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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43EB015293-01A1
Application #
8397233
Study Section
Special Emphasis Panel (ZRG1-SBIB-T (10))
Program Officer
Conroy, Richard
Project Start
2012-09-01
Project End
2014-02-28
Budget Start
2012-09-01
Budget End
2014-02-28
Support Year
1
Fiscal Year
2012
Total Cost
$168,701
Indirect Cost
Name
Medimagemetric, LLC
Department
Type
DUNS #
830966334
City
New York
State
NY
Country
United States
Zip Code
10044
Tan, H; Zhang, L; Mikati, A G et al. (2016) Quantitative Susceptibility Mapping in Cerebral Cavernous Malformations: Clinical Correlations. AJNR Am J Neuroradiol 37:1209-15
Wen, Yan; Wang, Yi; Liu, Tian (2016) Enhancing k-space quantitative susceptibility mapping by enforcing consistency on the cone data (CCD) with structural priors. Magn Reson Med 75:823-30
Wang, Yi; Liu, Tian (2015) Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. Magn Reson Med 73:82-101
Li, Jianqi; Chang, Shixin; Liu, Tian et al. (2015) Phase-corrected bipolar gradients in multi-echo gradient-echo sequences for quantitative susceptibility mapping. MAGMA 28:347-55
Dimov, Alexey V; Liu, Tian; Spincemaille, Pascal et al. (2015) Joint estimation of chemical shift and quantitative susceptibility mapping (chemical QSM). Magn Reson Med 73:2100-10
Zhang, Jingwei; Liu, Tian; Gupta, Ajay et al. (2015) Quantitative mapping of cerebral metabolic rate of oxygen (CMRO2 ) using quantitative susceptibility mapping (QSM). Magn Reson Med 74:945-52
Dong, Jianwu; Liu, Tian; Chen, Feng et al. (2015) Simultaneous phase unwrapping and removal of chemical shift (SPURS) using graph cuts: application in quantitative susceptibility mapping. IEEE Trans Med Imaging 34:531-40
Schweitzer, Andrew D; Liu, Tian; Gupta, Ajay et al. (2015) Quantitative susceptibility mapping of the motor cortex in amyotrophic lateral sclerosis and primary lateral sclerosis. AJR Am J Roentgenol 204:1086-92
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
Wen, Yan; Zhou, Dong; Liu, Tian et al. (2014) An iterative spherical mean value method for background field removal in MRI. Magn Reson Med 72:1065-71

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