This project will maximize the metabolic information which can be obtained from localized in vivo 31P and 1H spectra by developing and applying new technical and data processing procedures. The sensitivity of the data will be improved by implementing automatic shimming, by installing proton decoupling for 31P spectroscopy and by extending water suppression techniques to use with 1H chemical shift imaging (CSI). The measurement of T1's and determination of absolute concentrations will also be important in accurately interpreting the data. In addition to these technical advances, new data processing procedures and software packages will be developed for examination in planning and automatic first pass analysis of the CSI data. Current methods for quantifying individual spectra and producing metabolic images will be extended and optimized. The information in coil sensitivity maps, relaxation times and peak areas of individual spectra will be combined to produce absolute metabolic concentrations, which will then be correlated with the anatomy as depicted in the associated proton images. To test the procedures and build up a database of metabolite concentrations in normal tissue, 31P CSI will be applied to study liver, muscle and brain of volunteers. In the case of brain, a comparison will also be made with the metabolic information provided by localized 1H spectroscopy. In the final years of the grant, the techniques will be applied to study two tumor systems: squamous cell carcinoma metastases of the neck and brain metastases. This will provide an opportunity to study tumor heterogeneity, to compare localized 31P and 1H spectra and to test the feasibility of applying our procedures in a clinical setting.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
Project #
Application #
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Fox Chase Cancer Center
United States
Zip Code
Lee, Seung-Cheol; Arias-Mendoza, Fernando; Poptani, Harish et al. (2012) Prediction and Early Detection of Response by NMR Spectroscopy and Imaging. PET Clin 7:119-26
Hultman, Kristi L; Raffo, Anthony J; Grzenda, Adrienne L et al. (2008) Magnetic resonance imaging of major histocompatibility class II expression in the renal medulla using immunotargeted superparamagnetic iron oxide nanoparticles. ACS Nano 2:477-84
Stoyanova, Radka; Querec, Troy D; Brown, Truman R et al. (2004) Normalization of single-channel DNA array data by principal component analysis. Bioinformatics 20:1772-84
Stoyanova, Radka; Nicholls, Andrew W; Nicholson, Jeremy K et al. (2004) Automatic alignment of individual peaks in large high-resolution spectral data sets. J Magn Reson 170:329-35
Stoyanova, Radka; Nicholson, Jeremy K; Lindon, John C et al. (2004) Sample classification based on Bayesian spectral decomposition of metabonomic NMR data sets. Anal Chem 76:3666-74
Sajda, Paul; Du, Shuyan; Brown, Truman R et al. (2004) Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain. IEEE Trans Med Imaging 23:1453-65
Nahum, Alan E; Movsas, Benjamin; Horwitz, Eric M et al. (2003) Incorporating clinical measurements of hypoxia into tumor local control modeling of prostate cancer: implications for the alpha/beta ratio. Int J Radiat Oncol Biol Phys 57:391-401
Stoyanova, R; Brown, T R (2002) NMR spectral quantitation by principal component analysis. III. A generalized procedure for determination of lineshape variations. J Magn Reson 154:163-75
Stoyanova, R; Brown, T R (2001) NMR spectral quantitation by principal component analysis. NMR Biomed 14:271-7
Ochs, M F; Stoyanova, R S; Arias-Mendoza, F et al. (1999) A new method for spectral decomposition using a bilinear Bayesian approach. J Magn Reson 137:161-76

Showing the most recent 10 out of 46 publications