A) Noise reduction strategies in hyperpolarized MRI: Hyperpolarized (HP) MRI using pyruvate allows imaging kinetics of important bio-energetic pathways such as aerobic glycolysis or oxidative phosphorylation. Since malignant cells process glucose/pyruvate through aerobic glycolysis vs normal cells which rely on oxidative phosphorylation, this imaging allowed biochemically profiling regions of interest in vivo non-invasively using 13C MRI using hyperpolarized tracers. Hyperpolarization bridges the 4-orders in sensitivity needed for 13C MRI in vivo. However weaker metabolites are still often not at detectable levels. This prompted us to evaluate noise reduction strategies to improve image quality and detect low level metabolites in HP MRI with 13C pyruvate. A new noise reduction strategy which can be applied as a post-processing method to MRI data was developed to improve image quality and also quantify low level metabolites. An approximately 12-fold improvement in signal:noise was realized using this method. Image quality of several data were successfully improved using this newly developed noise reduction strategy. B) Metabolic MRI using 13C labeled glucose: Molecular imaging assessment of photoimmunotherapy: Pyruvate is metabolically several steps downstream from glucose. 13C labeled hyperpolarized pyruvate probes two important metabolic pathways and has optimal features for hyperpolarization which made it easy for transition from pre-clinical to clinical stage. However 13C glucose has potential to probe more metabolic pathways than pyruvate. Hyperpolarization methods have been evaluated in vivo in pre-clinical models for metabolic imaging. The low levels of polarization achieved and the short half-life of the polarization made hyperpolarization not practical for most pre-clinical in vivo studies as well as in humans. We evaluated the utility of de-noising techniques when using non-hyperpolarized 13C labeled glucose as a tracer in 13C MRI. Were the de-noising methods to be successful, metabolic MRI method can be more generally applicable and also be disseminated to more sites since the expensive hyperpolarization apparatus is no longer necessary. We have successfully demonstrated that the post-processing method of image de-noising to provide images of 13C labeled glucose uptake and its metabolic conversion to several products in tumor bearing mice. C) Development of 13C labeled N-acetyl cysteine as a tracer for hyperpolarized 13C MR: N-acetyl cysteine (NAC) is a widely used therapeutic in instances of oxidative damage. It is also used as a tool in pre-clinical studies to examine the role of reactive oxygen species casing tissue damage. We tested whether 13C-labeled NAC can be hyperpolarized to sufficient levels to permit in vivo detection/imaging. Initial studies were performed with NAC with natural abundance 13C and we identified the 1-position carbonyl as amenable for hyperpolarization. NAC with 100% enrichment with 13C at the 1-position was synthesized in collaboration with IPDC. Several formulations were made to arrive at optimal conditions for adequate polarization needed for in vivo imaging. We then tested for enzymatic conversion using acylase. A distinct peak with a different chemical shift than 13C-NAC was observed suggesting the feasibility of this approach. 13C MRI studies in the thoracic, abdomen and head region in mice administered in vivo with HP 13C-NAC showed a rapid biodistribution in brain, kidneys and liver region. Thus an imaging modality has been developed using 13C-NAC for applications for imaging foci of ROS generation. D) Clinical Imaging: We have integrated current pulse sequences for imaging kinetics of pyruvate to lactate conversion using echo planar spectroscopic imaging as well as spectral spatial imaging to allow image data collection 2 seconds.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIABC010477-16
Application #
9779643
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
16
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Basic Sciences
Department
Type
DUNS #
City
State
Country
Zip Code
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