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fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 12721)

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications. As with any widely used imaging modality, there is a need to ensure the quality of the same, with missing values being highly frequent due to the presence of artifacts or sub-optimal imaging resolutions. Our work focus on missing values imputation on multivariate signal data. To do so, a new imputation method is proposed consisting on two major steps: spatial-dependent signal imputation and time-dependent regularization of the imputed signal. A novel layer, to be used in deep learning architectures, is proposed in this work, bringing back the concept of chained equations for multiple imputation [26]. Finally, a recurrent layer is applied to tune the signal, such that it captures its true patterns. Both operations yield an improved robustness against state-of-the-art alternatives. The code is made available on Github.

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Notes

  1. 1.

    \(\psi , \mu , \varphi \) and \(\iota \) values selected for simplicity sake, not necessarily resembling fMRI values.

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Acknowledgments

This work was supported by national funds through Fundação para a Ciência e Tecnologia (FCT), for the Ph.D. Grant DFA/BD/5762/2020, ILU project DSAIPA/DS/0111/2018 and INESC-ID pluriannual UIDB/50021/2020.

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Correspondence to David Calhas .

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Calhas, D., Henriques, R. (2021). fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-77211-6_3

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