Article

Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke

2021-01-15

Abstract excerpt

<h4>Purpose</h4> In this study we investigate whether a Convolutional Neural Network (CNN) can generate clinically relevant parametric maps from CT perfusion data in a clinical setting of patients with acute ischemic stroke. <h4>Methods</h4> Training of the CNN was done on a subset of 100 perfusion data, while 15 samples were used as validation. All the data used for the training/validation of the network and to g...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7fdc20ed-6d38-511b-a698-0220ec2909c6
DOI: 10.1101/2021.01.13.21249757

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic strokeDOI 10.1101/2021.01.13.21249757Read

Clinically Scalable Deep Learning for Stroke Multicenter Validation of an Ultra-Efficient NCCT-Based Diagnostic FrameworkSemantic proximity Read

Automated Prediction of Ischemic Brain Tissue Fate from Multi-Phase CT-Angiography in Patients with Acute Ischemic Stroke Using Machine LearningSemantic proximity Read

Redefining Hemodynamic Imaging in Stroke: Perfusion Parameter Map Generation from TOF-MRA using Artificial IntelligenceSemantic proximity Read

Generating Synthetic MR Perfusion Maps from DWI and FLAIR in Acute Ischemic Stroke: Development and External Validation of a Deep Learning ModelSemantic proximity Read

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic strokeSemantic proximity Read

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and DiagnosticsSemantic proximity Read

Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular diseaseSemantic proximity Read

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome PredictionSemantic proximity Read

Machine Learning in Stroke Lesion Segmentation and Recovery Forecasting: A ReviewSemantic proximity Read

MAESTRO: A Public, Generalizable Model for Stroke Lesion Segmentation from T1 MRI Across the Recovery ContinuumSemantic proximity Read

Deep learning algorithms for automatic segmentation of acute cerebral infarcts on diffusion-weighted images: Effects of training data sample size, transfer learning, and data featuresSemantic proximity Read

An Open, Reproducible Gamma-Variate Pipeline for CT-Perfusion Time–Attenuation Curve Analysis, with Standardized (ASIST-Japan) Map VisualizationSemantic proximity Read