ABSTRACT We propose to develop a fast, quantitative chemical exchange saturation transfer (CEST) imaging technique, by integrating CEST with MR fingerprinting (MRF) and deep-learning techniques in a unified framework, with the ultimate goal of translation into routine clinical practice. CEST imaging is an important molecular MRI method that can generate contrast based on the proton exchange between solute labile protons and bulk water protons in tissue. Amide proton transfer (APT) imaging, a variant of CEST-based molecular MRI, is based on the amide protons (-NH) of endogenous mobile proteins and peptides in tissue. APT-MRI has been used successfully to image protein content and pH, enabling tumor grading and the differentiation of active recurrent tumor from treatment effects. However, most currently used APT imaging protocols depend on the acquisition of qualitative, so-called APT-weighted (APTw) images, limiting the detection sensitivity to quantitative parameters, such as pH or protein concentration. Currently, quantitative APT imaging is often attempted by assessing a so-called Z-spectrum, generated by measuring the normalized water signal intensity as a function of saturation frequency offset under varied radiofrequency (RF) saturation powers, which is time-consuming. Thus, the development of fast, quantitative APT imaging techniques is needed. MRF is a novel quantitative imaging method that simultaneously quantifies multiple tissue properties using pseudorandom acquisition parameters, and thus, significantly improves scan efficiency compared to conventional techniques. MRF has been successfully applied in patient studies to evaluate the range of and changes in MR relaxation times, T1 and T2, providing initial evidence of its clinical utility. Recent advances in deep neural networks open a new possibility to efficiently solve general inversion problems in MRF reconstruction, and to produce high-quality estimates of tissue parameters at high speed. Our hypothesis is that, by combining APT, MRF, and deep-learning techniques, we can highly accelerate image acquisition and accurately estimate the quantitative values of the tissue. Our hypotheses will be tested through three specific aims: 1) to develop a fast 3D APT-MRF sequence and design an optimal RF saturation schedule using deep-learning; 2) to quantify absolute amide proton concentrations and exchange rates using convolutional neural networks; and 3) to demonstrate the initial clinical utility of the technology in brain cancer, which will be confirmed by radiographically-guided stereotactic biopsy. Through quantitative APT imaging technology, a priori knowledge of the pH and protein content in gliomas may help in the stratification of patients into personalized therapeutic strategies and help monitor treatment response.