Study Design: An analytical study.
Purpose: To analyze the inadequacies of magnetic resonance imaging (MRI) films provided by diagnostic centers, leading to questionable and inconclusive diagnoses.
Overview Of Literature: No literature is currently available on this subject.
Methods: Lumbosacral MRI films of patients who visited the outpatient department between January 2023 and March 31, 2024, were evaluated to check for technical inadequacies.
Results: A total of 1,150 lumbar MRI sets from 100 MRI centers were examined. Thirty-five percent did not include T1 axial images, and 8% did not include T1 sagittal images. Thirty-eight percent did not specify the sagittal image sequencing (right-to-left or left-to-right). Eighty-five percent of the sagittal images were profiled from right to left, and 15% were profiled from left to right. Macnab's recommendation was not followed in 970 sets. The axial sectioning of the scout films was nonparallel to the examined segment in 350 sets. The sacroiliac joint was not screened in 40% of the sets. The number of plates provided ranged from two to six films.
Conclusions: Based on the results obtained, we strongly recommend that radiologists form structured guidelines to be followed by MRI centers to ensure uniformity, address inadequacies, and minimize the chance of errors in diagnosis and subsequent treatment.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.31616/asj.2024.0389 | DOI Listing |
Endocr Regul
January 2025
1Endocrinology and Internal Medicine Department, Fattouma Bourguiba University Hospital, Monastir, Tunisia.
Pituitary neuroendocrine tumors (PitNETS) are common intracranial tumors, but extrasellar or ectopic PitNETS are very rare and supposed to originate from some pituitary remnants. They are mostly found in sphenoidal sinus. But particularly, ectopic clival PitNETS are highly aggressive and can cause bone invasion and can be misdiagnosed as other lesions of the skull base such as chordomas.
View Article and Find Full Text PDFNeurology
April 2025
Brain Health and Wellness Research Program, St. Michael's Hospital, Unity Health Toronto, Ontario, Canada.
Background And Objectives: Medical clearance for return to play (RTP) after sports-related concussion is based on clinical assessment. It is unknown whether brain physiology has entirely returned to preinjury baseline at the time of clearance. In this longitudinal study, we assessed whether concussed individuals show functional and structural MRI brain changes relative to preinjury levels that persist beyond medical clearance.
View Article and Find Full Text PDFDentomaxillofac Radiol
March 2025
Radiology Center, Division of Integrated Facilities, Institute of Science Tokyo Hospital, 1-5-45 Yushima, Bunkyo-ku, Tokyo, Japan.
Objective: To quantitatively and qualitatively compare directly two types of cisternography images for diagnosing trigeminal neuralgia (TN) using 3-T magnetic resonance imaging.
Methods: This prospective study recruited 64 patients with a clinical diagnosis or suspicion of TN. Patients were examined through the three-dimensional (3D) Constructive Interference in Steady State (CISS) and Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) sequences.
Sci Adv
March 2025
Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty.
View Article and Find Full Text PDFSci Adv
March 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Brain age gap (BAG), the deviation between estimated brain age and chronological age, is a promising marker of brain health. However, the genetic architecture and reliable targets for brain aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)-based brain age using deep learning models trained on the UK Biobank and validated with three external datasets.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!