Improving intracranial aneurysms image quality and diagnostic confidence with deep learning reconstruction in craniocervical CT angiography.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Acta radiologica Pub Date : 2024-08-01 Epub Date: 2024-06-05 DOI:10.1177/02841851241258220
Kun Bai, Tiantian Wang, Guozhi Zhang, Ming Zhang, Hongchao Fu, Yun Feng, Kaiyi Liang
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Abstract

Background: The diagnostic impact of deep learning computed tomography (CT) reconstruction on intracranial aneurysm (IA) remains unclear.

Purpose: To quantify the image quality and diagnostic confidence on IA in craniocervical CT angiography (CTA) reconstructed with DEep Learning Trained Algorithm (DELTA) compared to the routine hybrid iterative reconstruction (HIR).

Material and methods: A total of 60 patients who underwent craniocervical CTA and were diagnosed with IA were retrospectively enrolled. Images were reconstructed with DELTA and HIR, where the image quality was first compared in noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Next, two radiologists independently graded the noise appearance, arterial sharpness, small vessel visibility, conspicuity of calcifications that may present in arteries, and overall image quality, each with a 5-point Likert scale. The diagnostic confidence on IAs of various sizes was also graded.

Results: Significantly lower noise and higher SNR and CNR were found on DELTA than on HIR images (all P < 0.05). All five subjective metrics were scored higher by both readers on the DELTA images (all P < 0.05), with good to excellent inter-observer agreement (κ = 0.77-0.93). DELTA images were rated with higher diagnostic confidence on IAs compared to HIR (P < 0.001), particularly for those with size ≤3 mm, which were scored 4.5 ± 0.6 versus 3.4 ± 0.8 and 4.4 ± 0.7 versus 3.5 ± 0.8 by two readers, respectively.

Conclusion: The DELTA shows potential for improving the image quality and the associated confidence in diagnosing IA that may be worth consideration for routine craniocervical CTA applications.

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在颅颈部 CT 血管造影中利用深度学习重建提高颅内动脉瘤图像质量和诊断信心。
背景:目的:与常规混合迭代重建(HIR)相比,量化使用深度学习训练算法(DELTA)重建的颅颈部CT血管造影(CTA)的图像质量和对IA的诊断信心:回顾性研究共收集了60例接受颅颈CTA检查并确诊为IA的患者。使用 DELTA 和 HIR 重建图像,首先比较图像质量的噪声、信噪比(SNR)和对比度-噪声比(CNR)。然后,由两名放射科医生分别对噪声外观、动脉清晰度、小血管可见度、动脉中可能出现的钙化的明显程度以及整体图像质量进行独立评分,每项评分均采用 5 分制李克特量表。此外,还对不同大小的内脏器官的诊断可信度进行了评分:结果:与 HIR 图像相比,DELTA 图像的噪声明显降低,信噪比和 CNR 明显提高(均为 P P P P 结论:DELTA 图像显示出改进动脉成像的潜力:DELTA 显示出提高图像质量和相关 IA 诊断信心的潜力,值得在常规颅颈 CTA 应用中加以考虑。
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来源期刊
Acta radiologica
Acta radiologica 医学-核医学
CiteScore
2.70
自引率
0.00%
发文量
170
审稿时长
3-8 weeks
期刊介绍: Acta Radiologica publishes articles on all aspects of radiology, from clinical radiology to experimental work. It is known for articles based on experimental work and contrast media research, giving priority to scientific original papers. The distinguished international editorial board also invite review articles, short communications and technical and instrumental notes.
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