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Deep learning-based motion artifact removal in functional near-infrared spectroscopy
Gao, Yuanyuan Chao, Hanqing Cavuoto, Lora Yan, Pingkun Kruger, Uwe Norfleet, Jack E. Makled, Basiel A. Schwaitzberg, Steven D. De, Suvranu Intes, Xavier R.
Gao, Yuanyuan
Chao, Hanqing
Cavuoto, Lora
Yan, Pingkun
Kruger, Uwe
Norfleet, Jack E.
Makled, Basiel A.
Schwaitzberg, Steven D.
De, Suvranu
Intes, Xavier R.
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Issue Date
2022-04-23
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Article
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Keywords
Functional near-infrared spectroscopy,Motion artifact,Deep learning,Denoising autoencoder
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Citation
Yuanyuan Gao, Hanqing Chao, Lora Cavuoto, Pingkun Yan, Uwe Kruger, Jack E. Norfleet, Basiel A. Makled, Steven D. Schwaitzberg, Suvranu De, and Xavier R. Intes "Deep learning-based motion artifact removal in functional near-infrared spectroscopy," Neurophotonics 9(4), 041406 (23 April 2022). https://doi.org/10.1117/1.NPh.9.4.041406
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters.
Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal.
Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences.
Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency.
Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.
Table of Contents
Description
Publisher
SPIE
Journal
Neurophotonics
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Series
Digital Collection
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Archival Collection
PubMed ID
ISSN
2329-423X
2329-4248
2329-4248