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 (CTDIVol, 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 COV1,𝑘,𝑙 was greater or similar to D𝑘,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.