Home > Publications database > Toward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by 31P-Informed deepCEST MRI. > print

001     301493
005     20250521113230.0
024 7 _ |a 10.1002/nbm.70060
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024 7 _ |a 1099-1492
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037 _ _ |a DKFZ-2025-01035
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Schüre, Jan-Rüdiger
|0 0000-0002-1472-9471
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245 _ _ |a Toward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by 31P-Informed deepCEST MRI.
260 _ _ |a New York, NY
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520 _ _ |a 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.
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650 _ 7 |a 31P‐MRS
|2 Other
650 _ 7 |a AI
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650 _ 7 |a APTw
|2 Other
650 _ 7 |a CEST
|2 Other
650 _ 7 |a brain tumor
|2 Other
650 _ 7 |a deep learning
|2 Other
650 _ 7 |a intracellular pH
|2 Other
650 _ 7 |a pHi
|2 Other
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Magnetic Resonance Imaging
|2 MeSH
650 _ 2 |a Hydrogen-Ion Concentration
|2 MeSH
650 _ 2 |a Brain Neoplasms: diagnostic imaging
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Adult
|2 MeSH
650 _ 2 |a Neural Networks, Computer
|2 MeSH
700 1 _ |a Rajput, Junaid
|b 1
700 1 _ |a Shrestha, Manoj
|b 2
700 1 _ |a Deichmann, Ralf
|b 3
700 1 _ |a Hattingen, Elke
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700 1 _ |a Maier, Andreas
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700 1 _ |a Nagel, Armin M
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700 1 _ |a Dörfler, Arnd
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700 1 _ |a Steidl, Eike
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700 1 _ |a Zaiss, Moritz
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773 _ _ |a 10.1002/nbm.70060
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