Calibration phantom-based prediction of CT lung nodule volume measurement performance.

Background

A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol.

Methods

The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing.

Results

The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting.

Conclusions

This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

Informations de copyright

2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-320/coif). Ricardo Avila is the CEO and owns stock in Accumetra. Accumetra also holds and submits patents in the area of CT image quality. He is also the owner of Paraxial, LLC which develops imaging software and other technologies. Ricardo Avila’s wife (Lisa Avila) is the CEO of Kitware which also develops imaging software and other technologies. Karthik Krishnan is an independent consultant of Accumetra, LLC. NO collaborates with QIBA as a statistical consultant through a contract between her institution and RSNA. AJ received a 2-year $100k grant from the Prevent Cancer Foundation for a project to model factors influencing nodule measurement uncertainty. The grant was paid to my institution and ended in Jan 2021. This is not directly related to this manuscript other than the overarching theme of trying to make better measurements. DY is a named inventor on a number of patents and patent applications related to the evaluation of chest diseases including measurements of chest nodules. He has received financial compensation for the licensing of these patents. In addition, he is a consultant and co-owner of Accumetra, a private company developing tools to improve the quality of CT imaging. He is on the advisory board and owns equity in HeartLung, a company that develops software related to CT scans of the chest. He is on the medical advisory board of Median Technology that is developing technology related to analyzing pulmonary nodules and is on the medical advisory board of Carestream, a company that develops radiography equipment and has consulted for Genentech, AstraZeneca and Pfizer. The other author has no conflicts of interest to declare.