In previously published work by local coinvestigators a fundamental correlation between the increase in the quantitative MR apparent diffusion coefficient (ADC) measured over the pre- to early post-initiation therapy interval and the response of various brain tumors has been recently demonstrated. This work was demonstrated in the brains of both rats and humans and was based on a voxel-based analysis of change in the scatter plots of ADC values between rigidly registered pre- and post-therapeutic ADC MRI exams. The goal of this Project is to develop and refine registration techniques that not only demonstrate the same correlation exists in breast cancer as an early biomarker of cell death and potential surrogate for clinical outcome, but also increase the accuracy of such techniques by including perfusion-related computed parameters, and implementing accurate, automatic warping registration techniques. In addition to following the lead of the previous work which demonstrated the correlation using epidemiological methods, e.g. successful stratification of Kaplan-Meier plots based on ADC changes within volumes of interest defined by oncologists in both animals and humans, we will extend the approach to eliminate the need for multiple definition of gross volumes of interest by oncologists and improve the performance of the measure within heterogeneous tumors. Additionally for a subset of data we will carry histological results back to the in vivo images for correlation with histological truth.

Public Health Relevance

. The development of accurate and automatic registration of interval MRI diffusion breast exams will help individuate neoadjuvant chemotherapy for breast cancer patients by improving the accuracy of estimating tumors'early response (1-3 weeks) to therapy.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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University of Michigan Ann Arbor
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Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A (2015) Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA). IEEE Trans Med Imaging 34:578-88
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