Background: High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these images require long acquisition times, so routinely acquired follow-up images after treatment often consist of 2D low-resolution (LR) images (with thick slices in multiple planes).
Purpose: In this work, we present a super-resolution convolutional neural network, based on previous single-image MRI super-resolution work, that can reconstruct a HR image from 2D LR slices in multiple planes in order to facilitate the extraction of structural biomarkers from routine scans.
Background And Purpose: A risk calculation model was presented in 2021 by Keilty et al. for determining the likelihood of severe hearing impairment (HI) for paediatric patients treated with photon radiation therapy. This study aimed to validate their risk-prediction model for our cohort of paediatric patients treated with proton therapy (PT) for malignancies of the head and neck (H&N) or central nervous system (CNS).
View Article and Find Full Text PDFBackground: Short all-oral regimens for Rifampicin-resistant tuberculosis (ShORRT) have been a turning point in the treatment of drug-resistant tuberculosis. Despite this, access to drugs, stockouts, or adverse effects may limit the use of the recommended regimens.
Methods: Pragmatic non-randomized trial evaluating the efficacy and safety of a ShORRT strategy for the treatment of rifampicin-resistant Tuberculosis (RR-TB) at the Hospital Nossa Senhora da Paz (Angola).