Browse the latest research summaries in the field of medical imaging for spinal cord injury patients and caregivers.
Showing 111-120 of 201 results
Acta Ortop Bras, 2012 • January 1, 2012
This study aimed to identify radiographic changes in the hips of spinal cord-injured patients, who often experience altered joint forces and are prone to osteoarticular changes. Radiographic assessmen...
KEY FINDING: The study found that only 23% of the hips examined showed no evidence of articular damage, highlighting a high rate of hip impairment in spinal cord-injured patients.
Acta Ortop Bras, 2012 • September 1, 2012
This study assessed shoulder pain in paraplegic and tetraplegic patients using clinical data and MRI exams to identify prevalent lesions and discuss their etiologies. The results showed a variety of s...
KEY FINDING: A significant percentage (41%) of shoulders examined showed normal results on MRI, indicating that pain isn't always linked to observable anatomical damage.
Acta Ortop Bras, 2014 • January 1, 2014
This study evaluated the use of SPECT/CT to assess heterotopic ossification (HO) in patients with spinal cord injuries, focusing on the stage of maturation and metabolic activity of the HO. The result...
KEY FINDING: Only a small percentage of hips with HO (12.5%) showed high osteoblastic activity, meaning active bone formation, as detected by SPECT/CT.
AJNR Am J Neuroradiol, 1998 • August 1, 1998
This case report describes a 58-year-old woman who developed Brown-Se´quard syndrome after undergoing chiropractic manipulation for neck and thoracic pain. The patient experienced left-sided weakness ...
KEY FINDING: The patient presented with clinical features consistent with Brown-Se´quard syndrome following chiropractic manipulation.
PLOS ONE, 2019 • May 9, 2019
This study presents a novel automatic 3-D approach for the volumetric segmentation and quantitative assessment of thigh MRI volumes in individuals with chronic SCI and non-disabled individuals. The pr...
KEY FINDING: The proposed automatic segmentation method achieved an overall accuracy of 0.93±0.06 for adipose tissue and muscle compartments segmentation based on Dice Similarity Coefficient.
BMC Musculoskeletal Disorders, 2022 • June 3, 2022
This study compared two methods (GVR-A and GVR-B) for evaluating spinal cord hyperechogenicity intensity using intraoperative ultrasound (IOUS) in patients with degenerative cervical myelopathy (DCM) ...
KEY FINDING: GVR-B has better repeatability of gray value measurement, smaller relative standard deviation (RSD%) compared with GVR-A.
Cancers, 2022 • July 23, 2022
The purpose of this study was to retrospectively analyze the diagnostic accuracy of magnetic resonance imaging (MRI) examinations of the scrotum in comparison with standard ultrasound (US) and histopa...
KEY FINDING: MRI shows good sensitivity and specificity for the estimation of testicular tumors in this collective.
Healthcare, 2022 • December 7, 2022
The study evaluated the necessity of follow-up chest X-rays (CXRs) in patients with minor chest trauma and fewer than three rib fractures who showed no initial respiratory distress. The results indica...
KEY FINDING: Only 1.6% of patients with minor chest trauma and fewer than three rib fractures required delayed intervention (tube thoracostomy) based on follow-up CXRs.
Bioengineering, 2023 • September 10, 2023
The study developed deep learning models to automate the detection and measurement of the dural sack cross-sectional area (DSCA) in lumbar spine MRI. The MultiResUNet model achieved the highest accura...
KEY FINDING: The MultiResUNet model showed the best performance, with high accuracy in both initial and external validation datasets.
PLOS ONE, 2023 • October 12, 2023
This study presents a deep learning approach for the fully automatic segmentation of vessels in mice brains using a shallow U-Net model trained on a small μMRI reference dataset. The proposed methodol...
KEY FINDING: Deep learning architectures, specifically a shallow 3D U-Net model, are applicable for segmenting mice brain vessels using small μMRI reference datasets.