Hotrakool, W. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2017) Towards Baseline-Independent Analysis of Compressive Sensed Functional Magnetic Resonance Image Data. In: Digital Signal Processing (DSP), 2017 22nd International Conference on. 2017 22nd International Conference on Digital Signal Processing (DSP), 23-25 Aug 2017, London, United Kingdom. IEEE
Abstract
The main task of Functional Magnetic Resonance Imaging (fMRI) is the localisation of brain activities, which depends on the detection of hemodynamic responses in the Blood Oxygenation-Level Dependent (BOLD) signal. While compressive sensing has been widely applied to improve the quality and resolution of MRI in general, its reconstruction noise overwhelms the small magnitude of hemodynamic responses. We propose a new reconstruction algorithm for the compressive sensing fMRI that exploits the temporal redundancy of the data, called Referenced Compressive Sensing, which works well in preserving fMRI analytical features. We also propose the use of the baseline-independent signal for analysis of reconstructed data. It is shown that the baseline-independent reconstructed data from Referenced Compressive Sensing is highly correlated to the lossless data, thus preserving more of the analytical features.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 May 2018 12:01 |
Last Modified: | 19 Dec 2022 13:49 |
Published Version: | https://doi.org/10.1109/ICDSP.2017.8096065 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICDSP.2017.8096065 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130526 |