This renewal continues efforts toward establishing diffusion-weighted MRI (DWI) as a quantitative imaging metric for cancer patients. Based upon our progress to date, this renewal effort will be advanced through three Specific Aims along with strategic collaborations within the NCI Quantitative Imaging Network (QIN), Imbio, LLC (industrial partner) and the National Institute of Standards and Technology (NIST).
(Aim 1) Development of a standardized platform for diffusion analysis and validation of DWI metrics for quantification of tumor diffusion values will be accomplished through establishment of histogram and voxel-based metrics.
(Aim 2) In collaboration with NIST, development of the next generation DWI phantom using in-situ thermometry for precise diffusion measurements over the full clinically-relevant ADC range to generate quantitative quality assurance and system performance metrics across diverse scanner platforms.
(Aim 3) Based on measured system characteristics, implementation of retrospective correction of DW nonlinearity errors in multi-center trials is possible. This research effort will address major hurdles in establishing DWI for therapeutic response assessment to improve clinical management of cancer patients. We will develop and rigorously test a medical imaging platform allowing for a standardized implementation and clinical validation of advanced DWI analytical techniques for quantification of tumor diffusion values across multiple MRI systems. We will also continue the success of our widely-adopted ice water DWI phantom with the development of a next-generation DWI phantom platform that will provide quantitative diffusion measurements over the full tissue range of diffusion values. Finally, we will provide a strategy for resolving a major source of technical variability in DWI related to instrumental bias from differences in MRI gradient systems through the development of system-specific correction tools. The renewal of our QIN project will provide advanced analytical and quality assurance support to clinical trials including I-SPY2 (investigation of serial studies to predict your therapeutic response with imaging and molecular analysis, ACRIN 6698, and ACRIN 6702. Success of this endeavor will significantly advance the clinical management of cancer patients ultimately improving outcomes.

Public Health Relevance

Current multi-center clinical trials evaluate quantitative diffusion imaging for systematic monitoring and early prediction of therapy response, as well as noninvasive detection of pre-symptomatic cancer. The goals of this project are to design, evaluate and implement practical instrumental bias corrections across human MRI scanners to effectively reduce significant technical variance that confounds current multi-center cancer imaging trials and to evaluate the ability of diffusion weighted MRI to detect treatment response early in patients with breast cancer. Ultimately, these studies will advance diagnostic, prognostic and treatment monitoring quantitative imaging technology toward more effective personalized management of breast cancer patients.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-TCRB-Y (M2))
Program Officer
Nordstrom, Robert J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Schools of Medicine
Ann Arbor
United States
Zip Code
Partridge, Savannah C; Zhang, Zheng; Newitt, David C et al. (2018) Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial. Radiology 289:618-627
Schmainda, K M; Prah, M A; Rand, S D et al. (2018) Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 39:1008-1016
Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura et al. (2018) Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 5:011006
Bane, Octavia; Hectors, Stefanie J; Wagner, Mathilde et al. (2018) Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study. Magn Reson Med 79:2564-2575
Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003
Burris, Nicholas S; Hoff, Benjamin A; Patel, Himanshu J et al. (2018) Three-Dimensional Growth Analysis of Thoracic Aortic Aneurysm With Vascular Deformation Mapping. Circ Cardiovasc Imaging 11:e008045
Galbán, C J; Hoff, B A; Chenevert, T L et al. (2017) Diffusion MRI in early cancer therapeutic response assessment. NMR Biomed 30:
Burris, Nicholas S; Hoff, Benjamin A; Kazerooni, Ella A et al. (2017) Vascular Deformation Mapping (VDM) of Thoracic Aortic Enlargement in Aneurysmal Disease and Dissection. Tomography 3:163-173
Ross, Brian D (2016) Demonstration of an Inline Publication Image Viewer: The Future of Radiological Publishing. Tomography 2:1-2
Farahani, Keyvan; Kalpathy-Cramer, Jayashree; Chenevert, Thomas L et al. (2016) Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network. Tomography 2:242-249

Showing the most recent 10 out of 34 publications