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Small beams, fast predictions A comparison of machine learning dose prediction models for proton minibeam therapy.

Journal Article


Abstract


  • Background

    Dose calculations for novel radiotherapy cancer treatments such as proton minibeam radiation therapy is often done using full Monte Carlo (MC) simulations. As MC simulations can be very time consuming for this kind of application, deep learning models have been considered to accelerate dose estimation in cancer patients.

    Purpose

    This work systematically evaluates the dose prediction accuracy, speed and generalisation performance of three selected state-of-the-art deep learning models for dose prediction applied to proton minibeam therapy. The strengths and weaknesses of those models are thoroughly investigated, helping other researchers to decide on a viable algorithm for their own application.

    Methods

    The following recently published models are compared: first, a 3D U-Net model trained as a regression network, second, a 3D U-Net trained as a generator of a generative adversarial network (GAN) and third, a dose transformer model which interprets the dose prediction as a sequence translation task. These models are trained to emulate the result of MC simulations. The dose depositions of a proton minibeam with a diameter of 800 μm and an energy of 20-100 MeV inside a simple head phantom calculated by full Geant4 MC simulations are used as a case study for this comparison. The spatial resolution is 0.5 mm. Special attention is put on the evaluation of the generalisation performance of the investigated models.

    Results

    Dose predictions with all models are produced in the order of a second on a GPU, the 3D U-Net models being fastest with an average of 130 ms. An investigated 3D U-Net regression model is found to show the strongest performance with overall 61.0±0.5\% of all voxels exhibiting a deviation in energy deposition prediction of less than 3\% compared to full MC simulations with no spatial deviation allowed. The 3D U-Net models are observed to show better generalisation performance for target geometry variations while the transformer-based model shows better generalisation with regard to the proton energy.

    Conclusions

    This paper reveals that (1) all studied deep learning models are significantly faster than non-machine learning approaches predicting the dose in the order of seconds compared to hours for MC, (2) all models provide reasonable accuracy and (3) the regression-trained 3D U-Net provides the most accurate predictions. This article is protected by copyright. All rights reserved.

Publication Date


  • 2022

Citation


  • Mentzel, F., Kröninger, K., Lerch, M., Nackenhorst, O., Rosenfeld, A., Tsoi, A. C., . . . Guatelli, S. (2022). Small beams, fast predictions A comparison of machine learning dose prediction models for proton minibeam therapy.. Medical physics. doi:10.1002/mp.16066

Web Of Science Accession Number


Abstract


  • Background

    Dose calculations for novel radiotherapy cancer treatments such as proton minibeam radiation therapy is often done using full Monte Carlo (MC) simulations. As MC simulations can be very time consuming for this kind of application, deep learning models have been considered to accelerate dose estimation in cancer patients.

    Purpose

    This work systematically evaluates the dose prediction accuracy, speed and generalisation performance of three selected state-of-the-art deep learning models for dose prediction applied to proton minibeam therapy. The strengths and weaknesses of those models are thoroughly investigated, helping other researchers to decide on a viable algorithm for their own application.

    Methods

    The following recently published models are compared: first, a 3D U-Net model trained as a regression network, second, a 3D U-Net trained as a generator of a generative adversarial network (GAN) and third, a dose transformer model which interprets the dose prediction as a sequence translation task. These models are trained to emulate the result of MC simulations. The dose depositions of a proton minibeam with a diameter of 800 μm and an energy of 20-100 MeV inside a simple head phantom calculated by full Geant4 MC simulations are used as a case study for this comparison. The spatial resolution is 0.5 mm. Special attention is put on the evaluation of the generalisation performance of the investigated models.

    Results

    Dose predictions with all models are produced in the order of a second on a GPU, the 3D U-Net models being fastest with an average of 130 ms. An investigated 3D U-Net regression model is found to show the strongest performance with overall 61.0±0.5\% of all voxels exhibiting a deviation in energy deposition prediction of less than 3\% compared to full MC simulations with no spatial deviation allowed. The 3D U-Net models are observed to show better generalisation performance for target geometry variations while the transformer-based model shows better generalisation with regard to the proton energy.

    Conclusions

    This paper reveals that (1) all studied deep learning models are significantly faster than non-machine learning approaches predicting the dose in the order of seconds compared to hours for MC, (2) all models provide reasonable accuracy and (3) the regression-trained 3D U-Net provides the most accurate predictions. This article is protected by copyright. All rights reserved.

Publication Date


  • 2022

Citation


  • Mentzel, F., Kröninger, K., Lerch, M., Nackenhorst, O., Rosenfeld, A., Tsoi, A. C., . . . Guatelli, S. (2022). Small beams, fast predictions A comparison of machine learning dose prediction models for proton minibeam therapy.. Medical physics. doi:10.1002/mp.16066

Web Of Science Accession Number