Toward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by P-Informed deepCEST MRI.

The intracellular pH (pH) is critical for understanding various pathologies, including brain tumors. While conventional pH measurement through P-MRS suffers from low spatial resolution and long scan times, H-based APT-CEST imaging offers higher resolution with shorter scan times. This study aims to directly predict P-pH maps from CEST data by using a fully connected neuronal network. Fifteen tumor patients were scanned on a 3-T Siemens PRISMA scanner and received H-based CEST and T1 measurement, as well as P-MRS. A neural network was trained voxel-wise on CEST and T1 data to predict P-pH values, using data from 11 patients for training and 4 for testing. The predicted pH maps were additionally down-sampled to the original the P-pH resolution, to be able to calculate the RMSE and analyze the correlation, while higher resolved predictions were compared with conventional CEST metrics. The results demonstrated a general correspondence between the predicted deepCEST pH maps and the measured P-pH in test patients. However, slight discrepancies were also observed, with a RMSE of 0.04 pH units in tumor regions. High-resolution predictions revealed tumor heterogeneity and features not visible in conventional CEST data, suggesting the model captures unique pH information and is not simply a T1 segmentation. The deepCEST pH neural network enables the APT-CEST hidden pH-sensitivity and offers pH maps with higher spatial resolution in shorter scan time compared with P-MRS. Although this approach is constrained by the limitations of the acquired data, it can be extended with additional CEST features for future studies, thereby offering a promising approach for 3D pH imaging in a clinical environment.