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深度学习重建与标准重建在腹部 CT 中的应用:BMI 的影响。

Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.

机构信息

The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China.

CT Imaging Research Center, GE Healthcare China, Beijing, 100176, China.

出版信息

Eur Radiol. 2024 Mar;34(3):1614-1623. doi: 10.1007/s00330-023-10179-0. Epub 2023 Aug 31.

Abstract

OBJECTIVE

This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs).

METHODS

A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m), 100-kVp group (BMI 24-28.9 kg/m), and 120-kVp group (BMI ≥ 29 kg/m). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared.

RESULTS

DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups.

CONCLUSION

For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both.

CLINICAL RELEVANCE STATEMENT

The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs.

KEY POINTS

• DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.

摘要

目的

本研究旨在评估深度学习图像重建(DLIR)算法与标准图像重建算法在体质量指数(BMI)范围较宽的腹部增强 CT 扫描中的图像质量和病变显示效果。

方法

将 112 名接受腹部增强 CT 扫描的参与者根据 BMI 分为三组:80kVp 组(BMI≤23.9kg/m)、100kVp 组(BMI 24-28.9kg/m)和 120kVp 组(BMI≥29kg/m)。所有图像均采用滤波反投影(FBP)、自适应统计迭代重建 50%水平(IR)和低、中、高水平的 DLIR(DL、DM 和 DH)进行重建。采用 5 分制对噪声、伪影、整体图像质量以及低和高对比肝病变显示效果进行评分。量化并比较 CT 衰减值(HU)、图像噪声和对比噪声比(CNR)。

结果

对于所有三组 BMI,DM 和 DH 均优于 FBP 和 IR,改善了定性和定量参数。DH 的图像噪声最低,CNR 值最高,而 DM 对三组 BMI 的主观整体图像质量和低、高对比病变显示效果评分最高。基于 FBP,DM 和 DH 图像的质量和病变显示效果的改善在 80kVp 组比 100kVp 和 120kVp 组更明显。

结论

对于所有 BMI,DLIR 均能提高图像质量和肝病变显示效果,其中 DM 是平衡两者的最佳选择。

临床相关性声明

本研究表明,在广泛的 BMI 范围内,使用 DLIR 可显著提高腹部 CT 扫描的图像质量和病变可见度。

要点

  • DLIR 提高了广泛 BMI 范围内的图像质量和病变显示效果。

  • 在所有重建水平中,DLIR 中的中水平具有最高的主观参数和病变显示效果评分。

  • 在基于 FBP 的情况下,80kVp 组的图像质量和病变显示效果的改善比 100kVp 和 120kVp 组更明显。

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