Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases

医学 核医学 图像质量 放射科 前瞻性队列研究 内科学 计算机科学 图像(数学) 人工智能
作者
Corey T. Jensen,Shiva Gupta,Mohammed Saleh,Xinming Liu,Vincenzo K. Wong,Usama Salem,Wei Qiao,Ehsan Samei,Nicolaus A. Wagner‐Bartak
出处
期刊:Radiology [Radiological Society of North America]
卷期号:303 (1): 90-98 被引量:67
标识
DOI:10.1148/radiol.211838
摘要

Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate liver metastases and image quality between reduced-dose deep learning image reconstruction (DLIR) and standard-dose filtered back projection (FBP) contrast-enhanced abdominal CT. Materials and Methods In this prospective Health Insurance Portability and Accountability Act–compliant study (September 2019 through April 2021), participants with biopsy-proven colorectal cancer and liver metastases at baseline CT underwent standard-dose and reduced-dose portal venous abdominal CT in the same breath hold. Three radiologists detected and characterized lesions at standard-dose FBP and reduced-dose DLIR, reported confidence, and scored image quality. Contrast-to-noise ratios for liver metastases were recorded. Summary statistics were reported, and a generalized linear mixed model was used. Results Fifty-one participants (mean age ± standard deviation, 57 years ± 13; 31 men) were evaluated. The mean volume CT dose index was 65.1% lower with reduced-dose CT (12.2 mGy) than with standard-dose CT (34.9 mGy). A total of 161 lesions (127 metastases, 34 benign lesions) with a mean size of 0.7 cm ± 0.3 were identified. Subjective image quality of reduced-dose DLIR was superior to that of standard-dose FBP (P < .001). The mean contrast-to-noise ratio for liver metastases of reduced-dose DLIR (3.9 ± 1.7) was higher than that of standard-dose FBP (3.5 ± 1.4) (P < .001). Differences in detection were identified only for lesions 0.5 cm or smaller: 63 of 65 lesions detected with standard-dose FBP (96.9%; 95% CI: 89.3, 99.6) and 47 lesions with reduced-dose DLIR (72.3%; 95% CI: 59.8, 82.7). Lesion accuracy with standard-dose FBP and reduced-dose DLIR was 80.1% (95% CI: 73.1, 86.0; 129 of 161 lesions) and 67.1% (95% CI: 59.3, 74.3; 108 of 161 lesions), respectively (P = .01). Lower lesion confidence was reported with a reduced dose (P < .001). Conclusion Deep learning image reconstruction (DLIR) improved CT image quality at 65% radiation dose reduction while preserving detection of liver lesions larger than 0.5 cm. Reduced-dose DLIR demonstrated overall inferior characterization of liver lesions and reader confidence. Clinical trial registration no. NCT03151564 © RSNA, 2022 Online supplemental material is available for this article.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
2秒前
4秒前
6秒前
JJ发布了新的文献求助10
6秒前
王子安完成签到,获得积分10
7秒前
橙色小人发布了新的文献求助10
8秒前
cyan发布了新的文献求助30
10秒前
11秒前
天天快乐应助喜喵喵采纳,获得10
11秒前
shinysparrow应助dream采纳,获得200
11秒前
13秒前
KevenDing完成签到,获得积分10
15秒前
wulalala发布了新的文献求助30
16秒前
CipherSage应助loski采纳,获得10
16秒前
我是老大应助loski采纳,获得10
16秒前
完美世界应助loski采纳,获得10
17秒前
李健应助loski采纳,获得10
17秒前
欣喜沛芹发布了新的文献求助10
19秒前
可爱的函函应助橙色小人采纳,获得10
20秒前
ED应助多发论文采纳,获得10
22秒前
22秒前
量子星尘发布了新的文献求助10
22秒前
Owen应助机智思真采纳,获得10
23秒前
传奇3应助loski采纳,获得10
24秒前
万能图书馆应助自觉的凛采纳,获得10
26秒前
30秒前
积极幻桃应助ssjjzhou采纳,获得10
31秒前
讨厌科研完成签到,获得积分10
32秒前
35秒前
Xw发布了新的文献求助10
37秒前
38秒前
失眠的霸完成签到,获得积分10
39秒前
ChatGPT发布了新的文献求助10
40秒前
42秒前
多发论文完成签到,获得积分20
43秒前
43秒前
44秒前
su发布了新的文献求助10
44秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989378
求助须知:如何正确求助?哪些是违规求助? 3531442
关于积分的说明 11254002
捐赠科研通 3270126
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173