abstract: Dynamic susceptibility contrast MRI (DSC-MRI) is a powerful tool used to quantitatively measure parameters related to blood flow and volume in the brain. The technique is known as a “bolus-tracking” method and relies upon very fast scanning to accurately measure the flow of contrast agent into and out of a region of interest. The need for high temporal resolution to measure contrast agent dynamics limits the spatial coverage of perfusion parameter maps which limits the utility of DSC-perfusion studies in pathologies involving the entire brain. Typical clinical DSC-perfusion studies are capable of acquiring 10-15 slices, generally centered on a known lesion or pathology. The methods developed in this work improve the spatial coverage of whole-brain DSC-MRI by combining a highly efficient 3D spiral k-space trajectory with Generalized Autocalibrating Partial Parallel Acquisition (GRAPPA) parallel imaging without increasing temporal resolution. The proposed method is capable of acquiring 30 slices with a temporal resolution of under 1 second, covering the entire cerebrum with isotropic spatial resolution of 3 mm. Additionally, the acquisition method allows for correction of T1-enhancing leakage effects by virtue of collecting two echoes, which confound DSC perfusion measurements. The proposed DSC-perfusion method results in high quality perfusion parameter maps across a larger volume than is currently available with current clinical standards, improving diagnostic utility of perfusion MRI methods, which ultimately improves patient care. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2017

abstract: Functional magnetic resonance imaging (fMRI) is used to study brain activity due to stimuli presented to subjects in a scanner. It is important to conduct statistical inference on such time series fMRI data obtained. It is also important to select optimal designs for practical experiments. Design selection under autoregressive models have not been thoroughly discussed before. This paper derives general information matrices for orthogonal designs under autoregressive model with an arbitrary number of correlation coefficients. We further provide the minimum trace of orthogonal circulant designs under AR(1) model, which is used as a criterion to compare practical designs such as M-sequence designs and circulant (almost) orthogonal array designs. We also explore optimal designs under AR(2) model. In practice, types of stimuli can be more than one, but in this paper we only consider the simplest situation with only one type of stimuli. / Dissertation/Thesis / Masters Thesis Statistics 2017

<p> The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-processing methods is used in three machine learning algorithms namely: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and k-Nearest Neighbors (k-nn) to recognize disease patterns and classify the diseases. While neurodegenerative diseases and psychiatric diseases overlap with a mix of diseases and resulted in fairly moderate classification, the classification between Alzheimer's disease and Cerebrovascular Small Vessel diseases yielded good results with an accuracy of up to 73.7%.</p><p>

The conventional method for quantifying sleep is through the use of Polysomnography (PSG) and a trained human sleep scorer by observing and evaluating the output in 30-second epochs. A PSG device can be rather invasive to one’s regular sleep pattern and therefore can potentially result in irregular sleep patterns. Furthermore, human sleep scoring classification by a trained expert can be rather time consuming and subject to inter/intra rater variability. Nevertheless, human sleep scoring with PSG still remains the gold-standard for sleep measuring and classification for the diagnosis disorders related to sleep. The present pilot study explores the possibility of using a wearable device known as a ByteFlies Sensor Dot to measure signal activity from an individual during a night’s sleep. This validation study focuses on the signal capture of alpha frequency band through a phenomenon known as “the Berger effect.” Participants will be asked to open and close their eyes while being connected to the gold standard PSG device and exploratory ByteFlies Sensor Dot device. The resulting alpha signals will be identified with a machine learning algorithm for cross comparison and analysis. In conclusion, the validation study will discuss methods to improve on the measuring of EEG and sleep stage scoring with the ByteFlies Sensor Dot for sleep monitoring and sleep disorder diagnosis.

With the advent of large-scale, multi-site imaging studies, there is a growing need for magnetic resonance imaging (MRI) pulse sequences and matching computer algorithms that generate accurate and harmonious quantitative information descriptive of the examined population. Algorithms for quantitative MRI (qMRI) are of particular importance, as rich information related to tissue structure and composition can be derived without ionizing radiation. With this thesis work, a multiparametric (MP) qMRI image processing pipeline is applied to the brains of adolescents born extremely preterm (EP), who experience high incidence of neurologic disability, and white and gray matter (WM, GM) injuries. Harmonized MP-qMRI parameters served as biological markers of neurodevelopment, and were implemented in computational frameworks for tissue segmentation, and characterization of macromolecular and metal components of the neuroarchitecture. This work first describes the triple turbo spin echo (Triple-TSE) MRI pulse sequence and multiple aspects of a highly automated MP-qMRI image processing pipeline. The primary MP-qMRI parameters of proton density, and longitudinal and transverse relaxation times were calculated according to the Bloch equation model of the Triple-TSE and harmonized across multiple MRI scanners. Next, the WM microstructure’s organization was studied with synthetic MRI and mapping spatial entropy (SE). The distribution of these parameters and their associations with SE density distinguished atypically versus neurotypically developing adolescents. In the second part, this work describes a deep GM segmentation method. A two-channel dual-clustering algorithm was applied in parallel with connected component theory to separate cortical and deep gray matter. For every voxel, the similarity of the three MP-qMRI parameters to those of a predefined imaging cluster was interrogated. In this way, the deep GM can be isolated from the in toto brain without additional pulse sequences for structural MRI. In the final part of this work, an MR relaxation theoretical framework was constructed to derive the distribution of macromolecules and metal deposits in the brain. These microstructural components follow interrelated pathways and play roles in neural signal transmission and normal brain function. Using a fast exchange relaxation model and synthetic MRI, linear associations between the concentrations of these components were identified in deep GM and WM structures. / 2023-05-23T00:00:00Z

The purpose of this experiment is to observe the diffusion coefficient of agarose solution as a function of temperature during the process of gel formation. The focus is on understanding how liquids become semi organized or semi-solid by monitoring dynamically with diffusion quantitative MRI the liquid-to-gel phase transition of pure agarose as a function of gel concentration. Four different concentrations of agarose solutions were allowed to cool down while scanning dynamically with 3 T MRI scanner (Achieva, Philips Medical Systems, Cleveland, OH) with diffusion qMRI, 70 dynamics with 56 seconds dynamic time. The 1%, 2%, 3%, and 4% agarose solutions were prepared by pouring agarose powder in distilled water and heating the solutions until they reached the boiling point ~80°C and completely dissolved. Then, scanning the phantoms dynamically as these cooled down immediately after preparation. A single axial slice diffusion-weighted-imaging turbo-spin-echo (DWI-TSE) pulse sequence was used. The diffusion versus time (temperature) curves of different agarose solutions show a distinct phase transition region characterized by a hump of increased diffusion. The diffusion coefficient as a function of time (temperature) curves of all the four concentrations shows similar behaviors with a phase transition characterized by a hump shaped at about 24 minutes at which time the gelation phase transition begins. These results may be useful for testing theoretical models of the NMR diffusion coefficient properties during liquids transforming to semi organized or semi solid

This study has been conducted for the purpose of investigating the systematic dose reduction of rectum and neurovascular bundles (NVBs) during treatment planning of the CyberKnifeTM prostate SBRT using CT-Based volumes versus MRI-based volumes. Three prostate cancer patients were Planned for the CyberKnifeTM prostate SBRT and they underwent computed tomography (CT) and magnetic resonance imaging (MRI) preplanning exams. The patients were positioned during both exams using an immobilizing device. A radiation oncologist and a radiologist delineated the prostate gland, intra-prostatic and peri-prostatic structures, and pelvic organs of interest in both CT and MRI images. The CT and MRI images were fused based on fuducial markers to accurately align the prostate. Radiation Therapy Oncology protocol RTOG 0938 was followed to meet the target volume (prostate plus margin) dose coverage requirement, and dose-volume constraints for organs at risk, including rectum, bladder, femoral heads, penile bulb, urethra, skin and NVBs. Radiation dose volume parameters were recorded for both volumes and compared. The preliminary result shows that the CT-based volumes were generally larger than MRI-based volumes of the prostate. Therefore, the CT-based volumes resulted in less accurate treatment planning and dose delivery to radiosensitive structures.

Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used. / 2020-02-20T00:00:00Z

With the continual growth of the average age of the population and the global rise in obesity, it is important to investigate age related cognitive decline and its many related risk factors. Alzheimer’s Disease is the most common cause of dementia and has been linked to another inflammation-associated disease, atherosclerosis. In our lab’s recent findings, we have demonstrated this mechanistic link between inflammation and atherosclerosis with specialized pro-resolving mediators, such as lipoxin and resolvin found in Omega-3 fatty acids. Here we investigated the viability of our rabbit model of atherosclerosis as a model of Alzheimer’s Disease, in an effort to eventually test the impact of inflammation resolution as a treatment to AD. We developed and optimized an MRI protocol as a way to demonstrate and quantify the effect of vascular inflammation on a brain ex vivo in first a murine model of arterial stiffness. We then applied the refined protocol for use on our rabbit model of atherosclerosis. The mouse brains induced with arterial stiffness showed a significant increase of cerebral microbleeds (indicators of cerebral amyloid angiopathy). Some of the rabbit brains used for this study were found to be preserved for too long but found good images in recently harvested and perfused rabbit brains. While the our findings are currently inconclusive, this thesis proposes a novel method for investigating the mechanistic and synergistic link between inflammation, atherosclerosis, and Alzheimer’s Disease.

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