coronary arteries were calculated. Of the twenty stents, stent strut thickness and luminal diameter were quantitatively evaluated. The image noise on SR-DLR was significantly lower than that on MBIR (median 22.1 HU; IQR, 19.3-24.9 HU vs. 27.4 HU; IQR, 24.2-31.2 HU, p < 0.01), whereas the SNR (median 16.3; IQR, 11.8-21.8 vs. 13.7; IQR, 9.9-18.4, p = 0.01) and CNR (median 24.4; IQR, 15.5-30.2 vs. 19.2; IQR, 14.1-23.2, p < 0.01) on SR-DLR were significantly higher than that on MBIR. Stent struts were significantly thinner (median, 0.68 mm; IQR, 0.61-0.78 mm vs. 0.81 mm; IQR, 0.72-0.96 mm, p < 0.01) and in-stent lumens were significantly larger (median, 1.84 mm; IQR, 1.65-2.26 mm vs. 1.52 mm; IQR, 1.28-2.25 mm, p < 0.01) on SR-DLR than on MBIR. Although further large-scale studies using invasive coronary angiography as the reference standard, comparative studies with UHRCT, and studies in more challenging population for CCTA are needed, this study's initial experience with SR-DLR would improve the utility of CCTA in daily clinical practice due to the better image quality of the coronary arteries and in-stent lumen at CCTA compared with conventional MBIR.

摘要

一种使用超高空间分辨率计算机断层扫描 (UHRCT) 采集的数据进行训练的超分辨率深度学习重建 (SR-DLR) 算法有可能提供更好的冠状动脉整体心脏、320 排 CT 扫描仪单次旋转心脏覆盖的图像质量。然而，SR-DLR 在冠状动脉计算机断层血管造影术 (CCTA) 中的优势尚未得到充分研究。本研究旨在比较 SR-DLR 和基于模型的迭代重建 (MBIR) 对冠状动脉和支架内管腔的图像质量。我们前瞻性地招募了 70 名患者（中位年龄 69 岁；四分位距 [IQR]，59-75 岁；50 名男性），他们在 2022 年 1 月至 8 月期间使用 320 排 CT 扫描仪进行 CCTA。测量升主动脉、左心房和心室间隔壁的图像噪声，并计算近端冠状动脉的信噪比 (SNR) 和对比噪声比 (CNR)。对二十个支架进行定量评估，包括支架支柱厚度和管腔直径。SR-DLR 的图像噪声明显低于 MBIR（中位数 22.1HU；IQR，19.3-24.9HU 比 27.4HU；IQR，24.2-31.2HU，p<0.01），而 SNR（中位数 16.3；IQR，11.8-21.8 比 13.7；IQR，9.9-18.4，p=0.01）和 CNR（中位数 24.4；IQR，15.5-30.2 比 19.2；IQR，14.1-23.2，p<0.01）在 SR-DLR 上明显高于 MBIR。支架支柱明显更薄（中位数，0.68mm；IQR，0.61-0.78mm 比 0.81mm；IQR，0.72-0.96mm，p<0.01），支架内管腔明显更大（中位数，1.84mm；IQR，1.65-2.26mm 比 1.52mm；IQR，1.28-2.25mm，p<0.01）在 SR-DLR 上比在 MBIR 上。尽管需要进一步使用有创冠状动脉造影作为参考标准的大规模研究、与 UHRCT 的比较研究以及在更具挑战性的 CCTA 人群中的研究，但由于与传统的 MBIR 相比，SR-DLR 可改善冠状动脉和支架内管腔的 CCTA 图像质量，因此本研究的初步经验可能会提高 CCTA 在日常临床实践中的应用价值。