FMRI denoising and data analysis for medical applications
Since its development in the early 1990s, functional MRI has emerged as a useful tool to explore the functional behavior of the human brain. Image processing for fMRI data analysis has been playing a very important role for determining which parts of the brain are activated by different types of s...
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| 格式: | Final Year Project |
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2010
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sg-ntu-dr.10356-405892023-07-07T16:24:19Z FMRI denoising and data analysis for medical applications Singh, Rahul. Mohammed Yakoob Siyal School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Since its development in the early 1990s, functional MRI has emerged as a useful tool to explore the functional behavior of the human brain. Image processing for fMRI data analysis has been playing a very important role for determining which parts of the brain are activated by different types of stimuli. Functional magnetic resonance imaging (FMRI) data is severely contaminated by noise, in large part due to physiologic noise caused by respiratory and cardiac variations over time. This Final Year Project attempts to better characterize several noise correction and analysis techniques applied to FMRI data. Bachelor of Engineering 2010-06-16T09:08:05Z 2010-06-16T09:08:05Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40589 en Nanyang Technological University 88 p. application/pdf |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| language |
English |
| topic |
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics |
| spellingShingle |
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Singh, Rahul. FMRI denoising and data analysis for medical applications |
| description |
Since its development in the early 1990s, functional MRI has emerged as a useful tool to explore the functional behavior of the human brain. Image processing for fMRI data
analysis has been playing a very important role for determining which parts of the brain
are activated by different types of stimuli. Functional magnetic resonance imaging
(FMRI) data is severely contaminated by noise, in large part due to physiologic noise
caused by respiratory and cardiac variations over time. This Final Year Project attempts
to better characterize several noise correction and analysis techniques applied to FMRI data. |
| author2 |
Mohammed Yakoob Siyal |
| author_facet |
Mohammed Yakoob Siyal Singh, Rahul. |
| format |
Final Year Project |
| author |
Singh, Rahul. |
| author_sort |
Singh, Rahul. |
| title |
FMRI denoising and data analysis for medical applications |
| title_short |
FMRI denoising and data analysis for medical applications |
| title_full |
FMRI denoising and data analysis for medical applications |
| title_fullStr |
FMRI denoising and data analysis for medical applications |
| title_full_unstemmed |
FMRI denoising and data analysis for medical applications |
| title_sort |
fmri denoising and data analysis for medical applications |
| publishDate |
2010 |
| url |
http://hdl.handle.net/10356/40589 |
| _version_ |
1772829047451025408 |