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@ARTICLE{Xiao:305638,
      author       = {F. Xiao and D. Radonic and N. Wahl$^*$ and N. Delopoulos
                      and A. Thummerer and S. Corradini and C. Belka$^*$ and G.
                      Dedes and C. Kurz and G. Landry},
      title        = {{D}eep learning-based synthetic-{CT}-free photon dose
                      calculation in {MR}-guided radiotherapy: {A}
                      proof-of-concept study.},
      journal      = {Medical physics},
      volume       = {52},
      number       = {11},
      issn         = {0094-2405},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DKFZ-2025-02280},
      pages        = {e70106},
      year         = {2025},
      abstract     = {In magnetic resonance imaging (MRI)-guided online adaptive
                      radiotherapy, MRI lacks tissue attenuation information
                      necessary for accurate dose calculations. Although deep
                      learning (DL)-based synthetic computed tomography (CT)
                      generation models have been developed to obtain CT density
                      information from MRI, they usually do not meet the
                      requirement of real-time plan adaptation.We propose a
                      DL-based photon dose calculation method directly on 0.35 T
                      MRI to skip synthetic CT generation and show its feasibility
                      for prostate patient cases.The 0.35 T planning MRI and
                      deformed planning CT (registered to the planning MRI) of 34
                      prostate cancer patients treated with a 0.35 T magnetic
                      resonance-linear accelerator (MR-Linac) were collected. The
                      air cavities (ACs) in the abdominopelvic area of the
                      deformed CT images were corrected based on manual AC
                      contouring on the MRI. Monte Carlo (MC) dose simulations
                      under a 0.35 T magnetic field were performed on the
                      corrected CT images. All photon beams were simulated using a
                      uniform field size of 1 cm × 1 cm $1\,\text{cm} \times
                      1\,\text{cm}$ . 10 800 beams were simulated with 5 × 10 6
                      $5\times 10^6$ initial photons for training (20 patients)
                      and 2160 beams with 5 × 10 7 $5\times 10^7$ photons for
                      validation (4 patients). For testing, 1080 beams shooting
                      through the planning target volume (PTV) in 10 patients and
                      five optimized nine-field intensity-modulated plans were
                      simulated with 5 × 10 7 $5\times 10^7$ photons. 3D MRI
                      cuboids covering the photon beams were input into a Unet
                      model to predict AC segmentation, and 3D MRI and predicted
                      AC cuboids were input into a long short-term memory (LSTM)
                      model for beam's eye view (BEV) processing to predict dose.
                      The gamma passing rate γ pr $\gamma _{\mathrm{pr}}$
                      (2\%/2mm, D > 10 \% D max $D>10\\%D_{\mathrm{max}}$ ), beam
                      dose profiles of single beams and dose volume histogram
                      (DVH) of intensity-modulated plans were evaluated.The test
                      results for all photon beams from the proposed models
                      demonstrated a mean γ pr $\gamma _{\mathrm{pr}}$ above
                      99.50\%. The five treatment plans recalculated by the DL
                      model each achieved γ pr $\gamma _{\mathrm{pr}}$ values
                      exceeding 99.80\%. Additionally, the model's inference time
                      was approximately 12 ms per photon beam.The proposed method
                      showed that DL-based dose calculation directly on MRI is
                      feasible for prostate cases, which has the potential to
                      simplify the procedure for MRI-only workflows and can be
                      beneficial for real-time plan adaptation.},
      keywords     = {Deep Learning / Radiotherapy, Image-Guided: methods /
                      Humans / Magnetic Resonance Imaging / Photons: therapeutic
                      use / Radiotherapy Planning, Computer-Assisted: methods /
                      Proof of Concept Study / Radiotherapy Dosage / Prostatic
                      Neoplasms: radiotherapy / Prostatic Neoplasms: diagnostic
                      imaging / Radiation Dosage / Male / Monte Carlo Method /
                      Tomography, X-Ray Computed / LSTM (Other) / MRI (Other) /
                      deep learning (Other) / dose calculation (Other) / online
                      MR‐guided photon therapy (Other)},
      cin          = {E040 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41186921},
      pmc          = {pmc:PMC12584934},
      doi          = {10.1002/mp.70106},
      url          = {https://inrepo02.dkfz.de/record/305638},
}