Abstract
Function magnetic resonance imaging (fMRI) data are typically contaminated by noise introduced by head motion, physiological noise, and thermal noise. To mitigate noise artifact in fMRI data, a variety of denoising methods have been developed by removing noise factors derived from the whole time series of fMRI data and therefore are not applicable to real-time fMRI data analysis. In the present study, we develop a generally applicable, deep learning based fMRI denoising method to generate noise-free realistic individual fMRI volumes (time points). Particularly, we develop a fully data-driven 3D convolutional encapsulated Long Short-Term Memory (3DConv-LSTM) approach to generate noise-free fMRI volumes regularized by an adversarial network that makes the generated fMRI volumes more realistic by fooling a critic network. The 3DConv-LSTM model also integrates a gate-controlled self-attention model to memorize short-term dependency and historical information within a memory pool. We have evaluated our method based on both task and resting state fMRI data. Both qualitative and quantitative results have demonstrated that the proposed method outperformed state-of-the-art alternative deep learning methods.
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Acknowledgement
Research reported in this study was partially supported by the National Institutes of Health under award number [R01MH120811 and R01EB022573]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Zhao, C., Li, H., Jiao, Z., Du, T., Fan, Y. (2020). A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_47
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DOI: https://doi.org/10.1007/978-3-030-59728-3_47
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