Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study.

Vainio, Tuomas; Mäkelä, Teemu; Savolainen, Sauli; Kangasniemi, Marko

Vainio, Tuomas; Mäkelä, Teemu; Savolainen, Sauli; Kangasniemi, Marko.

Affiliation

Vainio T; HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340 (Haartmaninkatu 4), FI-00290, Helsinki, Finland. tuomas.j.vainio@helsinki.fi.
Mäkelä T; HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340 (Haartmaninkatu 4), FI-00290, Helsinki, Finland.
Savolainen S; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland.
Kangasniemi M; HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340 (Haartmaninkatu 4), FI-00290, Helsinki, Finland.

Eur Radiol Exp ; 5(1): 45, 2021 09 24.

Article in En | MEDLINE | ID: mdl-34557979

Subject(s)

Neural Networks, Computer; Pulmonary Embolism; Angiography; Feasibility Studies; Humans; Pulmonary Embolism/diagnostic imaging; Tomography, X-Ray Computed

Key words

Computed tomography angiography; Deep learning; Feasibility Studies; Neural networks (computer); Pulmonary embolism

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pulmonary Embolism / Neural Networks, Computer Type of study: Diagnostic_studies / Guideline / Prognostic_studies Limits: Humans Language: En Journal: Eur Radiol Exp Year: 2021 Document type: Article Affiliation country: Finland Country of publication: United kingdom

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