Goetz, Theresa and Lang, Elmar W. and Schmidkonz, Christian and Kuwert, Torsten and Ludwig, Bernd (2021) Dose voxel kernel prediction with neural networks for radiation dose estimation. ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK, 31 (1). pp. 23-36. ISSN 0939-3889, 1876-4436
Full text not available from this repository. (Request a copy)Abstract
Background: Currently there is an ever increasing interest in Lu-177 targeted radionuclide therapies, which target neuroendocrine and prostate tumours. For a patient-specific treatment, an individual dosimetry based on SPECT/CT imaging is necessary. The aim of this study is to introduce a dosimetry method, where dose voxel kernels (DVK) are predicted by a neural network. Methods: Kidneys are considered one of the most critical organs in any radionuclide therapy. Hence we chose kidneys of 26 patients, who underwent Lu-177-DOTATOC or PSMA therapy, as target organs for our dosimetric method. First of all, density kernels with a size of 9 x 9 x 9 voxels were considered, and the corresponding DVKs were calculated by Monte Carlo simulations. These kernels were used to train a neural network (NN), which received a density kernel as input and predicted a DVK at the output. To predict the dose distribution of an entire kidney, the organ had to be partitioned into a large number of density kernels. Afterwards the DVKs were predicted by a trained NN, and employed to reconstruct the whole-organ dose distribution by convolution with the activity distribution. This method was compared to the standard method where the activity distribution is convolved with a DVK based on a homogeneous soft tissue kernel. Results: The number of training kernels amounted to 52,274 density kernels with corresponding MC-derived DVKs. The results serve as proof of principle of the newly proposed method and showed that the NN approach yielded superior results compared to the standard method with no additional computational effort. Conclusion: The NN approach is an accurate and highly competitive dosimetric method to precisely estimate absorbed radiation dose in critical organs like kidneys in clinical routine. To further improve the results, a larger number of DVKs needs to be computed by Monte Carlo simulations. An extension of the method to other organs is easily conceivable.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | S-VALUES; DISTRIBUTIONS; SIMULATIONS; DOSIMETRY; Neural networks; Dose voxel kernel; Radiation dose estimation; Patient-specific dosimetry |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 500 Science > 570 Life sciences |
| Divisions: | Languages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Professur für Informationslinguistik (Prof. Dr. Bernd Ludwig) Informatics and Data Science > Professur für Informationslinguistik (Prof. Dr. Bernd Ludwig) Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 05 Sep 2022 05:40 |
| Last Modified: | 05 Sep 2022 05:44 |
| URI: | https://pred.uni-regensburg.de/id/eprint/46503 |
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