Conductivity-based choline contrast as a potential imaging biomarker for assessing abnormal choline metabolism

Background: Abnormal brain metabolite levels are indicators of physiological and pathological tissue conditions. Noninvasive imaging of these metabolites enables functional assessment of tissue in situ. One consistent hallmark of various cancers is altered choline metabolism. Magnetic resonance (MR)-based conductivity imaging method provides novel tissue contrast by reflecting the concentration and mobility of constituent ions. This study presents preliminary evidence supporting the use of electrical conductivity of choline as a potential imaging biomarker for assessing abnormal choline metabolism. Methods: In vitro measurements evaluated changes in conductivity corresponding to variations in brain metabolite concentrations, measurable by MRI. Phantom imaging compared the sensitivity of conductivity imaging with MRSI to validate these results. To further assess sensitivity, a choline solution at twice the normal concentration was directly injected into the mouse brain, followed by an imaging experiment. Finally, in vivo imaging was performed using a mouse brain tumor model to compare choline-related conductivity contrasts between normal and cancerous tissues. Results: In vitro measurements showed metabolite-dependent conductivity, with Cho and ACh showing linear concentration-dependent increases, while NAA and Lac exhibited modest changes, Glx showed weak changes, and Cr and mI showed little to no changes. Phantom conductivity imaging provided linear sensitivity across physiologically relevant concentrations and maintained accuracy even in choline-dominant mixture phantoms, in contrast to MRSI, which underestimated Cho at low levels. In vivo focal choline injection and tumor models both exhibited marked conductivity increases of more than 20% and 150%, respectively, compared to the contralateral normal region. Conclusion: Electrical conductivity imaging can more sensitively detect choline concentrations associated with abnormal metabolism and may serve not only as a complementary imaging method but also as a potential quantitative biomarker for cancer diagnosis and metabolic assessment in clinical practice.