Browsing by Author "Pinto, M"

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    Comparative study of alternative Geant4 hadronic ion inelastic physics models for prediction of positron-emitting radionuclide production in carbon and oxygen ion therapy
    (IOP Publishing, 2019-08-01) Chacon, A; Guatelli, S; Rutherford, H; Bolst, D; Mohammadi, A; Ahmed, A; Nitta, M; Nishikido, F; Iwao, Y; Tashima, H; Yoshida, E; Akamatsu, G; Takyu, S; Kitagawa, A; Hofmann, T; Pinto, M; Franklin, DR; Parodi, K; Yamaya, T; Rosenfeld, AB; Safavi-Naeini, M
    The distribution of fragmentation products predicted by Monte Carlo simulations of heavy ion therapy depend on the hadronic physics model chosen in the simulation. This work aims to evaluate three alternative hadronic inelastic fragmentation physics options available in the Geant4 Monte Carlo radiation physics simulation framework to determine which model most accurately predicts the production of positron-emitting fragmentation products observable using in-beam PET imaging. Fragment distributions obtained with the BIC, QMD, and INCL + + physics models in Geant4 version 10.2.p03 are compared to experimental data obtained at the HIMAC heavy-ion treatment facility at NIRS in Chiba, Japan. For both simulations and experiments, monoenergetic beams are applied to three different block phantoms composed of gelatin, poly(methyl methacrylate) and polyethylene. The yields of the positron-emitting nuclei 11C, 10C and 15O obtained from simulations conducted with each model are compared to the experimental yields estimated by fitting a multi-exponential radioactive decay model to dynamic PET images using the normalised mean square error metric in the entrance, build up/Bragg peak and tail regions. Significant differences in positron-emitting fragment yield are observed among the three physics models with the best overall fit to experimental 12C and 16O beam measurements obtained with the BIC physics model. © 2019 Commonwealth of Australia, Australian Nuclear Science and Technology Organisation, ANSTO.
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    Dose quantification in carbon ion therapy using in-beam positron emission tomography
    (IOP Publishing, 2020-12-07) Rutherford, H; Chacon, A; Mohammadi, A; Takyu, S; Tashima, H; Yoshida, E; Nishikido, F; Hofmann, T; Pinto, M; Franklin, DR; Yamaya, T; Parodi, K; Rosenfeld, AB; Guatelli, S; Safavi-Naeini, M
    This work presents an iterative method for the estimation of the absolute dose distribution in patients undergoing carbon ion therapy, via analysis of the distribution of positron annihilations resulting from the decay of positron-emitting fragments created in the target volume. The proposed method relies on the decomposition of the total positron-annihilation distributions into profiles of the three principal positron-emitting fragment species - 11C, 10C and 15O. A library of basis functions is constructed by simulating a range of monoenergetic 12C ion irradiations of a homogeneous polymethyl methacrylate phantom and measuring the resulting one-dimensional positron-emitting fragment profiles and dose distributions. To estimate the dose delivered during an arbitrary polyenergetic irradiation, a linear combination of factors from the fragment profile library is iteratively fitted to the decomposed positron annihilation profile acquired during the irradiation, and the resulting weights combined with the corresponding monoenergetic dose profiles to estimate the total dose distribution. A total variation regularisation term is incorporated into the fitting process to suppress high-frequency noise. The method was evaluated with 14 different polyenergetic 12C dose profiles in a polymethyl methacrylate target: one which produces a flat biological dose, 10 with randomised energy weighting factors, and three with distinct dose maxima or minima within the spread-out Bragg peak region. The proposed method is able to calculate the dose profile with mean relative errors of 0.8%, 1.0% and 1.6% from the 11C, 10C, 15O fragment profiles, respectively, and estimate the position of the distal edge of the SOBP to within an average of 0.7 mm, 1.9 mm and 1.2 mm of its true location. © 2020 Commonwealth of Australia, ANSTO
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    Dose reconstruction from PET images in carbon ion therapy: a deconvolution approach
    (IOP Publishing, 2019-01-01) Hofmann, T; Pinto, M; Mohammadi, A; Nitta, M; Nishikido, F; Iwao, Y; Tashima, H; Yoshida, E; Chacon, A; Safavi-Naeini, M; Rosenfeld, AB; Yamaya, T; Parodi, K
    Dose and range verification have become important tools to bring carbon ion therapy to a higher level of confidence in clinical applications. Positron emission tomography is among the most commonly used approaches for this purpose and relies on the creation of positron emitting nuclei in nuclear interactions of the primary ions with tissue. Predictions of these positron emitter distributions are usually obtained from time-consuming Monte Carlo simulations or measurements from previous treatment fractions, and their comparison to the current, measured image allows for treatment verification. Still, a direct comparison of planned and delivered dose would be highly desirable, since the dose is the quantity of interest in radiation therapy and its confirmation improves quality assurance in carbon ion therapy. In this work, we present a deconvolution approach to predict dose distributions from PET images in carbon ion therapy. Under the assumption that the one-dimensional PET distribution is described by a convolution of the depth dose distribution and a filter kernel, an evolutionary algorithm is introduced to perform the reverse step and predict the depth dose distribution from a measured PET distribution. Filter kernels are obtained from either a library or are created for any given situation on-the-fly, using predictions of the β + -decay and depth dose distributions, and the very same evolutionary algorithm. The applicability of this approach is demonstrated for monoenergetic and polyenergetic carbon ion irradiation of homogeneous and heterogeneous solid phantoms as well as a patient computed tomography image, using Monte Carlo simulated distributions and measured in-beam PET data. Carbon ion ranges are predicted within less than 0.5 mm and 1 mm deviation for simulated and measured distributions, respectively. © 2019 Institute of Physics and Engineering in Medicine.
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    Dose reconstruction from PET images in carbon ion therapy: a deconvolution approach using an evolutionary algorithm
    (Institute of Electrical and Electronics Engineers, 2017-10-28) Hofmann, T; Fochi, A; Pinto, M; Mohammadi, A; Nitta, M; Nishikido, F; Iwao, Y; Tashima, H; Yoshida, E; Safavi-Naeini, M; Chacon, A; Rosenfeld, AB; Yamaya, T; Parodi, K
    Dose monitoring and range verification are important tools in carbon ion therapy. For their implementation, positron emission tomography (PET) can be used to image the β+-activation of tissue during treatment. Predictions of these β+-activity distributions are usually obtained from Monte Carlo simulations, which demands high computational time and thus limits the applicability of this technique in clinical scenario. Nevertheless, it is desirable to explore faster approaches able to give such a prediction, since only its comparison with the measured distributions allows a definite assessment of potential range deviations from the planned treatment. For the first time, we present an approach to perform deconvolution from PET data in carbon ion therapy and reconstruct the dose. A filtering method is used to predict positron emitter profiles from dose profiles in short time. In order to reverse the convolution and estimate a dose distribution from a positron emitter distribution, we apply an evolutionary algorithm. Filters are obtained from either a library or are created in advance for a specific problem, assuming that a prediction of the positron emitter distribution is available. To perform the latter method and find the best filter for a specific problem, we use another evolutionary algorithm, hence optimizing the filter on-the-fly for the given treatment scheme. The application of our method is shown for dose and positron emitter distributions in homogeneous phantoms using simulated and newly measured online PET data. Carbon ion ranges can be predicted within 2 mm and the shape of the dose distribution is reconstructed with an overall promising agreement.