Work

Towards optimized CT lung cancer screening scan protocols

Year: 2026

Type: article

Abstract: OBJECTIVES: To evaluate radiation dose and image quality for CT lung cancer screening protocols and to determine which reconstruction kernels deliver accurate nodule volumetry. METHODS: A variable size anthropomorphic chest phantom and a lung screening image quality phantom were scanned on 17 different scanner models. Dose metrics from the chest phantom scans were compared against international dose recommendations. Image quality phantom scans were used to assess whether the protocol yielded accurate nodule volumetry as per the Quantitative Imaging Biomarkers Alliance (QIBA) criteria, and to determine whether changes were needed to enable this. RESULTS: Doses varied by up to a factor of 7 for the largest phantom size. All protocols delivered doses below the UK standards but only 2 scanners met the European dose criteria. Many scanners failed to meet the QIBA criteria with lung reconstruction kernels, primarily due to excessive edge enhancement and insufficient 3D spatial resolution. With alternative reconstruction kernels, the QIBA criteria were met on all bar 2 scanners. CONCLUSIONS: Standard lung screening CT protocols on contemporary scanners produce images of varying image quality, over a wide range of radiation doses. Scanner-specific reconstruction kernel selection is required to meet the QIBA criteria. The study findings enable the development of optimized CT scanning protocols for lung cancer screening program. ADVANCES IN KNOWLEDGE: Standard lung cancer screening scanning protocols demonstrate notable variations in dose. Lung reconstruction kernels often yield inaccurate measurements of nodule volume; instead, soft tissue kernels are necessary to obtain reliable results.

Source: British Journal of Radiology

DOI: 10.1093/bjr/tqag066

Authors: Gareth R Iball, Charlotte A Porter

Institutions: University of Bradford, Leeds Teaching Hospitals NHS Trust

Language: English

Topic: Radiation Dose and Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,005