Quantifying the Statistical Impact of GRAPPA in fcMRI Data with a Real-Valued Isomorphism

The interpolation of missing spatial frequencies through the generalized auto-calibrating partially parallel acquisitions (GRAPPA) parallel magnetic resonance imaging (MRI) model implies a correlation is induced between the acquired and reconstructed frequency measurements. As the parallel image reconstruction algorithms in many medical MRI scanners are based on the GRAPPA model, this study aims to quantify the statistical implications that the GRAPPA model has in functional connectivity studies. The linear mathematical framework derived in the work of Rowe , 2007, is adapted to represent the complex-valued GRAPPA image reconstruction operation in terms of a real-valued isomorphism, and a statistical analysis is performed on the effects that the GRAPPA operation has on reconstructed voxel means and correlations. The interpolation of missing spatial frequencies with the GRAPPA model is shown to result in an artificial correlation induced between voxels in the reconstructed images, and these artificial correlations are shown to reside in the low temporal frequency spectrum commonly associated with functional connectivity. Through a real-valued isomorphism, such as the one outlined in this manuscript, the exact artificial correlations induced by the GRAPPA model are not simply estimated, as they would be with simulations, but are precisely quantified. If these correlations are unaccounted for, they can incur an increase in false positives in functional connectivity studies

Functional quantitative susceptibility mapping (fQSM)

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a powerful technique, typically based on the statistical analysis of the magnitude component of the complex time-series. Here, we additionally interrogated the phase data of the fMRI time-series and used quantitative susceptibility mapping (QSM) in order to investigate the potential of functional QSM (fQSM) relative to standard magnitude BOLD fMRI. High spatial resolution data (1 mm isotropic) were acquired every 3 seconds using zoomed multi-slice gradient-echo EPI collected at 7 T in single orientation (SO) and multiple orientation (MO) experiments, the latter involving 4 repetitions with the subject's head rotated relative to B0. Statistical parametric maps (SPM) were reconstructed for magnitude, phase and QSM time-series and each was subjected to detailed analysis. Several fQSM pipelines were evaluated and compared based on the relative number of voxels that were coincidentally found to be significant in QSM and magnitude SPMs (common voxels). We found that sensitivity and spatial reliability of fQSM relative to the magnitude data depended strongly on the arbitrary significance threshold defining “activated” voxels in SPMs, and on the efficiency of spatio-temporal filtering of the phase time-series. Sensitivity and spatial reliability depended slightly on whether MO or SO fQSM was performed and on the QSM calculation approach used for SO data. Our results present the potential of fQSM as a quantitative method of mapping BOLD changes. We also critically discuss the technical challenges and issues linked to this intriguing new technique

Design and Validation of an MR Conditional Upper Extremity Evaluation System to Study Brain Activation Patterns after Stroke

Stroke is the third leading cause of death and second most frequent cause of disability in the United States. Stroke rehabilitation methods have been developed to induce the cortical reorganization and motor-relearning that leads to stroke recovery. In this thesis, we designed and developed an MR conditional upper extremity reach and grasp movement evaluation system for the stroke survivors to study their kinematic performances in reach and grasp movement and the relationship between kinematic metrics and the recovery level measured by clinical assessment methods. We also applied the system into the functional MRI experiments to identify the ability to study motor performance with the system inside the scanner and the reach, grasp and reach-to-grasp movements related brain activation patterns. Our experiments demonstrates that ours system is an MR conditional system in the 3.0 Tesla magnetic field. It is able to measure the stroke survivors\u27 reach and grasp movement in terms of grasp aperture and elbow joint angles. We used the Mann Whitney U test to examine the significant metrics in each tasks and principle component analysis to decide the major metrics that are associated with the outcome. Then we discovered better recovery scores are associated with these major kinematic metrics such as larger maximal velocity, larger mean velocity, larger maximal movement angle, and longer time to peak velocity. Additional to these metrics, time to maximal angle, time to target and time to peak velocity could also be used as additional metrics to help predict the recovery and assess robot-assisted therapy and optimize task-oriented rehabilitation strategy. We also identified the movement related brain activations in the motor and sensory areas as well as cerebellum in both normal and stroke survivors

Determination Of Correlations Induced By The Sense And Grappa Pmri Models With An Application To Mri Rf Coil Design

Functional connectivity MRI is fast becoming a widely used non-invasive means of observing the connectivity between regions of the brain. In order to more accurately observe fluctuations in the blood oxygenation level of hemoglobin, parallel MRI reconstruction models such as SENSE and GRAPPA can be used to reduce data acquisition time, effectively increasing spatial and temporal resolution. However, the statistical implications of these models are not generally known or considered in the final analysis of the reconstructed data. In this dissertation, the non-biological correlations artificially induced by the SENSE and GRAPPA models are precisely quantified through the development of a real-valued isomorphism that represents each model in terms of a series of linear matrix operators. Using both theoretical and experimentally acquired functional connectivity data, these artificial correlations are shown to corrupt functional connectivity conclusions by incurring false positives, where regions of the brain appear to be correlated when they are not, and false negatives, where regions of the brain appear to be uncorrelated when they actually are. With a precise quantification of the artificial correlations induced by SENSE, a new cost function for optimizing the design of RF coil arrays has also been developed and implemented to generate more favorable magnetic fields for functional connectivity studies in specific brain regions. Images reconstructed with such arrays have an improved signal-to-noise ratio and a minimal SENSE induced correlation within the regions of interest, effectively improving the accuracy and reliability of functional connectivity studies