fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser

• URL: http://arxiv.org/abs/2009.12602v1
• Date: Sat, 26 Sep 2020 13:56:41 GMT
• Title: fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser
• Authors: David Calhas and Rui Henriques
• Abstract summary: Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications. 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. We propose a new imputation method consisting on two major steps: spatial-dependent signal imputation and time-dependent regularization of the imputed signal.
• Score: 0.913755431537592
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. 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.

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