Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction
Resting-state functional magnetic resonance imaging (R-fMRI) applications can entail a higher temporal-sampling rate that trades off spatial resolution, thereby challenging effective scientific studies. To enable higher spatial resolution, current schemes speedup per-timeframe scanning by reconstruc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Open Journal of Signal Processing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9501974/ |
| _version_ | 1818607962512424960 |
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| author | Prachi H. Kulkarni S. N. Merchant Suyash Awate |
| author_facet | Prachi H. Kulkarni S. N. Merchant Suyash Awate |
| author_sort | Prachi H. Kulkarni |
| collection | DOAJ |
| description | Resting-state functional magnetic resonance imaging (R-fMRI) applications can entail a higher temporal-sampling rate that trades off spatial resolution, thereby challenging effective scientific studies. To enable higher spatial resolution, current schemes speedup per-timeframe scanning by reconstruction from simultaneous multislice (SMS) magnetic resonance imaging (MRI) with k-space undersampling (sometimes temporal undersampling), while using prior models on the signal. We propose a novel <italic>algorithmic framework</italic> to reconstruct R-fMRI (SMS with controlled aliasing) that has, <italic>both</italic>, k-space undersampling and temporal undersampling. We propose a <italic>coupled</italic> spatiotemporal sparse model, incorporating (i) a robust spatially-regularized temporal-dictionary prior and (ii) a spatiotemporal wavelet prior, which we fit efficiently using <italic>variational Bayesian expectation maximization with nested minorization</italic> (VBEMNM). We show that our framework has the potential to enable higher spatial resolution without increasing scan time in R-fMRI that has inherently weak signals and is therefore prone to large physiological fluctuations, acquisition noise, and imaging artifacts. Qualitative and quantitative evaluation on retrospectively undersampled brain R-fMRI shows that estimates of resting-state networks from our framework and the boost in temporal stability given by our framework compares favourably to existing methods for R-fMRI reconstruction. |
| first_indexed | 2024-12-16T14:35:05Z |
| format | Article |
| id | doaj.art-56dcf866efa141ebaa71e570764b72ac |
| institution | Directory Open Access Journal |
| issn | 2644-1322 |
| language | English |
| last_indexed | 2024-12-16T14:35:05Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj.art-56dcf866efa141ebaa71e570764b72ac2022-12-21T22:28:07ZengIEEEIEEE Open Journal of Signal Processing2644-13222021-01-01238339510.1109/OJSP.2021.31007519501974Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI ReconstructionPrachi H. Kulkarni0https://orcid.org/0000-0002-5750-642XS. N. Merchant1https://orcid.org/0000-0002-9119-6795Suyash Awate2https://orcid.org/0000-0002-4945-9539Department of Electrical Engineering, IIT Bombay, Mumbai, IndiaDepartment of Electrical Engineering, IIT Bombay, Mumbai, IndiaDepartment of Computer Science and Engineering, IIT Bombay, Mumbai, IndiaResting-state functional magnetic resonance imaging (R-fMRI) applications can entail a higher temporal-sampling rate that trades off spatial resolution, thereby challenging effective scientific studies. To enable higher spatial resolution, current schemes speedup per-timeframe scanning by reconstruction from simultaneous multislice (SMS) magnetic resonance imaging (MRI) with k-space undersampling (sometimes temporal undersampling), while using prior models on the signal. We propose a novel <italic>algorithmic framework</italic> to reconstruct R-fMRI (SMS with controlled aliasing) that has, <italic>both</italic>, k-space undersampling and temporal undersampling. We propose a <italic>coupled</italic> spatiotemporal sparse model, incorporating (i) a robust spatially-regularized temporal-dictionary prior and (ii) a spatiotemporal wavelet prior, which we fit efficiently using <italic>variational Bayesian expectation maximization with nested minorization</italic> (VBEMNM). We show that our framework has the potential to enable higher spatial resolution without increasing scan time in R-fMRI that has inherently weak signals and is therefore prone to large physiological fluctuations, acquisition noise, and imaging artifacts. Qualitative and quantitative evaluation on retrospectively undersampled brain R-fMRI shows that estimates of resting-state networks from our framework and the boost in temporal stability given by our framework compares favourably to existing methods for R-fMRI reconstruction.https://ieeexplore.ieee.org/document/9501974/R-fMRIreconstructionSMS with CAIPIjoint k-t undersamplingcoupled dictionary and waveletsrobust |
| spellingShingle | Prachi H. Kulkarni S. N. Merchant Suyash Awate Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction IEEE Open Journal of Signal Processing R-fMRI reconstruction SMS with CAIPI joint k-t undersampling coupled dictionary and wavelets robust |
| title | Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction |
| title_full | Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction |
| title_fullStr | Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction |
| title_full_unstemmed | Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction |
| title_short | Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction |
| title_sort | dictionary wavelet model with nested minorized vb em for sms caipi r fmri reconstruction |
| topic | R-fMRI reconstruction SMS with CAIPI joint k-t undersampling coupled dictionary and wavelets robust |
| url | https://ieeexplore.ieee.org/document/9501974/ |
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