Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.

Purpose: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.

Methods: DCE-MRI data for simulation studies were synthesized using the extended Tofts model and a population-averaged arterial input function (AIF). The ranges of PK parameters for training the RNNs were determined from data of patients with brain tumors.

Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.

Purpose: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms.

Materials And Methods: This retrospective study included 54 consecutive patients who underwent contrast-enhanced abdominal CT. Thin-slice images (0.

Longitudinal associations between dietary diversity and serum lipid markers in Japanese workers.

Objective: The aim of this study was to determine the longitudinal associations between dietary diversity score and serum lipid markers in a five-year follow-up period in Japanese workers.

Methods: This study included 745 participants aged 20-60 years in 2012-2013 without dyslipidemia at baseline who participated at least once from 2013 to 2017. Dietary intake was assessed using a food frequency questionnaire, and dietary diversity score was determined using the Quantitative Index for Dietary Diversity.