3D task‐transfer function representation of the signal transfer properties of low‐contrast lesions in FBP‐ and iterative‐reconstructed CT. Issue 11 (9th October 2018)

• Record Type: Journal Article
• Authors: Robins, Marthony; Solomon, Justin; Richards, Taylor; Samei, Ehsan
• Is Part Of: Medical physics. Volume 45:Issue 11(2018)
• Page Start: 4977
• Page End: 4985
• Publication Date: 2018-10-09
• Subjects: CT simulation -- low contrast -- quantitative -- segmentation -- task‐transfer function -- volume; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13205
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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 mm 3 ), 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.