The proposed technologies effectively address a significant shortcoming in current MRI protocols, that is the incapability of existing protocols to acquire a complete set of high-resolution, artifact-free, multi- contrast and quantitative MR images from challenging patients (e.g., Parkinson?s disease (PD) patients; stroke patients; pediatric populations) within clinically-feasible time. Here we will develop and integrate multidisciplinary approaches to maximize the translatability of advanced MRI technologies to clinical uses for challenging patients.
Aim 1 A) We will incorporate fast scan strategies, motion-correction and distortion- correction modules into our recently developed multiplexed sensitivity encoded (MUSE) DTI and fMRI in a novel way, to enable high-resolution connectivity mapping (at 0.85 mm isotropic resolution, in contrast to 1.5mm to 4mm that are standard with conventional DTI and fMRI protocols). At this resolution, critical network nodes (e.g., motor and non-motor subregions of subthalamic nucleus (STN)) and pathways that are important for patient care (e.g., improved MRI guidance for deep brain stimulation) can be much more reliably resolved.
Aim 1 B) We will develop an innovative multi-echo-pathway MRI method to significantly reduce the scan time of multi-contrast MRI and parametric imaging (e.g., achieving simultaneous T1 and T2 parametric mapping within 3 min: ~ 4-fold improvement than conventional protocols).
Aim 1 C) We will develop motion artifact correction schemes that are suitable for high-resolution multi-contrast MRI in challenging patients.
Aim 2 A) We plan to first evaluate the MRI technologies in healthy adult volunteers in two ways. First, data obtained with our methods and conventional, more time-consuming protocols will be quantitatively compared. Second, new knowledge that can only be produced from our high-resolution data (e.g., imaging motor- subregions of STN) will be confirmed with theta-burst transcranial magnetic simulation (TMS) neuro- modulation of motor networks.
Aim 2 B) We plan to evaluate the proposed imaging technologies in PD patients in three ways. First, in a cross-sectional study, we will acquire and compare imaging data from 1) those who are at high risk of PD conversion (with positive family history, hyposmia, rapid eye movement sleep behavior disorder, constipation, and impairments in instrumental daily activities), 2) early-stage PD patients (Hoehn and Yahr scale 1 and 2), and 3) advanced-stage PD patients (Hoehn and Yahr scale 3 and 4). Difference in brain structure and function across three populations will be assessed. Second, in a longitudinal study, imaging data obtained from subjects with high risk of conversion to PD in year 2 and year 5 of the project will be compared to measure prodromal brain signal abnormalities and their correlation with longitudinal behavioral and MRI signal changes. Third, we will compare imaging data obtained from PD patients and non-PD patients (mainly essential tremor) to evaluate the differential diagnosis accuracy of the proposed imaging technologies.

Public Health Relevance

and Public Health Relevancy Statement The proposed quantitative MR imaging methods enable efficient and reliable measurements of both structural and physiological information indicative of disease progression in patients with neurological diseases (including Parkinson?s disease). Using the proposed methods, high-resolution, high-quality and quantitative MRI data can be reliably acquired even from challenging patient populations. The proposed high-speed MRI is expected to play a transformative role in reducing the cost, increasing the accessibility of MRI, and making a positive economic impact on healthcare system.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS102220-02
Application #
9735460
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Kukke, Sahana Nalini
Project Start
2018-07-01
Project End
2023-03-31
Budget Start
2019-04-01
Budget End
2020-03-31
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Arizona
Department
Biomedical Engineering
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
806345617
City
Tucson
State
AZ
Country
United States
Zip Code
85721