Quantitative accuracy of virtual non-contrast images derived from spectral detector computed tomography: an abdominal phantom study

Abstract

Dual-energy CT allows for the reconstruction of virtual non-contrast (VNC) images. VNC images have the potential to replace true non-contrast scans in various clinical applications. This study investigated the quantitative accuracy of VNC attenuation images considering different parameters for acquisition and reconstruction. An abdomen phantom with 7 different tissue types (different combinations of 3 base materials and 5 iodine concentrations) was scanned using a spectral detector CT (SDCT). Different phantom sizes (S, M, L), volume computed tomography dose indices (CTDIvol 10, 15, 20 mGy), kernel settings (soft, standard, sharp), and denoising levels (low, medium, high) were tested. Conventional and VNC images were reconstructed and analyzed based on regions of interest (ROI). Mean and standard deviation were recorded and differences in attenuation between corresponding base materials and VNC was calculated (VNCerror). Statistic analysis included ANOVA, Wilcoxon test and multivariate regression analysis. Overall, the VNCerror was − 1.4 ± 6.1 HU. While radiation dose, kernel setting, and denoising level did not influence VNCerror significantly, phantom size, iodine content and base material had a significant effect (e.g. S vs. M: − 1.2 ± 4.9 HU vs. − 2.1 ± 6.0 HU; 0.0 mg/ml vs. 5.0 mg/ml: − 4.0 ± 3.5 HU vs. 5.1 ± 5.0 HU and 35-HU-base vs. 54-HU-base: − 3.5 ± 4.4 HU vs. 0.7 ± 6.5; all p ≤ 0.05). The overall accuracy of VNC images from SDCT is high and independent from dose, kernel, and denoising settings; however, shows a dependency on patient size, base material, and iodine content; particularly the latter results in small, yet, noticeable differences in VNC attenuation.

Virtual Non-Contrast Reconstructions of Photon-Counting Detector CT Angiography Datasets as Substitutes for True Non-Contrast Acquisitions in Patients after EVAR—Performance of a Novel Calcium-Preserving Reconstruction Algorithm

“…Since the introduction of DECT, many studies have explored using material differentiation algorithms to generate from contrast-enhanced datasets as substitutes for TNC-series in various clinical scenarios [7,10,11,[20][21][22][23][24] but reported diverging results. Furthermore, these approaches require access to a DECT-scanner and usually the selection of specific dual-energy acquisition protocols before image acquisition.…”

Virtual Noncontrast Imaging of the Liver Using Photon-Counting Detector Computed Tomography

“…A total of 19 lesions exhibiting a size greater than 5 mm were analyzed. The liver lesions contained varying amounts of iodine and base material, yielding attenuation values of 45, 60, 120, and 180 HU at 120 kV 8 . A detailed overview of the base material and lesion characteristics (as provided by the phantom vendor) is listed in Table 1.…”

Detection of fatty liver using virtual non-contrast dual-energy CT

“…A recent retrospective study in human subjects compared DECT to subjective grading of steatosis as mild, moderate, or severe based on conventional ultrasound or CT [ 17 ]. VNC images were shown to match true non-contrast images in both abdominal phantoms [ 18 ] as well as the livers of patients undergoing planning for transcatheter aortic valve replacement [ 19 ]. Other retrospective studies have investigated DECT for fatty liver without a reference standard, i.e., no quantitative MRI or liver biopsy for comparison [ 20 – 22 ].…”