Multiparametric functional MRI of the kidneys - evaluation of test-retest repeatability and effects of different manual and automatic image analysis strategies.

Multiparametric MRI is a promising technique for noninvasive structural and functional imaging of the kidneys that is gaining increasing importance in clinical research. Still, there are no standardized recommendations for analyzing the acquired images and there is a need to further evaluate the accuracy and repeatability of currently recommended MRI parameters. The aim of the study was to evaluate the test-retest repeatability of functional renal MRI parameters using different image analysis strategies.

Pre-Operative Traction in Femoral Fractures for Pain Management: A Meta-Analysis of Comparative Studies.

Objectives: This meta-analysis studies and assesses the pain relief effect of different pre-operative traction systems in proximal and femoral shaft fractures as this subject is still debated and no clear guidelines are established.

Methods: PubMed, Cochrane, Embase and Google Scholar (page 1-20) were searched until January 2024. The clinical outcomes collected consisted of pain scales following traction.

Hip Labral Repair versus Reconstruction: Meta-analysis.

The purpose of this meta-analysis is to compare the postoperative outcomes and complications of labral repair with those of labral reconstruction. An electronic search strategy was conducted from 1986 until August 2023 using the following databases: PubMed, Cochrane, and Google Scholar (pages 1-20). The primary objectives included the postoperative clinical outcomes determined by the number of patients who reached minimal clinical important difference (MCID) on the visual analog scale (VAS), modified Harris hip score (mHHS), Hip Outcome Score-Sports Subscale (HOS-SS), Hip Outcome Score-Activities of Daily Life (HOS-ADL), and International Hip Outcome Tool-12 (iHOT-12).

Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging.

Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels and additionally leverage long-range contextual information using a transformer-based module to alleviate ambiguities in the presence of artifacts caused by undersampling.