About: Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

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About: Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
Creator	Ziekenhuis, St Boers, Anna Dippel, W Langezaal, Lucianne Lemmens, Robin Majoie, Charles Marquering, Henk Robben, David Roos, Yvo Suetens, Paul Van Der Lugt, Aad Van Oostenbrugge, Robert Van Zwam, Wim
source	ArXiv; Medline
abstract	CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.
has issue date	2018-12-06(xsd:dateTime)
bibo:doi	10.1016/j.media.2019.101589
bibo:pmid	31683091
has license	arxiv
sha1sum (hex)	819d2d679ca917f7a0ad1af31f55b5f721d459b3
schema:url	https://doi.org/10.1016/j.media.2019.101589
resource representing a document's title	Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
has PubMed identifier	31683091
schema:publication	Medical image analysis
resource representing a document's body	covid:819d2d679ca917f7a0ad1af31f55b5f721d459b3#body_text
is schema:about of	named entity 'native' named entity 'measurements' named entity 'multicenter' named entity 'insight' named entity 'metadata' named entity 'FREE' named entity 'ALLOW' named entity 'PREDICTION' named entity 'DIAGNOSIS' named entity 'ANALYSIS' named entity 'METADATA' named entity 'IMAGES' named entity 'DECONVOLUTION' named entity 'PREDICTED' named entity 'CT PERFUSION' named entity 'FINAL' named entity 'Perfusion' named entity 'predict' named entity 'measurements' named entity 'native' named entity 'learns' named entity 'effectively' named entity 'prediction' named entity 'ablation' named entity 'tissue' named entity 'CTP' named entity 'perfusion' named entity 'perfusion' named entity 'perfusion' named entity 'infarct' named entity 'deconvolved' named entity 'Tikhonov regularization' named entity 'acute stroke' named entity 'DWI' named entity 'contrast agent' named entity 'perfusion' named entity 'downsampled' named entity 'edema' named entity 'lesion' named entity 'infarct' named entity 'follow-up' named entity 'perfusion' named entity 'activation function' named entity 'MR CLEAN' named entity 'voxel' named entity 'neuroimaging' named entity 'deconvolution' named entity 'linear relationship' named entity 'cerebral blood flow' named entity 'perfusion' named entity 'tPA' named entity 'ischemic' named entity 'infarct' named entity 'neural network' named entity 'Elastix' named entity 'infarct' named entity 'blood supply to the brain' named entity 'metadata' named entity 'upsampling' named entity 'isotropic' named entity 'perfusion' named entity 'follow-up' named entity 'deep neural networks' named entity 'contrast agent' named entity 'SVD' named entity 'infarct' named entity 'Ischemic stroke' named entity 'impulse response function' named entity 'infarct' named entity 'time series'

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