Objective: To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs).
Methods: Variable echo times neural network (VET-Net) is a two-stage framework that first estimates nonlinear variables of the CSE-MR signal model, to posteriorly estimate water/fat signal components using the least-squares method. VET-Net incorporates a vector with TEs as an auxiliary input, therefore enabling PDFF calculation with any TE setting.
Purpose: To describe an experimental surgical model in rats using a dual-plane technique for evaluation of biomaterials in an in-vivo silicone implant coverage.
Methods: This study was developed following the ISO 10993-6 standard. In this study, 40 male Wistar rats weighing between 250 and 350 g were used, distributed into two groups: experimental, biomaterial superimposed on the minimammary prosthesis (MP); and control, MP without implantation of the biomaterial, with eight animals at each biological point: 1, 2, 4, 12, and 26 weeks.
This study aimed to evaluate the effects of a blend of different sources of magnesium oxide associated or not with monensin, on productive, ruminal, and nutritional parameters of steers. Eighty-four Nellore steers with an initial body weight (BW) of 367.3 ± 37.
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