Quantitative CT reconstruction kernel harmonization for multi‐site lung cancer screening
David W. Jordan, Ryan E. Misseldine

TL;DR
This study finds ways to standardize CT scans for lung cancer screening so that nodule measurements are accurate and images look consistent across different scanners.
Contribution
A method to harmonize CT reconstruction kernels across different scanners to ensure consistent nodule measurements and image appearance.
Findings
Medium sharp kernels matched reference MTF and NPS for six scanner models.
One medium smooth and one common lung kernel failed QIBA compliance.
MTF and NPS comparisons can identify compliant reconstructions for lung cancer screening.
Abstract
Published reference CT protocols for lung cancer screening are not optimized to produce uniform image appearance across different scanner manufacturers and models or to conform to quantitative imaging profiles for robust small lung nodule size and volume measurements, which are important in clinical management of screen‐detected nodules. This study used widely available phantoms and software to identify lung cancer screening CT reconstructions that enable accurate and reproducible nodule size measurements and to match reconstructions across scanner manufacturers and models to provide a consistent image appearance to interpreting physicians. ACR CT accreditation phantom scans were used to measure the modulation transfer function (MTF) and noise power spectrum (NPS) for various reconstruction kernels for six CT scanner models from three manufacturers. A reference kernel was chosen, and…
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Taxonomy
TopicsLung Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · Advanced X-ray and CT Imaging
