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深度学习图像重建高算法在低辐射和低对比剂剂量一站式冠状动脉及颈动脉-脑血管CT血管造影中的应用

Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses.

作者信息

Li Wanjiang, Huang Wenyu, Li Peiyao, Wen Yuting, Shuai Tao, He Yong, You Yongchun, Yu Jianqun, Diao Kaiyue, Song Bin

机构信息

Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.

West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China.

出版信息

Quant Imaging Med Surg. 2024 Feb 1;14(2):1860-1872. doi: 10.21037/qims-23-864. Epub 2024 Jan 23.

DOI:10.21037/qims-23-864
PMID:38415146
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10895143/
Abstract

BACKGROUND

For patients with suspected simultaneous coronary and cerebrovascular atherosclerosis, conventional single-site computed tomography angiography (CTA) for both sites can result in nonnegligible radiation and contrast agent dose. The purpose of this study was to validate the feasibility of one-stop coronary and carotid-cerebrovascular CTA (C&CC-CTA) with a "double-low" (low radiation and contrast) dose protocol reconstructed with deep learning image reconstruction with high setting (DLIR-H) algorithm.

METHODS

From February 2018 to January 2019, 60 patients referred to C&CC-CTA simultaneously in West China Hospital were recruited in this prospective cohort study. By random assignment, patients were divided into two groups: double-low dose group (n=30) used 80 kVp and 24 mgI/kg/s contrast dose with images reconstructed using DLIR-H; and routine-dose group (n=30) used 100 kVp and 32 mgI/kg/s contrast dose with images reconstructed using 50% adaptive statistical iterative reconstruction-V (ASIR-V50%). Radiation and contrast doses, subjective image quality score, CT attenuation values, noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured and compared between the groups.

RESULTS

The DLIR-H group used 30% less contrast dose (35.80±4.85 51.13±6.91 mL) and 48% less overall radiation dose (1.00±0.09 1.91±0.42 mSv) than the ASIR-V50% group (both P<0.001). There was no statistically significant difference on subjective quality score between the two groups (C-CTA: 4.38±0.67 4.17±0.81, P=0.337 and CC-CTA: 4.18±0.87 4.08±0.79, P=0.604). For coronary CTA, lower background noise (18.93±1.43 22.86±3.75 HU) was reached in DLIR-H group, and SNR and CNR at all assessed branches were significantly increased compared to ASIR-V50% group (all P<0.05), except SNR of left anterior descending (P>0.05). For carotid-cerebrovascular CTA, DLIR-H group was comparable in background noise (19.25±1.42 20.23±2.40 HU), SNR and CNR at all assessed branches with ASIR-V50% group (all P>0.05).

CONCLUSIONS

The "double-low" dose one-stop C&CC-CTA with DLIR-H obtained higher image quality compared with the routine-dose protocol with ASIR-V50% while achieving 48% and 30% reduction in radiation and contrast dose, respectively.

摘要

背景

对于疑似同时患有冠状动脉和脑血管动脉粥样硬化的患者,对两个部位进行传统的单部位计算机断层扫描血管造影(CTA)会导致不可忽视的辐射和造影剂剂量。本研究的目的是验证采用深度学习图像重建的高设置(DLIR-H)算法重建的“双低”(低辐射和低造影剂)剂量方案进行一站式冠状动脉和颈动脉-脑血管CTA(C&CC-CTA)的可行性。

方法

在这项前瞻性队列研究中,招募了2018年2月至2019年1月期间同时在华西医院接受C&CC-CTA检查的60例患者。通过随机分配,将患者分为两组:双低剂量组(n = 30)采用80 kVp和24 mgI/kg/s的造影剂剂量,并使用DLIR-H重建图像;常规剂量组(n = 30)采用100 kVp和32 mgI/kg/s的造影剂剂量,并使用50%自适应统计迭代重建-V(ASIR-V50%)重建图像。测量并比较两组之间的辐射剂量、造影剂剂量、主观图像质量评分、CT衰减值、噪声、信噪比(SNR)和对比噪声比(CNR)。

结果

与ASIR-V50%组相比,DLIR-H组的造影剂剂量减少了30%(35.80±4.85对51.13±6.91 mL),总辐射剂量减少了48%(1.00±0.09对1.91±0.42 mSv)(均P<0.001)。两组之间的主观质量评分无统计学显著差异(冠状动脉CTA:4.38±0.67对4.17±0.81,P = 0.337;颈动脉-脑血管CTA:4.18±0.87对4.08±0.79,P = 0.604)。对于冠状动脉CTA,DLIR-H组的背景噪声更低(18.93±1.43对22.86±3.75 HU),与ASIR-V50%组相比,所有评估分支的SNR和CNR均显著增加(均P<0.05),左前降支的SNR除外(P>0.05)。对于颈动脉-脑血管CTA,DLIR-H组在背景噪声(19.25±1.42对20.23±2.40 HU)、所有评估分支的SNR和CNR方面与ASIR-V50%组相当(均P>0.05)。

结论

与采用ASIR-V50%的常规剂量方案相比,采用DLIR-H的“双低”剂量一站式C&CC-CTA在辐射剂量和造影剂剂量分别降低48%和30%的同时,获得了更高的图像质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/7d07c43f78d7/qims-14-02-1860-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/6188b682b53b/qims-14-02-1860-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/4ce2d9610cd4/qims-14-02-1860-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/ab2db8c545f6/qims-14-02-1860-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/7d07c43f78d7/qims-14-02-1860-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/6188b682b53b/qims-14-02-1860-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/4ce2d9610cd4/qims-14-02-1860-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/ab2db8c545f6/qims-14-02-1860-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9618/10895143/7d07c43f78d7/qims-14-02-1860-f4.jpg

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