%0 Journal Article
%A Schüre, Jan-Rüdiger
%A Rajput, Junaid
%A Shrestha, Manoj
%A Deichmann, Ralf
%A Hattingen, Elke
%A Maier, Andreas
%A Nagel, Armin M
%A Dörfler, Arnd
%A Steidl, Eike
%A Zaiss, Moritz
%T Toward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by 31P-Informed deepCEST MRI.
%J NMR in biomedicine
%V 38
%N 6
%@ 0952-3480
%C New York, NY
%I Wiley
%M DKFZ-2025-01035
%P e70060
%D 2025
%X The intracellular pH (pHi) is critical for understanding various pathologies, including brain tumors. While conventional pHi measurement through 31P-MRS suffers from low spatial resolution and long scan times, 1H-based APT-CEST imaging offers higher resolution with shorter scan times. This study aims to directly predict 31P-pHi 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 1H-based CEST and T1 measurement, as well as 31P-MRS. A neural network was trained voxel-wise on CEST and T1 data to predict 31P-pHi values, using data from 11 patients for training and 4 for testing. The predicted pHi maps were additionally down-sampled to the original the 31P-pHi 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 pHi maps and the measured 31P-pHi 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 pHi neural network enables the APT-CEST hidden pH-sensitivity and offers pHi maps with higher spatial resolution in shorter scan time compared with 31P-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.
%K Humans
%K Magnetic Resonance Imaging
%K Hydrogen-Ion Concentration
%K Brain Neoplasms: diagnostic imaging
%K Male
%K Female
%K Middle Aged
%K Adult
%K Neural Networks, Computer
%K 31P‐MRS (Other)
%K AI (Other)
%K APTw (Other)
%K CEST (Other)
%K brain tumor (Other)
%K deep learning (Other)
%K intracellular pH (Other)
%K pHi (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40374188
%2 pmc:PMC12081166
%R 10.1002/nbm.70060
%U https://inrepo02.dkfz.de/record/301493