More articles from Adult Brain
- Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.
- Vessel-Selective 4D-MRA Using Superselective Pseudocontinuous Arterial Spin-Labeling with Keyhole and View-Sharing for Visualizing Intracranial Dural AVFs
4D-S-PACK is useful for the identification of feeding arteries and accurate classification of intracranial dural AVFs and can be a useful noninvasive clinical tool.
- Short-Range Structural Connections Are More Severely Damaged in Early-Stage MS
The authors demonstrate that more severe demyelination preceding axonal degeneration occurs in short-range connections but not in long-range connections in early-stage MS, suggesting the possibility that there are cortical lesions that are undetectable by current MR imaging.
- Acceleration of Brain Susceptibility-Weighted Imaging with Compressed Sensitivity Encoding: A Prospective Multicenter Study
Compressed sensitivity encoding factor 4 is recommended in routine practice. Compressed sensitivity encoding factor 10 is potentially a fast surrogate for distinguishing the basilar artery and detecting susceptibility-related abnormalities (eg, cerebral microbleeds, cavernous angiomas, gliomas, and venous malformation) at the sacrifice of visualization of the substantia nigra-red nucleus and internal cerebral vein.