Improving Data Quality for High Resolution Functional MRI in Cognitive Neuroscience Applications
type: CreativeWork
description: Since the first successful Magnetic Resonance Imaging (MRI) image was produced by Paul Lauterbur in 1973, the field of MRI has been improving by leaps and bounds. The number of MRI and functional MRI (fMRI) papers have sky rocketed over the last decade, alongside with advancements in MRI field strength and techniques. In this thesis, I explore various methods for improving data quality for high resolution fMRI in 3T and 7T MRI scanners. Firstly, I studied the effect of Prospective Motion Correction (PMC) on 3T data using a simple visual paradigm. In contrast to most conventional techniques that use retrospective motion correction (RMC), PMC collects real-time motion data and uses it to update the acquisition field of view prior to each radiofrequency (RF) pulse. This allows for the correction of spin-history effects and intra-volume distortions. In this study, I utilized a secondary optical camera in the bore of the scanner to track a Moiré phase marker attached to the participant via a custom-moulded dental mouthpiece. I demonstrated that the camera is capable of accurately tracking the participant’s head motion. While simple metrics such as temporal signal-to-noise ratio (tSNR) and functional contrast-to-noise ratio (fCNR) showed no difference between the two methods, more complex analysis such as the Linear Discriminant Contrast (LDC) showed that the PMC data was indeed cleaner than the RMC data for higher resolution data. Next, I compared the sensitivity of two multi-voxel pattern analysis (MVPA) methods, Support Vector Machines (SVM) and Linear Discriminant Contrast (LDC). MVPA attempts to capture the relationship between the spatial fMRI activity and the experimental manipulations by treating it as a supervised learning problem. This is a promising technique that can capture spatial activation patterns that are lost in univariate analysis. I demonstrated through both actual fMRI data and computer simulations that LDC is a better MVPA metric than SVM. This agre…
abstract: Since the first successful Magnetic Resonance Imaging (MRI) image was produced by Paul Lauterbur in 1973, the field of MRI has been improving by leaps and bounds. The number of MRI and functional MRI (fMRI) papers have sky rocketed over the last decade, alongside with advancements in MRI field strength and techniques. In this thesis, I explore various methods for improving data quality for high resolution fMRI in 3T and 7T MRI scanners. Firstly, I studied the effect of Prospective Motion Correction (PMC) on 3T data using a simple visual paradigm. In contrast to most conventional techniques that use retrospective motion correction (RMC), PMC collects real-time motion data and uses it to update the acquisition field of view prior to each radiofrequency (RF) pulse. This allows for the correction of spin-history effects and intra-volume distortions. In this study, I utilized a secondary optical camera in the bore of the scanner to track a Moiré phase marker attached to the participant via a custom-moulded dental mouthpiece. I demonstrated that the camera is capable of accurately tracking the participant’s head motion. While simple metrics such as temporal signal-to-noise ratio (tSNR) and functional contrast-to-noise ratio (fCNR) showed no difference between the two methods, more complex analysis such as the Linear Discriminant Contrast (LDC) showed that the PMC data was indeed cleaner than the RMC data for higher resolution data. Next, I compared the sensitivity of two multi-voxel pattern analysis (MVPA) methods, Support Vector Machines (SVM) and Linear Discriminant Contrast (LDC). MVPA attempts to capture the relationship between the spatial fMRI activity and the experimental manipulations by treating it as a supervised learning problem. This is a promising technique that can capture spatial activation patterns that are lost in univariate analysis. I demonstrated through both actual fMRI data and computer simulations that LDC is a better MVPA metric than SVM. This agre…
author: Pei, Huang
language: en
keywords: fMRI, Ultra High Field MRI, Prospective Motion Correction, BBR realignment, Linear Discriminant Contrast, Correcting superficial bias
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/82cb8f9b-dc69-44b9-9b4c-90ea4eab8abd/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2019-09-20
date modified: 2026-07-22T22:24:14Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.49828