The effect of intra spinal administration of cerium oxide nanoparticles on central pain mechanism: An experimental study.

This study investigated Cerium oxide nanoparticles (CeONPs) effect on central neuropathic pain (CNP). The compressive method of spinal cord injury (SCI) model was used for pain induction. Three groups were formed by a random allocation of 24 rats.

Introducing fitting models for estimating age-specific dose and effective dose in paediatric patients undergoing head, chest and abdomen-pelvis imaging protocols: a patient study.

Introduction: Concerns regarding the adverse consequences of radiation have increased due to the expanded application of computed tomography (CT) in medical practice. Certain studies have indicated that the radiation dosage depends on the anatomical region, the imaging technique employed and patient-specific variables. The aim of this study is to present fitting models for the estimation of age-specific dose estimates (ASDE), in the same direction of size-specific dose estimates, and effective doses based on patient age, gender and the type of CT examination used in paediatric head, chest and abdomen-pelvis imaging.

Prediction of chronic kidney disease in abdominal cancers radiation therapy using the functional assays of normal tissue complication probability models.

Aim: The purpose of this study is to predict chronic kidney disease (CKD) in the radiotherapy of abdominal cancers by evaluating clinical and functional assays of normal tissue complication probability (NTCP) models.

Materials And Methods: Radiation renal damage was analyzed in 50 patients with abdominal cancers 12 months after radiotherapy through a clinical estimated glomerular filtration rate (eGFR). According to the common terminology criteria for the scoring system of adverse events, Grade 2 CKD (eGFR ≤30-59 ml/min/1.

Deep Learning Approach for Fusion of Magnetic Resonance Imaging-Positron Emission Tomography Image Based on Extract Image Features using Pretrained Network (VGG19).

Background: The fusion of images is an interesting way to display the information of some different images in one image together. In this paper, we present a deep learning network approach for fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) images.

Methods: We fused two MRI and PET images automatically with a pretrained convolutional neural network (CNN, VGG19).

Generating pseudo-computerized tomography (P-CT) scan images from magnetic resonance imaging (MRI) images using machine learning algorithms based on fuzzy theory for radiotherapy treatment planning.

Purpose: The substitution of computerized tomography (CT) with magnetic resonance imaging (MRI) has been investigated for external radiotherapy treatment planning. The present study aims to use pseudo-CT (P-CT) images created by MRI images to calculate the dose distribution for facilitating the treatment planning process.

Methods: In this work, following image segmentation with a fuzzy clustering algorithm, an adaptive neuro-fuzzy algorithm was utilized to design the Hounsfield unit (HU) conversion model based on the features vector of MRI images.