fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser
This work addresses missing data issues in fMRI for scientific and clinical applications, representing an incremental improvement in imputation techniques.
The paper tackles the problem of missing values in fMRI data, which are common due to artifacts or sub-optimal imaging, by proposing a new imputation method that combines spatial-dependent imputation and time-dependent regularization using a novel deep learning layer and recurrent tuning, resulting in improved robustness compared to state-of-the-art alternatives.
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.