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J Appl Clin Med Phys. 2019 Aug;20(8):155-163. doi: 10.1002/acm2.12666. Epub 2019 Jun 20.

Reliability of CT-based texture features: Phantom study.

Author information

1
Dept. of Radiology, Univ. of Southern California, Los Angeles, CA, USA.
2
The Phantom Laboratory, Greenwich, NY, USA.
3
Department of Radiology, George Washington University, Washington, DC, USA.

Abstract

OBJECTIVE:

To determine the intra-, inter- and test-retest variability of CT-based texture analysis (CTTA) metrics.

MATERIALS AND METHODS:

In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust.

RESULTS:

As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used.

CONCLUSION:

Following large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

KEYWORDS:

computed tomography; imaging; phantom study; reliability; texture analysis

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