Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.

IF 4.7 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Radiology Pub Date : 2025-07-01 Epub Date: 2025-01-10 DOI:10.1007/s00330-024-11317-y

Kai Ye, Libo Xu, Boyang Pan, Jie Li, Meijiao Li, Huishu Yuan, Nan-Jie Gong

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引用次数: 0

Abstract

Objectives: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.

Materials and methods: A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses.

Results: A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001).

Conclusion: DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT.

Key points: Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.

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基于深度学习的图像域重建方法通过自适应统计迭代重建增强了超低剂量CT图像质量和肺结节检测。

目的：评价采用基于深度学习图像重建（DLIR）的图像域进行自适应统计迭代重建- v （ASiR-V）后处理的超低剂量CT （ULDCT）与低剂量CT （LDCT）和未进行DLIR的ULDCT的图像质量和肺结节可检出性。材料与方法：210例肺癌筛查患者分别行LDCT（平均±SD， 0.81±0.28 mSv）和ULDCT（0.17±0.03 mSv）扫描。用ASiR-V （ULDCT-ASiR-V）重建ULDCT图像，用DLIR （ULDCT-DLIR）进行后处理。分析三幅CT图像的质量。三名放射科医生在所有CT图像上检测并测量肺结节，并以LDCT结果作为参考。采用李克特五分制评估结节的显著性，然后进行进一步的统计分析。结果：LDCT共检出463个结节。与ULDCT-ASiR-V相比，ULDCT-DLIR的图像噪声降低了60%，低于LDCT (p 1 mm分别为2.9% （ULDCT-ASiR-V vs LDCT）和0.5% (ULDCT-DLIR vs LDCT) （p = 0.009）。ULDCT- dlir的结节成像清晰度评分（4.0±0.68）明显高于ULDCT-ASiR-V的（3.2±0.50）(p)。结论：基于dlir的图像域提高了ULDCT上ASiR-V的图像质量、结节检出率、结节成像清晰度和结节测量精度。与原始数据处理相比，深度学习后处理简单、成本低，但在超低剂量CT上表现不明显。发现深度学习后处理增强了图像质量，提高了超低剂量CT的结节检出率和结节测量的准确性。深度学习后处理提高了超低剂量CT的实用性，使患者在肺癌筛查中减少辐射暴露成为可能。

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来源期刊

European Radiology 医学-核医学

CiteScore

11.60

自引率

8.50%

发文量

874

审稿时长

2-4 weeks

期刊介绍： European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.