- Determinants of Deep Gray Matter Atrophy in Multiple Sclerosis: A Multimodal MRI Study
Seventy-seven patients with MS and 44 healthy controls were enrolled in this cross-sectional study. MR imaging investigation included volumetric, diffusion tensor imaging, perfusion weighted imaging, and Quantitative Susceptibility Mapping analyses. Deep gray matter structures were automatically segmented to obtain volumes and mean values for each MR imaging metric in the thalamus, caudate, putamen, and globus pallidus. Patients with MS showed a multifaceted involvement of the thalamus and basal ganglia, with significant atrophy of all deep gray matter structures. In the relapsing-remitting MS group, WM lesion burden proved to be the main contributor to volume loss for all deep gray matter structures.
- Deep Learning–Based Detection of Intracranial Aneurysms in 3D TOF-MRA
In a retrospective study, the authors established a system for the detection of intracranial aneurysms from 3D TOF-MRA data. The system is based on an open-source neural network, originally developed for segmentation of anatomic structures in medical images. Eighty-five datasets of patients with a total of 115 intracranial aneurysms were used to train the system and evaluate its performance. Manual annotation of aneurysms based on radiologic reports and critical revision of image data served as the reference standard. The highest overall sensitivity of this system for the detection of intracranial aneurysms was 90% with a sensitivity of 96% for aneurysms with a diameter of 3–7 mm and 100% for aneurysms of >7 mm. The best location-dependent performance was in the posterior circulation.
- Radiomics-Based Intracranial Thrombus Features on CT and CTA Predict Recanalization with Intravenous Alteplase in Patients with Acute Ischemic Stroke
Sixty-seven patients with ICA/M1 MCA segment thrombus treated with IV alteplase were included in this analysis. Three hundred twenty-six radiomics features were extracted from each thrombus on both NCCT and CTA images. Linear discriminative analysis was applied to select features most strongly associated with early recanalization with IV alteplase. These features were then used to train a linear support vector machine classifier. Thrombus radiomics features derived from NCCT and CTA are more predictive of recanalization with IV alteplase in patients with acute ischemic stroke with proximal occlusion than previously known thrombus imaging features such as length, volume, and permeability.
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