Real-time high-resolution functional magnetic resonance imaging with GPU parallel computations
Abstract/Contents
- Abstract
- Functional magnetic resonance imaging (fMRI) is a technique that enables non-invasive monitoring of brain activity by detecting changes in blood oxygenation levels. With recent advancements in high performance computing and MRI hardware, real-time fMRI has become possible and the spatiotemporal resolution of fMRI has been significantly improved. However, there are still many challenges for fMRI to achieve its full potential. First, because many basic real-time fMRI modules still uses a large portion of the available processing time, there is insufficient time for the integration of advanced real-time fMRI techniques. Second, current high-resolution fMRI techniques do not provide the resolution needed for imaging activity of small but critical brain regions, such as cortical layers and hippocampal sub-regions. Third, it is still not trivial to achieve the high-resolution and real-time fMRI at once because significant higher computation power is needed. To address these challenges, three projects were conducted and illustrated in this dissertation. In the first project, a high-throughput real-time fMRI system is designed on the graphics processing unit (GPU) to overcome computation barriers associated with reconstruction of non-uniformly sampled image, motion correction and statistical analysis. This system achieves an overall processing speed of 15.01 ms per 3D image, which is more than 49-fold faster than widely used software packages. The high processing speed also enables sliding window reconstruction, which improves the temporal resolution. With this ultra high speed fMRI system, integration of CS reconstruction for real-time and high spatiotemporal resolution fMRI becomes possible. The second project explores the feasibility of CS fMRI and demonstrates a High SPAtial Resolution compressed SEnsing (HSPARSE) fMRI method. HSPARSE fMRI enables a 6-fold spatial resolution improvement with contrast to noise ratio (CNR) increase and no loss of temporal resolution. A novel randomly under-sampled, variable density spiral data acquisition trajectory is designed to achieve an imaging speed acceleration factor of 5.3, which is 32 \% higher than previously reported CS fMRI methods. HSPARSE fMRI also achieves high sensitivity and low false positive rate. Importantly, its high spatial resolution enables localization of brain regions that cannot be resolved using the highest spatial resolution fully-sampled reconstruction. The third project combines the methods in the previous two into a real-time high-resolution CS fMRI system. A random stack of variable density spiral trajectory is first designed to achieve highly incoherent CS sampling and 3.2 times imaging speed acceleration. An optimized CS reconstruction algorithm using wavelet regularization is then implemented on GPU, which achieves a reconstruction speed of 605 ms per 3D image. This method also achieves a 4-fold spatial resolution improvement, with increased CNR, high sensitivity, low false positive rate and no loss of temporal resolution. Notably, this is the first system that achieves the real-time 3D non-uniformly sampled image CS fMRI reconstruction.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Fang, Zhongnan |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Lee, Jin Hyung |
| Thesis advisor | Lee, Jin Hyung |
| Thesis advisor | Nishimura, Dwight George |
| Thesis advisor | Pauly, John (John M.) |
| Advisor | Nishimura, Dwight George |
| Advisor | Pauly, John (John M.) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Zhongnan Fang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Zhongnan Fang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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