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Abstract:
Introduction: Deuterium metabolic imaging (DMI) is a non-invasive tool that offers quantitative measurements of metabolic processes, particularly glucose metabolism, for precise monitoring of disease progression. We optimized DMI technology at preclinical and clinical levels and effectively monitored in vivo metabolism using DMI in neuro-oncology models, bridging the gap from bench to bedside. Additionally, we have implemented SSFP-based methods to enhance DMI detection in human subjects.
Methods: Preclinical: A custom-built 2H surface coil operating at 7 T was used to acquire chemical shift imaging (CSI) in phantoms, in healthy rat brains as well as brain-implanted glioma rat models before and after injecting a bolus of 1M [6,6′-2H2]glucose ([6,6′-2H2]glu) (90 min p.i., 0.5 mL/min for 2 min). Clinical: Three healthy volunteers participated in a study at 9.4 T, adhering to approved ethical protocols, where [6,6′−2H2]glu (0.75 g/kg of body weight) was orally administered. Whole brain bSSFP 10-minute acquisitions with a resolution of 3.7 mL were conducted for DMI at different time intervals. A 2 mm3 spatial field map with protons (1H) was obtained for B0 correction. 10-minute 3D CSI with a resolution of 7.9 mL were performed at three-time points for the reference values of metabolites. Results/Discussion: We have successfully developed a unique DMI setup that facilitates accurate and precise quantitative metabolic imaging. We quantified [6,6'-2H2]glu using CSI phantoms containing different [6,6'-2H2]glu concentrations. Furthermore, we performed in vivo rat brain imaging using CSI pre- and post-injection of [6,6'-2H2]glu. CSIs exhibited distinct signals from 2H-labeled water, [6,6'-2H2]glu, followed by a glutamate/glutamine (Glx) peak observed 60 minutes post-injection. The glioma rat model revealed increased lactate levels at the tumor regions, indicating enhanced glycolysis. Clinical studies using the novel bSSFP technique with multiple phase cylces at 9.4T showed improved detection of dynamic DMI compared to conventional CSI. The bSSFP sequence improved spatial resolution due to its exceptional signal-to-noise detection capabilities. Data showed a strong uptake of [6,6'-2H2]glu and efficient incorporation into downstream metabolites, including 2H-labeled water, [6,6'-2H2]glu, and Glx.
Conclusion: We have successfully designed, built, and evaluated DMI technology at 7 T preclinical and 9.4 T clinical settings. Further, we have developed a new MR sequence that enhances temporal and spatial resolution for DMI. In preclinical investigations, DMI has shown promising insights for future in vivo translation in oncology, improving understanding of tumor metabolism from bench to bedside.
