- Spine MRI in Spontaneous Intracranial Hypotension for CSF Leak Detection: Nonsuperiority of Intrathecal Gadolinium to Heavily T2-Weighted Fat-Saturated Sequences
The authors performed a retrospective study of patients with spontaneous intracranial hypotension examined from February 2013 to October 2017. The spine MR imaging was reviewed by 3 blinded readers for the presence of epidural CSF using 3 different sequences (T2WI, 3D T2WI fat-saturated, T1WI gadolinium). In patients with leaks, the presumed level of the leak was reported. They conclude that intrathecal gadolinium-enhanced spine MR imaging does not improve the diagnostic accuracy for the detection of epidural CSF. Gadolinium myelography lacks a rationale to be included in the routine spontaneous intracranial hypotension work-up. Heavily T2-weighted images with fat saturation provide high accuracy for the detection of an epidural CSF collection.
- High Spatiotemporal Resolution 4D Flow MRI of Intracranial Aneurysms at 7T in 10 Minutes
The authors used pseudospiral Cartesian undersampling with compressed sensing reconstruction to achieve high spatiotemporal resolution (0.5mm isotropic, ∼30 ms) in a scan time of 10 minutes. They analyzed the repeatability of accelerated 4D-flow scans and compared flow rates, stroke volume, and the pulsatility index with 2D-flow and conventional 4D-flow MR imaging in a flow phantom and 15 healthy subjects. Mean flow-rate bias compared with 2D-flow was lower for accelerated than for conventional 4D-flow MR imaging. Pulsatility index bias gave similar results. Stroke volume bias showed no difference from accelerated bias for conventional 4D-flow MR imaging. Repeatability for accelerated 4D-flow was similar to that of 2D-flow MR imaging. They conclude that highly accelerated high-spatiotemporal-resolution 4D-flow MR imaging at 7T in intracranial arteries and aneurysms provides repeatable and accurate quantitative flow values.
- High Prevalence of Spinal Cord Cavernous Malformations in the Familial Cerebral Cavernous Malformations Type 1 Cohort
With prospective imaging to screen the spinal cord, the authors found SCCMs in 21 of 29 familial CCM1 patients, a prevalence of 72.4%. They conclude that the study demonstrates that SCCMs are indeed a common finding in patients with familial CCM and supports the idea of familial CCM syndrome as a progressive systemic disease that affects the entire central nervous system. They found an expected positive correlation of number of SCCMs with both patient age and number of intracranial CCMs. They also found a high prevalence of vertebral intraosseous vascular malformations (69%), including atypical (T1 hypointense) intraosseous vascular malformation in approximately 38% of the patients who underwent MR imaging screening.
- Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on 18F-Florbetapir PET Using ADNI Data
Using the Alzheimer's Disease Neuroimaging Initiative dataset, the authors identified 2582 18F-florbetapir PET scans, which were separated into positive and negative cases by using a standardized uptake value ratio threshold of 1.1. They trained convolutional neural networks to predict standardized uptake value ratio and classify amyloid status. The best performance was seen for ResNet-50 by using regression before classification, 3 input PET slices, and pretraining, with a standardized uptake value ratio root-mean-squared error of 0.054, corresponding to 95.1% correct amyloid status prediction. The best trained network was more accurate than humans (96% versus a mean of 88%, respectively). They conclude that deep learning algorithms can estimate standardized uptake value ratio and use this to classify 18F-florbetapir PET scans and have promise to automate this laborious calculation.
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