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000301493 0247_ $$2ISSN$$a1099-1492
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000301493 1001_ $$00000-0002-1472-9471$$aSchüre, Jan-Rüdiger$$b0
000301493 245__ $$aToward Noninvasive High-Resolution In Vivo pH Mapping in Brain Tumors by 31P-Informed deepCEST MRI.
000301493 260__ $$aNew York, NY$$bWiley$$c2025
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000301493 520__ $$aThe 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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000301493 650_7 $$2Other$$a31P‐MRS
000301493 650_7 $$2Other$$aAI
000301493 650_7 $$2Other$$aAPTw
000301493 650_7 $$2Other$$aCEST
000301493 650_7 $$2Other$$abrain tumor
000301493 650_7 $$2Other$$adeep learning
000301493 650_7 $$2Other$$aintracellular pH
000301493 650_7 $$2Other$$apHi
000301493 650_2 $$2MeSH$$aHumans
000301493 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000301493 650_2 $$2MeSH$$aHydrogen-Ion Concentration
000301493 650_2 $$2MeSH$$aBrain Neoplasms: diagnostic imaging
000301493 650_2 $$2MeSH$$aMale
000301493 650_2 $$2MeSH$$aFemale
000301493 650_2 $$2MeSH$$aMiddle Aged
000301493 650_2 $$2MeSH$$aAdult
000301493 650_2 $$2MeSH$$aNeural Networks, Computer
000301493 7001_ $$aRajput, Junaid$$b1
000301493 7001_ $$aShrestha, Manoj$$b2
000301493 7001_ $$aDeichmann, Ralf$$b3
000301493 7001_ $$aHattingen, Elke$$b4
000301493 7001_ $$aMaier, Andreas$$b5
000301493 7001_ $$0P:(DE-He78)054fd7a5195b75b11fbdc5c360276011$$aNagel, Armin M$$b6$$udkfz
000301493 7001_ $$aDörfler, Arnd$$b7
000301493 7001_ $$00000-0001-7464-0236$$aSteidl, Eike$$b8
000301493 7001_ $$aZaiss, Moritz$$b9
000301493 773__ $$0PERI:(DE-600)2002003-X$$a10.1002/nbm.70060$$gVol. 38, no. 6, p. e70060$$n6$$pe70060$$tNMR in biomedicine$$v38$$x0952-3480$$y2025
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