Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

IF 1.1 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Acta radiologica Pub Date : 2024-10-22 DOI:10.1177/02841851241288507

Fuminari Tatsugami, Toru Higaki, Ikuo Kawashita, Chikako Fujioka, Yuko Nakamura, Shinya Takahashi, Kazuo Awai

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引用次数: 0

Abstract

Background: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality.

Purpose: To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA.

Material and methods: We enrolled 29 patients who underwent CT angiography before aortic repair. VMIs obtained at 70 and 40 keV were reconstructed using hybrid iterative reconstruction (HIR), and 40 keV VMIs were reconstructed using DLIR. The image noise of the spinal cord, the maximum CT values of the anterior spinal artery (ASA), and the contrast-to-noise ratio (CNR) of the ASA were compared. The overall image quality and the delineation of the AKA were evaluated on a 4-point score (1 = poor, 4 = excellent).

Results: The mean image noise of the spinal cord was significantly lower on 40-keV DLIR than on 40-keV HIR scans; they were significantly higher than on 70-keV HIR images. The CNR of the ASA was highest on the 40-keV DLIR images among the three reconstruction images. The mean image quality scores for 40-keV DLIR and 70-keV HIR scans were comparable, and higher than of 40-keV HIR images. The mean delineation scores for 40-keV HIR and 40-keV DLIR scans were significantly higher than for 70-keV HIR images.

Conclusion: Visualization of the AKA was significantly better on low-keV VMIs subjected to DLIR than conventional HIR images.

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深度学习重建提高了低keV双能CT中Adamkiewicz动脉的图像质量。

背景：双能计算机断层扫描（CT）的低keV虚拟单能图像（VMI）增强了碘对比度，可用于检测小动脉，如Adamkiewicz动脉（AKA），但图像噪声可能是个问题。深度学习图像重建（DLIR）可在不影响图像质量的情况下有效降低噪声。目的：评估双能 CT 扫描的低 KeV VMI 上的 DLIR 是否能改善 AKA 的可视化：我们招募了 29 名在主动脉修复前接受 CT 血管造影术的患者。使用混合迭代重建（HIR）对 70 和 40 keV 的 VMI 进行重建，使用 DLIR 对 40 keV 的 VMI 进行重建。比较了脊髓的图像噪声、脊髓前动脉（ASA）的最大 CT 值和 ASA 的对比-噪声比（CNR）。对整体图像质量和 AKA 的划分进行了 4 级评分（1 = 差，4 = 优）：结果：脊髓的平均图像噪声在 40-keV DLIR 扫描中明显低于 40-keV HIR 扫描；在 70-keV HIR 图像中则明显高于 40-keV DLIR 扫描。在三种重建图像中，40-keV DLIR 图像的 ASA CNR 最高。40-keV DLIR 和 70-keV HIR 扫描的平均图像质量得分相当，且高于 40-keV HIR 图像。40-keV HIR 和 40-keV DLIR 扫描的平均划分分数明显高于 70-keV HIR 图像：结论：与传统的 HIR 图像相比，采用 DLIR 的低 keV VMI 对 AKA 的可视化效果明显更好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.