MRI Denoising Using Pixel-Wise Threshold Selection.

Magnetic resonance imaging (MRI) has emerged as a promising technique for non-invasive medical imaging. The primary challenge in MRI is the trade-off between image visual quality and acquisition time. Current MRI image denoising algorithms employ global thresholding to denoise the whole image, which leads to inadequate denoising or image distortion.

Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

Background And Purpose: Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI.

Endonasal, supraorbital, and transorbital approaches: minimal access endoscope-assisted surgical approaches for meningiomas in the anterior and middle cranial fossae.

Objective: Minimally invasive endoscope-assisted approaches to the anterior skull base offer an alternative to traditional open craniotomies. Given the restrictive operative corridor, appropriate case selection is critical for success. In this paper, the authors present the results of three different minimal access approaches to meningiomas of the anterior and middle fossae and examine the differences in the target areas considered appropriate for each approach, as well as the outcomes, to determine whether the surgical goals were achieved.

Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System.

Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes.