# CENC - Diffusion Tensor Imaging Standardization using Novel MR Diffusion Phantoms

> **NIH VA I01** · MICHAEL E DEBAKEY VA MEDICAL CENTER · 2020 · —

## Abstract

   
DESCRIPTION (provided by applicant):    
Project Background/Rationale: Diffusion imaging has gained importance in the past decade as a valuable means of depicting white matter injury caused by various disease processes. Diffusion imaging holds particular promise for evaluation of individuals who have experienced traumatic brain injury (TBI) because damage to white matter pathways is considered to be an important component in the causation of the many types of neurocognitive impairment that can result from TBI. Diffusion imaging can be performed using a number of different imaging techniques, and no single technique is universally recognized as the single best method. As a result, development of large pools of data is hampered by the fact that combining imaging studies obtained by multiple techniques results in an inhomogeneous data set that is difficult to analyze. Analytical difficulties arise even in data obtained from two scanners made by the same manufacturer and having the same field strength if different image acquisition protocols, imaging software packages and imaging equipment (e.g., head coils) are used. Project Objectives: If diffusion imaging is to be developed as a means to evaluate Veterans with suspected TBI, a uniform type of image acquisition is needed across the different types of imaging systems available within the VA hospital network. To construct such a system, a means is needed to establish exactly how one scanner differs from another (or from itself over the course of time). Then, modification of imaging sequences and, as needed, hardware and software components, can be performed to allow more uniform data acquisition across scanners. In this proposal, we will use diffusion imaging phantoms to evaluate differences between scanners with the goal of providing acquisition techniques that will data to be compared across different patient groups and combined into large data collections. Our objective is to provide a means for the many different scanners across the

## Key facts

- **NIH application ID:** 10098003
- **Project number:** 5I01RX001880-03
- **Recipient organization:** MICHAEL E DEBAKEY VA MEDICAL CENTER
- **Principal Investigator:** Elisabeth A Wilde
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2015-01-01 → 2017-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10098003

## Citation

> US National Institutes of Health, RePORTER application 10098003, CENC - Diffusion Tensor Imaging Standardization using Novel MR Diffusion Phantoms (5I01RX001880-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10098003. Licensed CC0.

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