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@ARTICLE{Schre:301493,
      author       = {J.-R. Schüre and J. Rajput and M. Shrestha and R.
                      Deichmann and E. Hattingen and A. Maier and A. M. Nagel$^*$
                      and A. Dörfler and E. Steidl and M. Zaiss},
      title        = {{T}oward {N}oninvasive {H}igh-{R}esolution {I}n {V}ivo p{H}
                      {M}apping in {B}rain {T}umors by 31{P}-{I}nformed deep{CEST}
                      {MRI}.},
      journal      = {NMR in biomedicine},
      volume       = {38},
      number       = {6},
      issn         = {0952-3480},
      address      = {New York, NY},
      publisher    = {Wiley},
      reportid     = {DKFZ-2025-01035},
      pages        = {e70060},
      year         = {2025},
      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.},
      keywords     = {Humans / Magnetic Resonance Imaging / Hydrogen-Ion
                      Concentration / Brain Neoplasms: diagnostic imaging / Male /
                      Female / Middle Aged / Adult / Neural Networks, Computer /
                      31P‐MRS (Other) / AI (Other) / APTw (Other) / CEST (Other)
                      / brain tumor (Other) / deep learning (Other) /
                      intracellular pH (Other) / pHi (Other)},
      cin          = {E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40374188},
      pmc          = {pmc:PMC12081166},
      doi          = {10.1002/nbm.70060},
      url          = {https://inrepo02.dkfz.de/record/301493},
}