Dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI) biomarkers are displaying significant promise as early indicators of therapeutic response in the modern clinic. As a consequence, they possess an evolving role in the current realm of individualized medicine. Challenges remain however regarding their application in cross-sectional contemporaneous or longitudinal studies - it is critical in these studies to decouple errors due to intra-scan patient motion or repositioning on the scanner from anatomical or functional changes occurring due to treatment that these biomarkers reflect. Tools that are capable of rapid, but reliable derivation of DCE-MRI biomarkers are therefore urgently needed. The GOAL of this proposed work is to develop, implement and validate three-dimensional image registration technology for enabling the rapid derivation of disease-associated biomarkers from clinical DCE-MRI. The focus is on the alignment of intra-subject anatomical images of body regions that involve both rigid (bone) and non-rigid (soft tissue) components. In our innovative registration scheme, pre-determined components such as bones deform in a rigid manner while soft tissue warps non-rigidly. We HYPOTHESIZE therefore that our method will provide better modeling of patient repositioning compared to currently clinically used technologies as well as state-of-the-art registration methods. In our SPECIFIC AIMS, we will: (1) develop the quasi-rigid, intra-subject registration method for MR image alignment, and (2) validate the registration method using MR images of adult patients with soft tissue sarcoma in a retrospective setting. Both computer-based and human observers will be used to compare the performance of the proposed method against conventional rigid and non-rigid registration methods. A novel system for radiologic scoring of the merits of image registration is devised and will be tested. The registration method will be developed as an open-source technique allowing easy distribution. DCE-MRI biomarkers are currently being evaluated as surrogate end-points in clinical trials of several novel drug treatments. The proposed registration method will enable rapid quantitative change analyses of DCE-MRI biomarkers over the treatment period. Physicians will be able to use this information to tailor therapeutic strategies according to the individual requirements of their patients. In this context, techniques developed in this proposal are applicable to a broad class of human diseases, e.g., oncologic (e.g., sarcomas, head and neck cancer, etc) or musculoskeletal disease (e.g., arthritis, etc). This work is a close collaboration with Robert Canter, a surgical oncologist who specializes in the clinical management of soft tissue sarcoma, Michael Buonocore, an MRI physicist whose expertise lies in clinical MRI protocol design and image analysis, Robert Boutin, a clinical radiologist with expertise in soft tissue sarcoma and consultant Wayne Monsky, an expert in cancer imaging biomarker development.

Public Health Relevance

Advanced imaging tools like magnetic resonance imaging (MRI) are playing a crucial role in combating serious human diseases like cancer and arthritis. The goal of this research is to develop open-source software to aid clinicians in rapidly analyzing MR images, enabling them to personalize therapeutic options for their patients.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Small Research Grants (R03)
Project #
5R03EB015099-02
Application #
8598873
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Pai, Vinay Manjunath
Project Start
2013-01-01
Project End
2014-12-31
Budget Start
2014-01-01
Budget End
2014-12-31
Support Year
2
Fiscal Year
2014
Total Cost
$65,285
Indirect Cost
$20,285
Name
University of California Davis
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
047120084
City
Davis
State
CA
Country
United States
Zip Code
95618
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