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| Home > Publications database > Toward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by 31P-Informed deepCEST MRI. |
| Home > Publications database > Toward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by 31P-Informed deepCEST MRI. |
| Journal Article | DKFZ-2025-01035 |
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2025
Wiley
New York, NY
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Please use a persistent id in citations: doi:10.1002/nbm.70060
Abstract: 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.
Keyword(s): Humans (MeSH) ; Magnetic Resonance Imaging (MeSH) ; Hydrogen-Ion Concentration (MeSH) ; Brain Neoplasms: diagnostic imaging (MeSH) ; Male (MeSH) ; Female (MeSH) ; Middle Aged (MeSH) ; Adult (MeSH) ; Neural Networks, Computer (MeSH) ; 31P‐MRS ; AI ; APTw ; CEST ; brain tumor ; deep learning ; intracellular pH ; pHi
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