Two-dimensional radial MRI using compressed sensing (2D radial CS) enables incoherence sampling in k space unlike conventional Cartesian MRI, however 2D radial CS has not been sufficiently investigated. Numerical and visual evaluations of 2D radial CS were performed in this paper. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weigthted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for the numerical evaluation. The Brainweb MRI Data Base was used for test images. Projection of 80 spokes with linear sampling of 256 pixels was used. Reconstruction was performed by minimizing the L1 norm of a transformed image using wavelet transform and spatial finite-differences (total variation), subject to data fidelity constraint. In the absence of noise, the root mean square error (RMSE) of T1WI was in the range of 3.75 to 5.05; that of the anatomical region of interests (ROIs) was in the range of 1.54 to 10.24; those of T2WI were 8.75 to 11.65 and 4.31 to 6.99; and those of PDWI were 3.44 to 4.46 and 1.34 to 3.09. Visual evaluation was performed by three radiologists on the basis of three categories: artifact, anatomical structure, and tissue contrast. Average percent scores of the visual evaluation were 96% for T1WI, 74-81% for T2WI, and 81-89% for PDWI.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.11323/jjmp.37.3_150 | DOI Listing |
Retina
January 2025
Kresge Eye Institute/Department of Ophthalmology, Visual and Anatomical Sciences, Wayne State University School of Medicine, Detroit, MI 48201, USA.
Purpose: To assess the effectiveness of split-thickness amniotic membrane (SAM) grafts in achieving closure of refractory or large macular holes (MH).
Methods: This retrospective study reviewed data from patients who underwent surgical repair of MHs using SAM grafts between January 2019 and December 2023. Key parameters, including best-corrected visual acuity (BCVA) and MH size, were evaluated both preoperatively and postoperatively.
PLoS Negl Trop Dis
January 2025
Department of Infectious Disease, Peking University Ditan Teaching Hospital, Beijing, China.
Background: Platelet recovery was an important prognostic indicator in severe fever with thrombocytopenia syndrome (SFTS). This study focused on risk factors affecting platelet recovery in surviving SFTS patients, which can assist clinicians in the early screening of patients associated with a greater risk of mortality.
Method: We retrospectively analyzed the clinical data of SFTS patients admitted to Yantai Qishan Hospital throughout 2023.
J Bone Joint Surg Am
January 2025
Northumbria Healthcare NHS Foundation Trust, Northumberland, United Kingdom.
Background: Greater trochanteric pain syndrome (GTPS) is a painful condition that can impair a patient's quality of life. If nonoperative measures fail, progressively more invasive treatment options may be required. This clinical trial aimed to evaluate the effectiveness of ultrasound-guided leukocyte-rich platelet-rich plasma (LR-PRP) injections in the treatment of refractory GTPS caused by bursitis and/or gluteal tendinopathy.
View Article and Find Full Text PDFGigascience
January 2025
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
Background: The accurate deciphering of spatial domains, along with the identification of differentially expressed genes and the inference of cellular trajectory based on spatial transcriptomic (ST) data, holds significant potential for enhancing our understanding of tissue organization and biological functions. However, most of spatial clustering methods can neither decipher complex structures in ST data nor entirely employ features embedded in different layers.
Results: This article introduces STMSGAL, a novel framework for analyzing ST data by incorporating graph attention autoencoder and multiscale deep subspace clustering.
Bioinformatics
January 2025
School of Artificial Intelligence, Jilin University, Jilin, China.
Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.
Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!