Abstract

The goals of this research are to develop novel quantitative methods for simultaneous whole- body (WB) PET-MR imaging, validate these methods in a woodchuck model of spontaneous hepatocellular carcinoma and evaluate their clinical value, compared to PET-CT, in monitoring response to therapy in liver metastases. Simultaneous PET-MR is a novel and promising imaging modality that is generating substantial interest in the medical community and offers the scientific community many challenges and opportunities. Unlike sequentially- acquired WB PET-CT scans, the simultaneous acquisition of MR and PET data can be used to incorporate MR motion and anatomical MR priors within the PET reconstruction model. We hypothesize that the additional MR information will yield substantial improvement of PET in terms of lesion detection and activity estimation. We have formed a multi-disciplinary team that consists of scientists and clinicians to develop quantitative methods for PET-MR and evaluate clinically the improvement that can be achieved over conventional sequential PET-CT.

Public Health Relevance

Simultaneous PET-MR is a novel and promising imaging modality that is generating substantial interest in the medical community and offers the scientific community many challenges and opportunities. The goals of this research are to develop novel quantitative methods for simultaneous whole-body PET-MR imaging, validate these methods in an animal model and evaluate their clinical value, compared to PET-CT, in monitoring response to cancer therapy.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA165221-02
Application #
8451303
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Zhang, Huiming
Project Start
2012-05-01
Project End
2017-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
2
Fiscal Year
2013
Total Cost
$467,900
Indirect Cost
$198,992

  Institution

Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199

  Publications

Han, Paul Kyu; Ma, Chao; Deng, Kexin et al. (2018) A minimum-phase Shinnar-Le Roux spectral-spatial excitation RF pulse for simultaneous water and lipid suppression in 1H-MRSI of body extremities. Magn Reson Imaging 45:18-25
Ma, Chao; Clifford, Bryan; Liu, Yuchi et al. (2017) High-resolution dynamic 31 P-MRSI using a low-rank tensor model. Magn Reson Med 78:419-428
Ma, Chao; Lam, Fan; Ning, Qiang et al. (2017) High-resolution 1 H-MRSI of the brain using short-TE SPICE. Magn Reson Med 77:467-479
Rakvongthai, Yothin; El Fakhri, Georges (2017) Magnetic Resonance-based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging. PET Clin 12:321-327
Li, Quanzheng; Li, Hao; Kim, Kyungsang et al. (2017) Joint estimation of activity image and attenuation sinogram using time-of-flight positron emission tomography data consistency condition filtering. J Med Imaging (Bellingham) 4:023502
Zhang, Xiaomeng; Chen, Yen-Lin E; Lim, Ruth et al. (2016) Synergistic role of simultaneous PET/MRI-MRS in soft tissue sarcoma metabolism imaging. Magn Reson Imaging 34:276-9
Sitek, Arkadiusz; Li, Quanzheng; El Fakhri, Georges et al. (2016) Validation of Bayesian analysis of compartmental kinetic models in medical imaging. Phys Med 32:1252-1258
Lam, Fan; Ma, Chao; Clifford, Bryan et al. (2016) High-resolution (1) H-MRSI of the brain using SPICE: Data acquisition and image reconstruction. Magn Reson Med 76:1059-70
Wang, Mengdie; Guo, Ning; Hu, Guangshu et al. (2016) A novel approach to assess the treatment response using Gaussian random field in PET. Med Phys 43:833-42
Hu, Chenhui; Hua, Xue; Ying, Jun et al. (2016) Localizing Sources of Brain Disease Progression with Network Diffusion Model. IEEE J Sel Top Signal Process 10:1214-1225

Showing the most recent 10 out of 45 publications