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Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has been developed and employed in multiple clinical imaging research centers worldwide. Selective radiofrequency (RF) saturation pulses with standard 2D and 3D MRI acquisition schemes are now routinely performed, and CEST MRI can produce semiquantitative results using magnetization transfer ratio asymmetry (MTRasym) analysis while accounting for B0 inhomogeneity. Faster clinical CEST MRI acquisition methods and more quantitative acquisition and analysis routines are under development. Endogenous biomolecules with amide, amine, and hydroxyl groups have been detected during clinical CEST MRI studies, and exogenous CEST agents have also been administered to patients. These CEST MRI tools show promise for contributing to assessments of cerebral ischemia, neurological disorders, lymphedema, osteoarthritis, muscle physiology, and solid tumors. This review summarizes the salient features of clinical CEST MRI protocols and critically evaluates the utility of CEST MRI for these clinical imaging applications. Level of Evidence: 5 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:11–27.

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Purpose We optimized acido-chemical exchange saturation transfer (acidoCEST) magnetic resonance imaging (MRI), a method that measures extracellular pH (pHe), and translated this method to the radiology clinic to evaluate tumor acidosis. Procedures A CEST-FISP MRI protocol was used to image a flank SKOV3 tumor model. Bloch fitting modified to include the direct estimation of pH was developed to generate parametric maps of tumor pHe in the SKOV3 tumor model, a patient with high-grade invasive ductal carcinoma, and a patient with metastatic ovarian cancer. The acidoCEST MRI results of the patient with metastatic ovarian cancer were compared with DCE MRI and histopathology. Results The pHe maps of a flank model showed pHe measurements between 6.4 and 7.4, which matched with the expected tumor pHe range from past acidoCEST MRI studies in flank tumors. In the patient with metastatic ovarian cancer, the average pHe value of three adjacent tumors was 6.58, and the most reliable pHe measurements were obtained from the right posterior tumor, which favorably compared with DCE MRI and histopathological results. The average pHe of the kidney showed an average pHe of 6.73 units. The patient with high-grade invasive ductal carcinoma failed to accumulate sufficient agent to generate pHe measurements. Conclusions Optimized acidoCEST MRI generated pHe measurements in a flank tumor model and could be translated to the clinic to assess a patient with metastatic ovarian cancer.

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Acidosis within tumor and kidney tissues has previously been quantitatively measured using a molecular imaging technique known as acidoCEST MRI. These previous studies have used iopromide and iopamidol, two iodinated contrast agents that are approved for clinical CT diagnoses and have been repurposed for acidoCEST MRI studies. We aimed to compare the performance of both agents for measuring pH by optimizing image acquisition conditions, correlating pH with a ratio of CEST effects from an agent, and evaluating the effects of concentration, endogenous T1 relaxation time and temperature on the pH-CEST ratio correlation for each agent. These results showed that both agents had similar performance characteristics, although iopromide produced a pH measurement with a higher dynamic range while iopamidol produced a more precise pH measurement. We then compared the performance of both agents to measure in vivo extracellular pH (pHe) within xenograft tumor models of Raji lymphoma and MCF-7 breast cancer. Our results showed that the pHe values measured with each agent were not significantly different. Also, iopromide consistently measured a greater region of the tumor relative to iopamidol in both tumor models. Therefore, an iodinated contrast agent for acidoCEST MRI should be selected based on the measurement properties needed for a specific biomedical study and the pharmacokinetic properties of a specific tumor model.

Purpose We aimed to develop pixelwise maps of tumor acidosis to aid in evaluating extracellular tumor pH (pHe) in cancer biology. Procedures MCF-7 and MDA-MB-231 mouse models were imaged during a longitudinal study. AcidoCEST MRI and a series of image processing methods were used to produce parametric maps of tumor pHe, and tumor pHe was also measured with a pH microsensor. Results Sufficient contrast-to-noise for producing pHe maps was achieved by using standard image processing methods. A comparison of pHe values measured with acidoCEST MRI and a pH microsensor showed that acidoCEST MRI measured tumor pHe with an accuracy of 0.034 pH units. The MCF-7 tumor model was found to be more acidic compared to the MDA-MB-231 tumor model. The pHe was not related to tumor size during the longitudinal study. Conclusions These results show that acidoCEST MRI can create pixelwise tumor pHe maps of mouse models of cancer.

Purpose We proposed to detect the in vivo enzyme activity of γ-glutamyl transferase (GGT) within mouse models of human ovarian cancers using catalyCEST MRI with a diamagnetic CEST agent. Methods A CEST-FISP MRI protocol and a diamagnetic CEST agent were developed to detect GGT enzyme activity in biochemical solution. A quantitative Michaelis-Menten enzyme kinetics study was performed to confirm that catalyCEST MRI can measure enzyme activity. In vivo catalyCEST MRI studies generated pixelwise activity maps of GGT activities. Ex vivo fluorescence imaging was performed for validation. Results CatalyCEST MRI selectively detected two CEST signals from a single CEST agent, whereby one CEST signal was responsive to GGT enzyme activity and the other CEST signal was an unresponsive control signal. The comparison of these CEST signals facilitated in vivo catalyCEST MRI studies that detected high GGT activity in OVCAR-8 tumors, low GGT activity in OVCAR-3 tumors, and low or no GGT activity in muscle tissues. Conclusions CatalyCEST MRI with a diamagnetic CEST agent can detect the level of GGT enzyme activity within in vivo tumor models of human ovarian cancers.