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...