The objective of this research is to develop a robust and accurate quantification tool for molecular mouse MRI using the novel quantitative susceptibility mapping approach. There are significant efforts in preclinical mouse MRI, such as to improve biological specificity in drug delivery using nanocarriers loaded with diagnostic contrast agents (CA) and therapeutic drugs. CA quantification is required for many preclinical mouse MRI studies. Current methods to quantify CA are based on measuring their effects on relaxation, which is well known to be problematic because of the need of calibration and relaxation quenches when CA aggregate in cell/tissue. We have developed a novel approach to CA quantification by making use of the fact that CA in MRI affect not only water proton relaxation (signal magnitude) but also create local field that can be measured from the signal phase of neighboring water. Deconvolution of the field using a novel morphology enabled dipole inversion (MEDI) method can quantitatively determine its susceptibility sources, which are contrast agents. Our preliminary studies on mice and humans have demonstrated that this MEDI method is capable of accurately mapping CA, solving the CA quantification problem in mouse MRI.

Public Health Relevance

This SBIR Phase I research project will develop a robust and accurate quantification tool for molecular mouse MRI using the novel quantitative susceptibility mapping approach. FDA approved contrast agents are routinely used in clinical practice and new contrast agents targeting cancer and other diseases are under very active developments, and yet there is no accurate method to quantify contrast agents in MRI. The successful outcome of this project will enable the dissemination of a novel contras agent quantification technology to the large MRI community.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZRG1-SBIB-T (10))
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Conroy, Richard
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Medimagemetric, LLC
New York
United States
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Tan, Huan; Liu, Tian; Wu, Ying et al. (2014) Evaluation of iron content in human cerebral cavernous malformation using quantitative susceptibility mapping. Invest Radiol 49:498-504

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