Background The axial length of a conventional PET/CT scanner is about 15–30 cm. However, uEXPLORER Total-Body PET/CT has an ultra-long axial field of view of 194 cm. By taking full use of all the scintillation photons, uEXPLORER has a 40 times higher sensitivity for photon detection relative to the conventional PET/CT. Ordered subset expectation maximization (OSEM) is a commonly used iterative algorithm in PET, however, it has a limitation that the image noise will increase when large number iteration is selected. A new penalized-likelihood iterative PET reconstruction, termed HYPER Iterative, was invented and now is available on the uEXPLORER Total-Body PET/CT. To date, its impact in lesion conspicuity in the patients with full injected dose or half injected dose was unclear. The goal of this study is to determine a proper protocol for routine 18F-FDG uEXPLORER Total-Body PET/CT scan. Results The quality of the 5 min PET image was excellent (score 5) for all the dose and reconstructed methods. Using the HYPER iterative method, PET image reached the excellent quality at 1 min with full-dose, and at 2 min with half-dose. While PET image reached a similar excellent quality at 2 min with full-dose and 3 min with half-dose using OSEM. The noise in OSEM reconstruction was higher than that by HYPER Iterative. Compared to OSEM, HYPER Iterative had slightly higher SUVmax and TBR of the lesions for large positive lesions (≥ 2cm) (SUVmax: up to 9% higher in full-dose and up to 13% higher in half-dose; TBR: up to 9% higher in full-dose and up to 23% higher in half-dose). For small positive lesions(≤ 10mm), HYPER Iterative had obviously higher SUVmax and TBR of the lesions (SUVmax: up to 45% higher in full-dose and up to 75% higher in half-dose; TBR: up to 45% higher in full-dose and up to 94% higher in half-dose). Conclusions Our study demonstrates that 1min scan with full dose and 2 min with half dose is proper for clinical diagnosis using HYPER Iterative, and 2 to 3 min scan for OSEM reconstruction. For detection of the small lesions, HYPER Iterative reconstruction is preferred.
The fractional differential algorithm has a good effect on extracting image textures, but it is usually necessary to select an appropriate fractional differential order for textures of different scales, so we propose a novel approach for haptic texture rendering of two-dimensional (2D) images by using an adaptive fractional differential method. According to the fractional differential operator defined by the Grünvald–Letnikov derivative (G–L) and combined with the characteristics of human vision, we propose an adaptive fractional differential method based on the composite sub-band gradient vector of the sub-image obtained by wavelet decomposition of the image texture. We apply these extraction results to the haptic display system to reconstruct the three-dimensional (3D) texture force filed to render the texture surface of two-dimensional (2D) images. Based on this approach, we carry out the quantitative analysis of the haptic texture rendering of 2D images by using the multi-scale structural similarity (MS-SSIM) and image information entropy. Experimental results show that this method can extract the texture features well and achieve the best texture force file for 2D images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.