Can a 3D task transfer function accurately represent the signal transfer properties of low-contrast lesions in non-linear CT systems?

Marthony Robins; Justin Solomon; Ehsan Samei

doi:10.1117/12.2294588

9 March 2018 Can a 3D task transfer function accurately represent the signal transfer properties of low-contrast lesions in non-linear CT systems?

Marthony Robins, Justin Solomon, Ehsan Samei

Author Affiliations +

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 1057343 (2018) https://doi.org/10.1117/12.2294588
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

The purpose of this study was to investigate how accurately the task-transfer function (TTF) models the signal transfer properties of low-contrast features in a non-linear CT system. A cylindrical phantom containing 24 anthropomorphic liver lesions (modeled from patient lesions) was designed using computer-aided design software (Rhinoceros 3D). Lesions had irregular shapes, 2 sizes (523, 2145 mm³), and 2 contrast levels (80, 100 HU). The phantom was printed with a state-of-the-art multimaterial 3D printer (Stratasys J750). CT images were acquired on a clinical CT scanner (Siemens Flash) at 4 dose levels (CTDI_Vol, 32 cm phantom: 1.5, 3, 6, 22 mGy) and reconstructed using 2 FBP kernels (B31f, B45f) and 2 iterative kernels (SAFIRE, strength-2: I31f, and I44f). 3D-TTFs were estimated by combining TTFs measured using low-contrast rod inserts (in-plane) and a slanted edge (z-direction) printed in-phantom. CAD versions of lesions were blurred by 3D-TTFs and virtually superimposed into corresponding phantom images using a previously validated technique. We compared lesion morphometry (i.e., size and shape) measurements between 3D printed “physical” and TTF-blurred “simulated” lesions across multiple acquisitions. Lesion size was quantified using a commercial segmentation software (Syngo.via). Lesion shape was quantified by measuring the Jaccard index between the segmented masks of paired physical and simulated lesions. The relative volume difference D between physical and simulated lesions was mostly less than the natural variability COV of the physical lesions. For large and small lesions, the COV_1,𝑘,𝑙 was greater or similar to D_𝑘,𝑙 in 12 and 13 out of 16 imaging scenarios, respectively. Simulated and physical lesion shapes were similar, with an average simulated-physical Jaccard index of 0.70 (out of max value of unity). These results suggest 3D-TTFs closely models the signal transfer properties of linear and non-linear CT conditions for low-contrast objects.

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Marthony Robins, Justin Solomon, and Ehsan Samei "Can a 3D task transfer function accurately represent the signal transfer properties of low-contrast lesions in non-linear CT systems?", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057343 (9 March 2018); https://doi.org/10.1117/12.2294588

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