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Theresa I Götz1, Elmar W Lang2, Christian Schmidkonz3

  • 1Clinic of Nuclear Medicine, University Hospital Erlangen, 91054 Erlangen, Germany; CIML Group, Biophysics, University of Regensburg, 93040 Regensburg, Germany; Information Sciences, University of Regensburg, 93053 Regensburg, Germany.

Zeitschrift Fur Medizinische Physik
|October 23, 2020
PubMed
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A novel neural network (NN) method accurately predicts radiation dose distributions for Lutetium-177 (Lu-177) therapies. This approach enhances patient-specific dosimetry for neuroendocrine and prostate tumors, offering superior results to standard methods without increased computational cost.

Area of Science:

  • Nuclear Medicine
  • Medical Physics
  • Radiotherapy

Background:

  • Increasing interest in Lutetium-177 (Lu-177) targeted radionuclide therapies for neuroendocrine and prostate tumors.
  • Need for patient-specific dosimetry using SPECT/CT imaging for effective Lu-177 treatments.
  • Current dosimetry methods rely on standard dose voxel kernels (DVKs) which may lack precision for complex organs.

Purpose of the Study:

  • To introduce and evaluate a novel dosimetry method utilizing a neural network (NN) to predict dose voxel kernels (DVKs).
  • To assess the accuracy and efficiency of the NN-based dosimetry compared to standard methods.
  • To apply the method to kidney dosimetry in patients undergoing Lu-177 therapies.

Main Methods:

  • Kidneys from 26 patients treated with Lu-177-DOTATOC or PSMA therapy were selected as target organs.
Keywords:
Dose voxel kernelNeural networksPatient-specific dosimetryRadiation dose estimation

Related Experiment Videos

  • Dose voxel kernels (DVKs) were calculated using Monte Carlo simulations for density kernels of 9x9x9 voxels.
  • A neural network (NN) was trained to predict DVKs from density kernels, and applied to reconstruct whole-organ dose distributions.

    • Main Results:

    • The study trained a neural network on 52,274 density kernels with corresponding Monte Carlo-derived DVKs.
    • The NN approach demonstrated superior accuracy in dose distribution prediction compared to the standard method.
    • The proposed NN method achieved these improved results with no additional computational effort.

    Conclusions:

    • The neural network approach provides an accurate and competitive method for precise absorbed radiation dose estimation in critical organs like kidneys.
    • This dosimetry method is suitable for clinical routine and enhances patient-specific Lu-177 radionuclide therapy.
    • Future work includes computing a larger set of DVKs and extending the method to other organs.