- Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model
The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.
- Dual-Layer Detector Cone-Beam CT Angiography for Stroke Assessment: First-in-Human Results (the Next Generation X-ray Imaging System Trial)
Dual-layer detector cone-beam CTA virtual monoenergetic images are noninferior to CTA under certain conditions. Notably, the prototype is hampered by a long scan time and is not capable of contrast media bolus tracking.
- A Novel MR Imaging Sequence of 3D-ZOOMit Real Inversion-Recovery Imaging Improves Endolymphatic Hydrops Detection in Patients with Ménière Disease
The 3D-ZOOMit real IR sequences are superior to conventional 3D TSE inversion-recovery with real reconstruction sequences in visualizing the endolymphatic space, detecting endolymphatic hydrops, and discovering contrast permeability.
- Deep Learning of Time–Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma
The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.
- MELAS: Phenotype Classification into Classic-versus-Atypical Presentations
Recognizing different patterns in MELAS presentations will enable clinical and research care teams to better understand the natural history and prognosis of MELAS and identify the best candidates for specific therapeutic interventions.
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