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This work aimed to compare two different types of volume estimation methods (a model-based and a segmentationbased method) in terms of identifying factors affecting measurement uncertainty. Twenty-nine synthetic nodules with varying size, radiodensity, and shape were placed in an anthropomorphic thoracic phantom and scanned with a 16- detector row CT scanner. Ten repeat scans were acquired using three exposures and two slice collimations, and were reconstructed with varying slice thicknesses. Nodule volumes were estimated from the reconstructed data using a matched-filter and a segmentation approach. Log transformed volumes were used to obtain measurement error with truth obtained through micro-CT. ANOVA and multiple linear regression were applied to measurement error to identify significant factors affecting volume estimation for each method. Root mean square of measurement errors (RMSE) for meaningful subgroups, repeatability coefficients (RC) for different imaging protocols, and reproducibility coefficients (RDC) for thin and thick collimation conditions were evaluated. Results showed that for both methods, nodule size, shape and slice thickness were significant factors. Collimation was significant for the matched-filter method. RMSEs for matched-filter measurements were in general smaller than segmentation. To achieve RMSE on the order of 15% or less for {5, 8, 9, 10mm} nodules, the corresponding maximum allowable slice thicknesses were {3, 5, 5, 5mm} for the matched-filter and {0.8, 3, 3, 3mm} for the segmentation method. RCs showed similar patterns for both methods, increasing with slice thickness. For 8-10mm nodules, the measurements were highly repeatable provided the slice thickness was ≤3mm, regardless of method and across varying acquisition conditions. RDCs were lower for thin collimation than thick collimation protocols. While RDC of matched filter volume estimation results was always lower than segmentation results, for 8-10mm nodules with thin collimation protocols, the measurements for both approaches were highly reproducible (RDC on the order of 15% or less). These findings are valuable for validating lung nodule volume as a quantitative imaging biomarker.
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