# Correction of Diffusion Gradient Bias in Quantitative Diffusivity Metrics for MultiPlatform Clinical Oncology Trials

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $718,691

## Abstract

Abstract
Multi-center clinical trials increasingly utilize quantitative diffusion imaging (DWI) to aid in patient management
and treatment response assessment for translational oncology applications. A major source of systematic bias
in diffusion was discovered originating from platform-dependent gradient hardware. Left uncorrected, these
biases confound quantitative diffusion metrics used for characterization of tissue pathology and treatment
response leading to inconclusive findings, and increasing the requisite subject numbers and trial cost. Technical
remedy was defined by University of Michigan investigators and motivated our parent AIP involving the three
dominant MRI manufactures. The current AIP merged expertise among commercial scientists/engineers and
academic researchers, and resulted in successful design and development of prototype correction tools to
eliminate systematic diffusion weighting bias in quantitative DWI applications across diverse clinical MRI
platforms. As a result, two vendors have implemented prototype tools on their respective scanner platforms for
correction of mean tissue diffusivity metric widely used in oncology trials. Furthermore, feasibility of retrospective
correction across all three vendor platforms was demonstrated for the repeatability cohort of ACRIN 6698 breast
cancer imaging trial. Our AIP participation with clinical trial cooperative groups and quantitative imaging
consortia revealed that the most efficient route for adoption of developed technology on clinical platforms is by
vendor implementation. Additional need was noted for flexible integration with advanced acquisition protocols
and analyses using multiple b-values to accurately quantify complex metrics beyond mean diffusivity. To address
these needs, the renewal AIP will extend our collaborations to include two additional academic cancer imaging
centers to integrate and validate developed practical correction tools in five active clinical oncology trials, as well
as enable application to advanced tissue diffusivity models. These tools will eliminate systematic cross-platform,
cross-exam variability to facilitate longitudinal and multi-institutional translational cancer research that utilize
quantitative diffusivity metrics. Success of this project will further enhance accuracy and precision of cancer
detection and monitoring. These goals will be achieved through Aim1: deployment of DWI bias correction tools
for application in multiple cancer imaging trials, and through Aim2: correction integration with advanced DWI
protocols and tissue models.
Academic team of the proposed partnership consists of recognized experts in quantitative diffusion imaging
standardization and translation to clinical oncology trials. The PI institution has active research agreements with
three dominant clinical MRI manufactures with prior record of successful implementations for the developed
technologies. Accomplishment of the project goals will eliminate significant i...

## Key facts

- **NIH application ID:** 10206340
- **Project number:** 2R01CA190299-06A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** THOMAS L CHENEVERT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $718,691
- **Award type:** 2
- **Project period:** 2015-08-10 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10206340

## Citation

> US National Institutes of Health, RePORTER application 10206340, Correction of Diffusion Gradient Bias in Quantitative Diffusivity Metrics for MultiPlatform Clinical Oncology Trials (2R01CA190299-06A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10206340. Licensed CC0.

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