Masters Applied Projects
Masters Applied Projects
Project 17
Project 17
Analysis of Data Collected for Quantitative Mapping of Brain Tissue Oxygenation Around Neural Interfaces
Student:
Umu Jalloh
Mentors:
Dr. Scott Beeman, PhD – Arizona State University, SBHSE
Dr. Sung-Min Sohn, PhD - Arizona State University, SBHSE
Dr. Vikram Kodibagkar, PhD - Arizona State University, SBHSE
YouTube Link:
View the video link below before joining the zoom meeting
Zoom Link:
https://asu.zoom.us/j/81045679109
Abstract
Hypoxia is defined as a lack of oxygen in the body. It aids the progression of diseases like cancer, stroke, traumatic injuries, and diabetes. Despite the important need, hypoxia research is impeded by the difficulty of detecting tissue oxygenation (pO2) distribution within the tissue. Proton imaging of siloxanes to map tissue oxygenation levels (PISTOL) is an MRI-based oximetry technique that offers advantages over standard pO2 measurement methods like Clark electrodes and fiber optic probes in terms of low invasiveness and the ability to create pO2 maps of deep-seated tissues. In the PISTOL approach, siloxanes are used as an MRI pO2 probe. The spin-lattice relaxation rate (R1) of the protons in the siloxane methyl group is related to the surrounding pO2 level. Thus, obtaining the R1 for a specific location can be used to calculate the pO2 of that location. When looking into a small number of siloxanes, the Signal-to-Noise ratio (SNR) decreases leading to unreliable R1 estimation. Therefore, in this project, it is hypothesized that filtered data produces a better SNR as compared to unfiltered data. Analysis of data collected using computational modeling in conjunction with PISTOL technique to perform pO2 mapping on implant sites of neuro sensors and three-dimensional (3D) cell model to obtain a quantitative spatiotemporal pO2 map within the tissue for both filtered and unfiltered dataset was performed. The brain electrodes and the 3D cell culture model are used here as a "platform" to test if filtering the kspace improves the SNR and reliability of R1 estimation.