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A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data

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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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References

  1. Caballero-Gaudes, C., Reynolds, R.C.: Methods for cleaning the BOLD fMRI signal. Neuroimage154, 128–149 (2017)

    Article  Google Scholar 

  2. Murphy, K., Birn, R.M., Bandettini, P.A.: Resting-state fMRI confounds and cleanup. Neuroimage80, 349–359 (2013)

    Article  Google Scholar 

  3. Power, J.D., Mitra, A., Laumann, T.O., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage84, 320–341 (2014)

    Article  Google Scholar 

  4. Ciric, R., et al.: Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage154, 174–187 (2017)

    Article  Google Scholar 

  5. Raut, R.V., Mitra, A., Snyder, A.Z., Raichle, M.E.: On time delay estimation and sampling error in resting-state fMRI. Neuroimage194, 211–227 (2019)

    Article  Google Scholar 

  6. Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A.: The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage40, 644–654 (2008)

    Article  Google Scholar 

  7. Tijssen, R.H., Jenkinson, M., Brooks, J.C., Jezzard, P., Miller, K.L.: Optimizing RetroICor and RetroKCor corrections for multi-shot 3D FMRI acquisitions. NeuroImage84, 394–405 (2014)

    Article  Google Scholar 

  8. Behzadi, Y., Restom, K., Liau, J., Liu, T.T.: A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage37, 90–101 (2007)

    Article  Google Scholar 

  9. Kay, K., Rokem, A., Winawer, J., Dougherty, R., Wandell, B.: GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Front. Neurosci.7, 247 (2013)

    Article  Google Scholar 

  10. Salimi-Khorshidi, G., Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L., Smith, S.M.: Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage90, 449–468 (2014)

    Article  Google Scholar 

  11. Pruim, R.H., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J.K., Beckmann, C.F.: ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage112, 267–277 (2015)

    Article  Google Scholar 

  12. Kam, T.-E., et al.: A deep learning framework for noise component detection from resting-state functional MRI. In: Shen, D., Liu, T., Peters, Terry M., Staib, Lawrence H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 754–762. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-32248-9_84

    Chapter  Google Scholar 

  13. Yang, Z., Zhuang, X., Sreenivasan, K., Mishra, V., Curran, T., Cordes, D.: A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory. Med. Image Anal.60, 101622 (2020)

    Article  Google Scholar 

  14. Yan, Y., et al.: Reconstructing lost BOLD signal in individual participants using deep machine learning. bioRxiv 808089 (2019)

    Google Scholar 

  15. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  16. Berthelot, D., Raffel, C., Roy, A., Goodfellow, I.: Understanding and improving interpolation in autoencoders via an adversarial regularizer. arXiv preprintarXiv:1807.07543 (2018)

  17. Glasser, M.F., et al.: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage80, 105–124 (2013)

    Article  Google Scholar 

  18. Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., Anderson, J.S., Assaf, M., Bookheimer, S.Y., Dapretto, M.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry19, 659–667 (2014)

    Article  Google Scholar 

  19. Wang, Y., Jiang, L., Yang, M.-H., Li, L.-J., Long, M., Fei-Fei, L.: Eidetic 3D LSTM: a model for video prediction and beyond. In: International Conference on Learning Representations (2018)

    Google Scholar 

  20. Esteban, O., et al.: fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods16, 111–116 (2019)

    Article  Google Scholar 

  21. Li, H., Fan, Y.: Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks. NeuroImage202, 116059 (2019)

    Article  Google Scholar 

  22. Li, H., Fan, Y.: Identification of temporal transition of functional states using recurrent neural networks from functional MRI. In: Frangi, Alejandro F., Schnabel, Julia A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 232–239. Springer, Cham (2018).https://doi.org/10.1007/978-3-030-00931-1_27

    Chapter  Google Scholar 

  23. Li, H., Fan, Y.: Brain decoding from functional MRI using long short-term memory recurrent neural networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 320–328. Springer, Cham (2018).https://doi.org/10.1007/978-3-030-00931-1_37

    Chapter  Google Scholar 

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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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Authors and Affiliations

  1. Center for Biomedical Image Computing and Analysis, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Chongyue Zhao, Hongming Li, Zhicheng Jiao, Tianming Du & Yong Fan

Authors
  1. Chongyue Zhao

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  2. Hongming Li

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  3. Zhicheng Jiao

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  4. Tianming Du

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  5. Yong Fan

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Corresponding author

Correspondence toYong Fan.

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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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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