Development of a Clinical CEST MR Fingerprinting Method for Treatment Response Assessment in Brain Metastases

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Project Summary/Abstract Chemical Exchange Saturation Transfer (CEST) MRI uses selective radio-frequency (RF) pulses to saturate the magnetization of exchangeable protons on a variety of molecules and macromolecules, including proteins, which, due to fast chemical exchange with bulk water, results in a decreased water MRI signal. The CEST contrast depends on the chemical exchange rate (kex), which is pH sensitive, and the volume fraction of the exchangeable proton pool (fs) that is sensitive to protein and metabolite concentrations. The sensitivity of CEST MRI to pH and protein/metabolite concentrations has proven to be a powerful tool for imaging a wide range of disease pathologies. For example, the amide proton CEST contrast from endogenous proteins has been used to distinguish pseudo-progression from true progression in malignant gliomas, differentiate between radiation necrosis and tumor progression, and image the tumor's extracellular pH. However, clinical translation of these CEST-MRI methods has been hindered by the qualitative nature of the image contrast, long image acquisition times, and the complex data processing required. Efficient methods for quantification of kex and fs are needed to produce high-quality pH and volume fraction maps required to move many of these studies forward into the clinic. In this proposal a CEST magnetic resonance fingerprinting (MRF) method that enables accurate quantification of both proton exchange rates and volume fractions in a fraction of the time required by conventional pulse sequences will be developed and optimized. These novel techniques exploit deep learning methods to enable the simultaneous quantification of multiple tissue maps from a single measurement. The improved CEST-MRF method will enable the acquisition of accurate pH, water T1 and T2, and protein/metabolite concentration maps in acquisition times of less than 5 minutes. The sequence will be adapted to a clinical scanner, and a novel multi- slice method will be implemented to obtain whole brain coverage (Aim 1). Next the CEST-MRF acquisition schedule will be optimized to maximize the parameter map discrimination and accuracy using a deep learning approach for the parameter map reconstruction. The parameter map reconstructions in normal human subjects will be validated with conventional CEST and test-retest studies (Aim 2). Lastly, the optimized CEST-MRF method will be used to evaluate the change in the quantitative parameter maps before and after radiation therapy to assess the potential role of CEST-MRF maps as predictive imaging biomarkers for brain metastases (Aim 3).