What question did this study set out to answer?

To evaluate a BMI-based low-dose CT protocol for lung nodule detection and image quality.

February 16, 2026

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

SQSiqi QuGuangzhou University of Chinese Medicine QCQiuxing ChenGuangzhou University of Chinese Medicine SLShulin LiGuangzhou University of Chinese Medicine

Key Points

To evaluate a BMI-based low-dose CT protocol for lung nodule detection and image quality.
Prospective study with 214 participants comparing low-dose CT and standard-dose CT.
CT protocols included multiple reconstruction algorithms: ASIR-V-50%, DLIR-H, DLIR-M, and FBP.
Radiation exposure levels were stratified into higher-dose and lower-dose groups with BMI-based subgroups.
DLIR-H showed superior image quality and the highest nodule detection rate (99.04%).
Detection rates were consistent across BMI subgroups, with no significant variation.
All algorithms achieved excellent Lung-RADS agreement, indicating reliable performance.

Abstract

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. 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. In LDCT, DLIR-H provided superior image quality ( p 0.05). For measurement accuracy, FBP and ASIR-V-50% performed better in LD-A (all p 0.9, p < 0.001). 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. 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.

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