Improving image quality and diagnostic performance using deep learning image reconstruction in 100-kVp CT enterography for patients with wide-range body mass index

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-05-14 DOI:10.1016/j.ejrad.2025.112167

Yan Luo , Youfa Tang , Suping Chen , Hao Tang , Yiqi Cheng , Fan Zhang , Yaqi Shen , Qiuxia Wang

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引用次数: 0

Abstract

Objective

To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion detection in 100-kVp CT enterography (CTE) for patients with wide-range body mass index (BMI).

Methods

A total of 84 patients underwent 100-kVp dual-phase CTE were included. Images were reconstructed using filtered back projection (FBP), ASiR-V 30 %, ASiR-V 60 %, and DLIR with low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H). The CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of small and large intestines were compared using repeated measures analysis of variance with the Bonferroni correction or Friedman test. The correlation between relative CNR increment and BMI was analyzed using Pearson’s correlation coefficient. The overall image quality and diagnostic confidence scores were evaluated. Additionally, lesion detection of intestinal disease was conducted by three readers with different experience and compared between DLIR-M and ASiR-V 60 % images using McNemar’s test.

Results

SD decreased sequentially from FBP, ASiR-V 30 %, DLIR-L, ASiR-V 60 %, DLIR-M, to DLIR-H, which corresponded with improvements in CNR and SNR (all p < 0.001). The relative CNR increment of DLIR exhibited a significantly positive linear correlation with BMI (r:0.307–0.506, all p ≤ 0.005). For overall image quality scores, the ranking was: FBP < ASiR-V 30 % < ASiR-V 60 % ≈DLIR-L < DLIR-M ≈ DLIR-H. DLIR-M outperformed ASiR-V 60 % in diagnostic confidence (p ≤ 0.018 for all three readers). In lesion detection, for the two junior readers, DLIR-M exhibited higher sensitivity for inflammatory lesions compared to ASiR-V 60 % (0.700 (95 % confidence interval [95 % CI]: 0.354–0.919) vs. 0.300 (95 % CI: 0.081–0.646) for reader 1 and 0.700 (95 %CI: 0.354–0.919) vs. 0.500 (95 % CI: 0.201–0.799) for reader 2), though no statistical significance was reached.

Conclusion

DLIR effectively reduces noise and improves image quality in 100-kVp dual-phase CTE for wide-range BMIs. DLIR-M exhibits superior performance in image quality and diagnostic confidence, also provide potential value in improving intestinal inflammatory lesion detection in junior readers and sheds lights on benefiting clinical decision making, which needs further investigation.

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利用深度学习图像重建提高100 kvp CT肠造影对大范围体重指数患者的图像质量和诊断性能

目的评价深度学习图像重建（DLIR）算法与传统自适应统计迭代重建- veo （ASiR-V）算法在100 kvp CT肠造影（CTE）中对大范围体重指数（BMI）患者的图像质量、诊断置信度和肠道病变检测方面的临床价值。方法84例行100 kvp双期CTE的患者。使用滤波后投影（FBP）、ASiR-V 30%、ASiR-V 60%和低、中、高电平DLIR （DLIR- l、DLIR- m和DLIR- h）重建图像。采用重复测量方差分析、Bonferroni校正或Friedman检验比较小肠和大肠的CT值、标准差（SD）、信噪比（SNR）和对比噪声比（CNR）。采用Pearson相关系数分析相对CNR增量与BMI的相关性。评估整体图像质量和诊断信心得分。此外，由3名不同经验的读者进行肠道疾病的病变检测，并使用McNemar测试对DLIR-M和ASiR-V 60%图像进行比较。结果ssd从FBP、ASiR-V 30%、dir - l、ASiR-V 60%、DLIR-M到DLIR-H依次下降，与CNR和SNR的改善相对应(p <；0.001)。DLIR的相对CNR增量与BMI呈显著的线性正相关（r:0.307 ~ 0.506，均p≤0.005）。对于整体图像质量得分，排名为：FBP <；ASiR-V 30% <；ASiR-V 60%≈DLIR-L <；dlr - m≈dlr - h。DLIR-M在诊断置信度上优于ASiR-V 60%（所有三种阅读器的p≤0.018）。在病变检测方面，对于两名初级读卡器，DLIR-M对炎性病变的敏感性高于ASiR-V，前者为0.700(95%可信区间[95% CI]: 0.354-0.919)，后者为0.300 (95% CI: 0.081-0.646)，后者为0.700 (95% CI: 0.354-0.919)，后者为0.500 (95% CI: 0.201-0.799)，但差异无统计学意义。结论dlir在100 kvp双相CTE中可有效降低噪声，提高图像质量。DLIR-M在图像质量和诊断置信度方面表现优异，在提高初级读者肠道炎症病变检测方面具有潜在价值，对临床决策有益，有待进一步研究。

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.