Toward Noninvasive High‐Resolution In Vivo pH Mapping in Brain Tumors by 31P‐Informed deepCEST MRI

ABSTRACT The intracellular pH (pH i ) is critical for understanding various pathologies, including brain tumors. While conventional pH i measurement through 31 P‐MRS suffers from low spatial resolution and long scan times, 1 H‐based APT‐CEST imaging offers higher resolution with shorter scan times. This study aims to directly predict 31 P‐pH i 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 1 H‐based CEST and T1 measurement, as well as 31 P‐MRS. A neural network was trained voxel‐wise on CEST and T1 data to predict 31 P‐pH i values, using data from 11 patients for training and 4 for testing. The predicted pH i maps were additionally down‐sampled to the original the 31 P‐pH i 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 i maps and the measured 31 P‐pH i 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 i neural network enables the APT‐CEST hidden pH‐sensitivity and offers pH i maps with higher spatial resolution in shorter scan time compared with 31 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.