A physical phantom for amine chemical exchange saturation transfer (CEST) MRI.

OBJECTIVE: To develop a robust amine chemical exchange saturation transfer (CEST) physical phantom, validate the temporal stability, and create a supporting software for automatic image processing and quality assurance. MATERIALS AND METHODS: The phantom was designed as an assembled laser-cut acrylic rack and 18 vials of phantom solutions, prepared with different pHs, glycine concentrations, and gadolinium concentrations. We evaluated glycine concentrations using ultraviolet absorbance for 70 days and measured the pH, relaxation rates, and CEST contrast for 94 days after preparation. We used Spearman's correlation to determine if glycine degraded over time. Linear regression and Bland-Altman analysis were performed between baseline and follow-up measurements of pH and MRI properties. RESULTS: No degradation of glycine was observed (p > 0.05). The pH and MRI measurements stayed stable for 3 months and showed high consistency across time points (R2 = 1.00 for pH, R1, R2, and CEST contrast), which was further validated by the Bland-Altman plots. Examples of automatically generated reports are provided. DISCUSSION: We designed a physical phantom for amine CEST-MRI, which is easy to assemble and transfer, holds 18 different solutions, and has excellent short-term chemical and MRI stability. We believe this robust phantom will facilitate the development of novel sequences and cross-scanners validations.

Modeling cell-specific dynamics and regulation of the common gamma chain cytokines.

The γ-chain receptor dimerizes with complexes of the cytokines interleukin-2 (IL-2), IL-4, IL-7, IL-9, IL-15, and IL-21 and their corresponding "private" receptors. These cytokines have existing uses and future potential as immune therapies because of their ability to regulate the abundance and function of specific immune cell populations. Here, we build a binding reaction model for the ligand-receptor interactions of common γ-chain cytokines, which includes receptor trafficking dynamics, enabling quantitative predictions of cell-type-specific response to natural and engineered cytokines. We then show that tensor factorization is a powerful tool to visualize changes in the input-output behavior of the family across time, cell types, ligands, and concentrations. These results present a more accurate model of ligand response validated across a panel of immune cell types as well as a general approach for generating interpretable guidelines for manipulation of cell-type-specific targeting of engineered ligands.