Purpose: The Bloch-McConnell equations can be used to fit CEST spectra to estimate the proton chemical exchange rates (kₑₓ) and pH of a sample. This study aims to test if the inclusion of T₁, T₂, B₁ and B₀ experimental values improve the accuracy of the output fitting results.
Methods: Two-hundred iopamidol samples were prepared with 5 concentrations, 5 T₁ values and 8 pH levels (6.25-7.30, equally spaced). Thirty-six CEST scans were performed on each sample using a FISP sequence and a cross-combination of six saturation powers (0.5-6 μT) and six saturation times (0.5-6 sec) with a Bruker 7T preclinical scanner. T₁, T₂, B₁ and B₀ maps were also acquired. In addition, acidoCEST MRI data were acquired with 4 μT saturation power and 6 sec saturation time from five mice with a subcutaneous 4T1 tumor. Image analysis and Bloch-fitting was performed with MATLAB.
Results: Our Bloch fitting algorithm can detect the increase of amide kₑₓ with increasing sample pH and temperatures. Our analysis of Lin’s concordance index has revealed that the improvement to the accuracy of the fitting results from the Bloch fitting method does not warrant the time burden for acquiring additional experimental information. Performance of the Bloch fitting method varies with saturation parameters, and we recommend acquiring acidoCEST data with 3 μT or 4 μT saturation power and > 1 sec saturation time. Our Bloch-fitting algorithm fits extracellular pH effectively from within 4T1 tumors in mice.
Conclusion: Our Bloch-fitting algorithm is capable of detecting the changes of kₑₓ of iopamidol exchangeable protons at different pH levels, which allows for an accurate pH measurement. Although the inclusion of experimental information in the Bloch-fitting process does not improve the accuracy of fitting results, we have found that proper selection of saturation parameters affects the accuracy and speed of the Bloch-fitting analysis.
Funding Support, Disclosures, and Conflict of Interest: This work is supported by the National Institute of Health under grant no. R01 CA231513.