EOS® imaging is a proprietary imaging technology that was launched in 2007. Based on a gaseous particle detector with a multi-wire proportional chamber, it offers several advantages over other imaging modalities: low dose of radiation, ability to create 3D reconstructions, ability to conduct whole body imaging, high reproducibility in measuring various parameters of alignment and faster imaging time. EOS® imaging is slowly gaining widespread acceptance as its applications in various disorders continue to evolve. It has been found to be particularly useful and has opened up new avenues of research in the field of spinal deformities. This narrative review seeks to provide an overview of the proprietary technology behind EOS® imaging, compare it to existing imaging modalities, summarize its current applications in various spinal disorders and outline its limitations.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7452333 | PMC |
http://dx.doi.org/10.1016/j.jcot.2020.06.012 | DOI Listing |
Sleep
January 2025
UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).
View Article and Find Full Text PDFNeurol Sci
January 2025
Epilepsy Center, Department of Neurology, West China Hospital of Sichuan University, Chengdu, China.
This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups.
View Article and Find Full Text PDFMAGMA
January 2025
Aix Marseille Univ, CNRS, CRMBM, Marseille, France.
Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.
View Article and Find Full Text PDFJ Ultrasound
January 2025
Department of Medical Imaging, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
This systematic review and meta-analysis aimed to assess the accuracy and success rate of ultrasound in determining fetal sex. A search was conducted on Medline, Cochrane Library, and EMBASE databases, and the reference lists of selected studies were also reviewed. Meta-analyses were performed using Revman 5.
View Article and Find Full Text PDFBiochem Genet
January 2025
Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand, 248001, India.
Indian Himalayan Region (IHR) supports a plethora of biodiversity with a unique assemblage of many charismatic and endemic species. We assessed the genetic diversity, demographic history, and habitat suitability of blue sheep (Pseudois nayaur) in the IHR through the analysis of the mitochondrial DNA (mtDNA) control region (CR) and Cytochrome b gene, and 14 ecological predictor variables. We observed high genetic divergence and designated them into two genetic lineage groups, i.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!