Objectives To evaluate the performance of a body mass index (BMI)-based sub-milliSievert low-dose CT (LDCT) protocol with multiple reconstruction algorithms for image quality and lung nodule assessment. Materials and methods This prospective study included 214 participants who underwent standard-dose CT (SDCT, 3.68 ± 1.53 mSv) reconstructed with 50% adaptive statistical iterative reconstruction (ASIR-V-50%) and LDCT. LDCT was randomly divided into a higher-dose group (LD-A, 0.57–1.15 mSv, n = 108) and a lower-dose group (LD-B, 0.33–0.63 mSv, n = 106). Each group was stratified into four BMI-based subgroups with individualized protocols reconstructed with deep learning image reconstruction (DLIR-H and DLIR-M), ASIR-V-50%, and filtered back projection (FBP). Image quality, nodule detection across BMI subgroups, and the performance of four algorithms in detection, size measurement accuracy, and Lung-RADS v2022 consistency were analyzed. Results In LDCT, DLIR-H provided superior image quality (p < 0.001) and the highest overall nodule detection rate (99.04%), surpassing ASIR-V-50% (98.55%) and FBP (97.87%) (both p < 0.05). The advantage was most evident for nodules < 6 mm, while all nodules ≥ 6 mm were consistently detected across algorithms. Detection rates showed no significant variation among BMI subgroups (all p > 0.05). For measurement accuracy, FBP and ASIR-V-50% performed better in LD-A (all p < 0.05), whereas DLIR-M was superior in LD-B (p < 0.001). All algorithms demonstrated excellent Lung-RADS agreement (κ > 0.9, p < 0.001). Conclusion A BMI-based sub-milliSievert LDCT protocol significantly reduced radiation exposure while maintaining nodule detection across BMI subgroups, with DLIR offering superior image quality and diagnostic performance. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Evidence remains scarce on BMI-based sub-milliSievert low-dose CT using different reconstruction algorithms, regarding image quality and nodules detection (particularly < 6 mm). <Emphasis Type="BoldItalic">Findings</Emphasis> BMI-based sub-milliSievert low-dose CT ensured balanced detectability across populations, while deep learning reconstruction improved image quality and achieved excellent sensitivity for lung nodule detection. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Deep learning reconstruction enhanced BMI-based sub-milliSievert low-dose CT, supporting its application in personalized sub-milliSievert low-dose lung cancer screening. Graphical Abstract

Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules

期刊论文
发表日期：

2026年2月14日

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Siqi Qu,
Qiuxing Chen,
Shulin Li,
Qijia Han,
Zhu Ai,
Minyi Wu,
Kun Ma,
Zhiming Xiang

摘要

Objectives

To evaluate the performance of a body mass index (BMI)-based sub-milliSievert low-dose CT (LDCT) protocol with multiple reconstruction algorithms for image quality and lung nodule assessment.

Materials and methods

This prospective study included 214 participants who underwent standard-dose CT (SDCT, 3.68 ± 1.53 mSv) reconstructed with 50% adaptive statistical iterative reconstruction (ASIR-V-50%) and LDCT. LDCT was randomly divided into a higher-dose group (LD-A, 0.57–1.15 mSv, n = 108) and a lower-dose group (LD-B, 0.33–0.63 mSv, n = 106). Each group was stratified into four BMI-based subgroups with individualized protocols reconstructed with deep learning image reconstruction (DLIR-H and DLIR-M), ASIR-V-50%, and filtered back projection (FBP). Image quality, nodule detection across BMI subgroups, and the performance of four algorithms in detection, size measurement accuracy, and Lung-RADS v2022 consistency were analyzed.

Results

In LDCT, DLIR-H provided superior image quality (p < 0.001) and the highest overall nodule detection rate (99.04%), surpassing ASIR-V-50% (98.55%) and FBP (97.87%) (both p < 0.05). The advantage was most evident for nodules < 6 mm, while all nodules ≥ 6 mm were consistently detected across algorithms. Detection rates showed no significant variation among BMI subgroups (all p > 0.05). For measurement accuracy, FBP and ASIR-V-50% performed better in LD-A (all p < 0.05), whereas DLIR-M was superior in LD-B (p < 0.001). All algorithms demonstrated excellent Lung-RADS agreement (κ > 0.9, p < 0.001).

Conclusion

A BMI-based sub-milliSievert LDCT protocol significantly reduced radiation exposure while maintaining nodule detection across BMI subgroups, with DLIR offering superior image quality and diagnostic performance.

Key Points

Question Evidence remains scarce on BMI-based sub-milliSievert low-dose CT using different reconstruction algorithms, regarding image quality and nodules detection (particularly < 6 mm).

Findings BMI-based sub-milliSievert low-dose CT ensured balanced detectability across populations, while deep learning reconstruction improved image quality and achieved excellent sensitivity for lung nodule detection.

Clinical relevance Deep learning reconstruction enhanced BMI-based sub-milliSievert low-dose CT, supporting its application in personalized sub-milliSievert low-dose lung cancer screening.

Graphical Abstract