No abstract

Digital subtraction myelography is a valuable diagnostic technique to detect the exact location of CSF leaks in the spine to facilitate appropriate diagnosis and treatment of spontaneous spinal CSF leaks. Digital subtraction myelography is an excellent diagnostic tool for assessment of various types of CSF leaks, and lateral decubitus digital subtraction myelography is increasingly being used to diagnose CSF-venous fistulas. Lateral decubitus digital subtraction myelography differs from typical CT and fluoroscopy-guided myelograms in many ways, including equipment, supplies, and injection and image-acquisition techniques. Operators should be familiar with techniques, common pitfalls, and artifacts to improve diagnostic yield and prevent nondiagnostic examinations.

No abstract

No abstract

Objective:Assess the diagnostic yield of lateral decubitus digital subtraction myelography (LDDSM) and stratify LDDSM diagnostic yield by the Bern spontaneous intracranial hypotension (SIH) score of the pre-procedure brain MRI.Methods:This retrospective diagnostic study included consecutive adult patients investigated for SIH who underwent LDDSM. Patients without pre-procedure brain and spine MRI, and patients with extradural fluid collection on spine MRI (type 1 leak) were excluded. LDDSM images and brain MRIs were assessed by two independent blinded readers; a third reader adjudicated any discrepancies. Diagnostic yield of LDDSM was assessed, both overall and stratified by Bern SIH scoring.Results:Of the 62 patients included in this study, 33(53.2%) had a CSF leak identified on LDDSM. Right-sided leaks were more common (70.6%), and the most commonly identified levels of leaks were at T6, T7, and T10. No leak was found in any of the 9 patients with Bern SIH score of 2 or less. Of the 11 patients with Bern SIH score of 3-4, 5(45.5%) had a CSF leak identified, while of the 42 patients with Bern SIH score of 5 or higher, 28(66.7%) had a CSF leak identified.Conclusions:LDDSM has a high diagnostic yield for finding the exact location of spinal CSF leak, and the diagnostic yield increases with higher Bern SIH score. No leaks were found in patients with Bern SIH score of 2 or less, suggesting that foregoing invasive testing such as LDDSM in these patients may be appropriate unless accompanied by high clinical suspicion.Classification of Evidence:This study provides Class II evidence that for patients with suspected SIH, higher Bern SIH scores are associated with a greater likelihood of LDDSM-identified CSF leaks.

The most common MRI finding in GAD65-associated autoimmune epilepsy is disproportionate parenchymal atrophy for age, often associated with abnormal cortical/subcortical T2 hyperintensities. Hippocampal abnormalities are seen in a minority of patients. This constellation of findings in a patient with medically intractable epilepsy should raise the possibility of GAD65 autoimmunity.

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations.Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap).

No abstract