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

> **NIH NIH R37** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $658,526

## Abstract

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).

## Key facts

- **NIH application ID:** 10814916
- **Project number:** 5R37CA262662-03
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Ouri Cohen
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $658,526
- **Award type:** 5
- **Project period:** 2022-04-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10814916

## Citation

> US National Institutes of Health, RePORTER application 10814916, Development of a Clinical CEST MR Fingerprinting Method for Treatment Response Assessment in Brain Metastases (5R37CA262662-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10814916. Licensed CC0.

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