Deep learning-based motion artifact removal in functional near-infrared spectroscopy.

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第一作者： Yuanyuan,Gao

第一单位： Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States.

作者： Yuanyuan,Gao ^[1] ; Hanqing,Chao ^[2] ; Lora,Cavuoto ^[3] ; Pingkun,Yan ^[1] ; Uwe,Kruger ^[2] ; Jack E,Norfleet ^[1] ; Basiel A,Makled ^[2] ; Steven,Schwaitzberg ^[4] ; Suvranu,De ^[5] ; Xavier,Intes ^[6]

作者单位： Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States. ^[1] Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States. ^[2] University at Buffalo, Department of Industrial and Systems Engineering, Buffalo, New York, United States. ^[3] U.S. Army Combat Capabilities Development Command-Soldier Center, Orlando, Florida, United States. ^[4] SFC Paul Ray Smith Simulation and Training Technology Center, Orlando, Florida, United States. ^[5] Medical Simulation Research Branch, Orlando, Florida, United States. ^[6] University at Buffalo, Department of Surgery, Buffalo, New York, United States. ^[7]

关键词 deep learning denoising autoencoder functional near-infrared spectroscopy motion artifact

DOI 10.1117/1.NPh.9.4.041406

PMID 35475257

发布时间 2022-10-07

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Neurophotonics

2022年9卷4期

041406页

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Deep learning-based motion artifact removal in functional near-infrared spectroscopy.

Neurophotonics

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Deep learning-based motion artifact removal in functional near-infrared spectroscopy.

Neurophotonics

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Neurophotonics

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