3D task-transfer function representation of the signal transfer properties of low-contrast lesions in FBP- and iterative-reconstructed CT.

PURPOSE: 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 commercial CT system. METHODS: A cylindrical phantom containing 24 anthropomorphic "physical" lesions was 3D printed. Lesions had two sizes (523, 2145 mm3 ), and two nominal radio-densities (80 and 100 HU at 120 kV). CT images were acquired on a commercial CT system (Siemens Flash scanner) at four dose levels (CTDIvol , 32 cm phantom:1.5, 3.0, 6.0, 22.0 mGy) and reconstructed using FBP and IR kernels (B31f, B45f, I31f\2, I44f\2). Low-contrast rod inserts (in-plane) and a slanted edge (z-direction) were used to estimate 3D-TTFs. CAD versions of lesions were blurred by the 3D-TTFs, virtually superimposed into corresponding phantom images, and compared to the physical lesions in terms of (a) a 4AFC visual assessment, (b) edge gradient, (c) size, and (d) shape similarity. Assessments 2 and 3 were based on an equivalence criterion D ¯ ≥ COV ¯ to determine if the natural variability COV ¯ in the physical lesions was greater or equal to the difference D ¯ between physical and simulated. Shape similarity was quantified via Sorensen-Dice coefficient (SDC). Comparisons were done for each lesion and for all imaging conditions. RESULTS: The readers detected simulated lesions at a rate of 37.9 ± 3.1% (25% implies random guessing). Lesion edge blur and volume differences D ¯ were on average less than physical lesions' natural variability COV ¯ . The SDC (average ± SD) was 0.80 ± 0.13 (max of 1 possible). CONCLUSIONS: The visual appearance, edge blur, size, and shape of simulated lesions were similar to the physical lesions, which suggests 3D-TTF models the low-contrast signal transfer properties of this non-linear CT system reasonably well.

Duke Scholars

Author Justin Solomon Radiology

Author Ehsan Samei Radiology