Doctor of Philosophy
Chronik, Blaine A.
Magnetic resonance imaging (MRI) has proven to be a clinically valuable tool that can produce anatomical and functional images with improved soft tissue contrast compared to other imaging modalities. There has recently been a surge in low- and mid-field scanners due to hardware developments and innovative acquisition techniques. These compact scanners are accessible, offer reduced siting requirements and can be made operational at a reduced cost.
This thesis aims to implement blood-oxygen-level-dependent (BOLD) resting-state functional MRI (fMRI) at such a mid-field point-of-care scanner. The availability of this technique can be beneficial to get neurological information in cases of traumatic brain injury, stroke, epilepsy, and dementia. This technique was previously not implemented at low- and mid-field since signal-to-noise ratio and the contrast scale with field strength.
Studies were conducted to gauge the performance of an independent component analysis (ICA) based platform (GraphICA) to analyze artificially added noisy resting state functional data previously collected with a 3T scanner. This platform was used in later chapters to preprocess and perform functional connectivity studies with data from a mid-field scanner.
A single echo gradient echo echoplanar imaging (GE-EPI) sequence is typically used for BOLD-based fMRI. Task-based fMRI experiments were performed with this sequence to gauge the feasibility of this technique on a mid-field scanner. Once the feasibility was established, the sequence was further optimized to suit mid-field scanners by considering all the imaging parameters.
Resting-state experiments were conducted with an optimized single echo GE-EPI sequence with reduced dead time on a mid-field scanner. Temporal and image signal-to-noise ratio were calculated for different cortical regions. Along with that, functional connectivity studies and identification of resting-state networks were performed with GraphICA which demonstrated the feasibility of this resting-state fMRI at mid-field. The reliability and repeatability of the identified networks were assessed by comparing the networks identified with 3T data.
Resting-state experiments were conducted with a multi-echo GE-EPI sequence to use the dead time due to long T2* at mid-field effectively. Temporal signal-to-noise was calculated for different cortical regions. Along with that, functional connectivity studies and identification of resting-state networks were performed with GraphICA which demonstrated the feasibility of this resting-state fMRI at mid-field.
Summary for Lay Audience
Magnetic resonance imaging could provide clinical value in point-of-care imaging such as in the emergency department or in the intensive care unit. These scanners mostly operate at low- to mid-field range which was previously associated with poor performance. Recently there has been a surge of such scanners with high-performance components, and improved acquisition and reconstruction methods enabling techniques that were previously not implemented. One such technique is functional MRI which investigates brain function using the changing concentration of blood oxygen in the brain. This thesis focuses on implementing this technique on a specialized mid-field scanner built for head imaging.
The second chapter of this thesis evaluates the performance of the resting state data analysis platform used throughout this thesis. To do so, higher field data with additional noise was provided to the platform. This was done specifically because the data expected from the mid-field scanner was potentially noisy compared to higher-field functional data. The results from this chapter suggest the detection of resting-state networks from higher field functional data with additional noise.
The third chapter of this thesis focuses on optimizing the gradient echo planar imaging sequence used to perform functional MRI at this mid-field scanner. The T2* contrasts at this mid-field scanner for gray and white matter were measured. Quantities such as signal-to-noise ratio and temporal signal-to-noise ratio were used to calculate the physiological-to-thermal noise ratio and contrast efficiency with such scanners.
The fourth chapter focuses on implementing the optimized sequence for resting-state functional studies on a mid-field scanner. Functional connectivities within resting state networks were detected using the independent component based analysis platform described before.
The fifth chapter focuses on using multi-echo EPI for resting-state studies on this scanner. To ensure the effective use of long T2* and short T1 available at mid-field strength along with the high slew rate gradient multi-echo sequence was chosen. The results from this study suggest increased SNR and the potential for increased functional connectivity detection with further optimization.
Overall, this thesis aims to prove the feasibility of functional MRI both task-based and resting-state at these modern mid-field scanners.
Halder, Arjama, "Feasibility of functional MRI on point-of-care MR platforms" (2023). Electronic Thesis and Dissertation Repository. 9663.