Ilknur Icke1,2, Raquel Sevilla1, Gain Robinson1, Corin Miller1 (1) Merck Research Laboratories (MRL), USA (2) Boston University, Bioimaging, USA Generative Models for measuring pH using CEST MRI Abstract Non-invasive measurement of pH provides multiple potential benefits for identification of disease states such as in cancer, inflammation, hypoxia, and tissue injury. In oncology, non- invasive pH measurements may help identify proper chemotherapeutics, characterize tumors that are more likely to either respond to therapy or metastasize, and may also help better assess treatment response. AcidoCEST MRI techniques have been developed over the recent years to perform tumor pH measurements by utilizing a contrast agent for which chemical exchange with tissue water depends on the pH of the local microenvironment. Quantitative analysis of the CEST MRI signals is generally done via modeling of the Bloch- McConnell equations by incorporating chemical exchange as a parameter, or by fitting Lorentzian line shapes to observed Z-spectra and then computing a log ratio of the CEST effects from multiple labile protons within the same molecule (ratiometric method). Modeling using Bloch-McConnell equations requires careful inclusion of many scan parameters to infer pH, while the ratiometric method requires contrast agents with multiple labile protons, thus making it unsuitable for molecules with a single labile proton. Furthermore, depending on the pH, sometimes it might not be possible to accurately calculate the ratio due to the low signal to noise ratio (SNR) of the CEST signal for certain labile protons. To overcome these limitations, we developed a generative machine learning algorithm to predict in vivo tumor pH where the observed Z-spectra after contrast agent infusion is modeled as a perturbation on the observed Z-spectra before the contrast agent was introduced. In this scheme, perturbation is modulated by the contrast agent. A sub-network of the architecture is pre-trained on a separate phantom dataset where temperature and contrast agent concentrations were controlled prior to CEST MRI scanning and subsequently used as prior knowledge that modulates the perturbation caused by the infusion of the contrast agent into the body. Our results on an experimental study using animal models (mice) indicate that our machine learning method provides a more general and accurate prediction of pH in comparison to the ratiometric method. Our method is also more general in the sense that it does not require explicit modeling of signal peaks that are dependent on the type of contrast agent. Finally, this framework can be extended into use-cases where new probing materials and sensing technologies can be designed and tested in order to better understand the biological entities being studied. References [1] W. R. Hanahan D, Hallmarks of cancer: the next generation, Cell, 2011. [2] O. Warburg, On the origin of cancer cells, Science, 1956 [3] R. e. a. Martınez-Zaguilan, Acidic pH enhances the invasive behavior of human melanoma cells, Clinical and Experimental Metastasis, 1996 [4] Y. e. a. Kato, Acidic extracellular microenvironment and cancer, Cancer Cell International, 2013 [5] J. M. Goldenberg and M. D. Pagel, Assessments of tumor metabolism with CEST MRI, NMR in Biomedicine, vol. 32, no. 10, p. e3943, 2019. e3943 NBM-18-0007.R2. [6] C. LQ, H. CM, J. JJ, R. IF, K. PH, and P. MD, Evaluations of extracellular pH within in vivo tumors using AcidoCEST MRI, Magn. Reson. Med, 2014. Conclusions CEST MRI based tumor microenvironment characterization can benefit from generative machine learning methods that can be trained in an unbiased manner, independent of the contrast agent chemical properties. Background PhantomNET InVivoNET Methods Altered cell metabolism is one of the hallmarks of cancer [1]. It has been known since the 1930s that cancer cells “prefer” aerobic glycolysis to generate the energy they need (Warburg effect [2]), which increases the lactic a