Relevance of antinuclear antibody in diagnosis and characteristics of multiple sclerosis.

Background: Criteria for multiple sclerosis (MS) diagnosis rely upon clinical and paraclinical data that are supportive of MS in the absence of a better explanation. Patients referred for consideration of a MS diagnosis often undergo an extensive serologic workup including antinuclear antibody (ANA) testing, even when an individual already meets diagnostic criteria for MS. It is unclear whether ANA serostatus is associated with clinical outcomes in MS.

Clinical and radiologic characteristics associated with multiple sclerosis misdiagnosis at a tertiary referral center in the United States.

Background: Misdiagnosis of multiple sclerosis (MS) is common and can have harmful effects on patients and healthcare systems. Identification of factors associated with misdiagnosis may aid development of prevention strategies.

Objective: To identify clinical and radiological predictors of MS misdiagnosis.

Dual-Energy Computed Tomography in Stroke Imaging : Value of a New Image Acquisition Technique for Ischemia Detection after Mechanical Thrombectomy.

Objective: To assess if a new dual-energy computed tomography (DECT) technique enables an improved visualization of ischemic brain tissue after mechanical thrombectomy in acute stroke patients.

Material And Methods: The DECT head scans with a new sequential technique (TwinSpiral DECT) were performed in 41 patients with ischemic stroke after endovascular thrombectomy and were retrospectively included. Standard mixed and virtual non-contrast (VNC) images were reconstructed.

A Novel Dual-Energy CT Method for Detection and Differentiation of Intracerebral Hemorrhage From Contrast Extravasation in Stroke Patients After Endovascular Thrombectomy : Feasibility and First Results.

Purpose: Dual-energy computed tomography (DECT) has been shown to be able to differentiate between intracranial hemorrhage (ICH) and extravasation of iodinated contrast media (contrast staining [CS]). TwinSpiral DECT is a recently introduced technique, which allows image acquisition at two different energy levels in two consecutive spiral scans. The aim of this study was to evaluate the feasibility and accuracy of TwinSpiral DECT to distinguish between ICH and CS after endovascular thrombectomy (EVT) in patients with acute ischemic stroke.

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process.