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The positron range, that is, the distance from the positron emission point to the annihilation one in β+ decay, limits the spatial resolution of PET images. Thus, any realistic simulation of PET acquisitions must include this effect as accurately as possible. Positron range distributions have been computed and compared in the literature. Not all authors present it in the same way. For instance, accumulated range distributions, 1D projection of the positrons annihilation coordinates, or angular integrated 3D radial distributions, have been used. Any of these presentations may emphasize a different physical property. Thus it is difficult to perform a meaningful comparison of the results obtained by different authors. The aim of this work is to present a general framework to compare positron range distributions. We use a genetic algorithm to fit the radial, angle integrated, g3D(r) distribution of the annihilation points to any positron annihilation distribution chosen as reference. Therefore, given a positron range distribution presented in a certain way, we can obtain the distributions, first fitting g3D and then using the relations between distributions. Using this procedure, positron range distributions available in the literature are compared with the ones computed with the simulation package PeneloPET

Monte Carlo (MC) methods are a very powerful tool to compute dose in radiotherapy, as all effects that need to be considered, such as material inhomogeneities, back-scatter from large bones, and beam hardening can be properly modeled. Their most important caveat, however, besides computation time, is that they need a realistic and reliable description of the electron and/or photon beam that delivers the dose, and this is not usually available. In this respect, Monte Carlo (MC) methods have been an invaluable tool for realistic modeling of medical therapy accelerators, including electron linear accelerators (LINAC) used in Intra-operative Radiation Therapy (IORT). The purpose of this work is to obtain the radiation beam properties (or phase-space fro the beam or PHSP) at phantom surface based on a set of dose measurements, without the need for a detailed simulation of the accelerator head and/or applicator. An iterative reconstruction algorithm (EM-ML), commonly used in tomographic image reconstruction, has been employed to optimize iteratively all aspects of the PHSP, such as energy spectra, particle type and fluency, and angle of particle emission, required by the MC dose calculation code Dose Planning Method (DPM). Phase space files for IORT have been derived for different energies and applicator diameters, which yield dose in good agreement with the measurements.

Proceeding of: 2011 Nuclear Science Symposium and Medical Imaging Conference, Valencia, España, 23-29 October, 2011 In this work, we investigate PET imaging with 68Ga and 66Ga after proton irradiation on a natural zinc foil. The nuclides 68Ga and 66Ga are ideally suited for off line PET monitoring of proton radiotherapy due to their beta decay halflives of 67.71(9) minutes and 9.49(3) hours, respectively, and suitable fl end point energy. The purpose of this work is to explore the feasibility of PET monitoring in hadrontherapy treatments, and to study how the amount of activity and the positron range affect the PET image reconstruction. Profiting from the low energy reaction threshold for production via (p,n) reactions, both 68Ga and 66Ga gallium isotopes have been produced by activation on a natural zinc target by a proton pencil beam. In this way, it is possible to create detailed patterns, such as the Derenzo inspired one employed here. The proton beam was produced by the 5 MV tandetron accelerator at CMAM in Madrid. The energy of this beam (up to 10 MeV) is similar to the residual energy of the protons used for therapy at the distal edge of their path. The activated target was imaged in an ARGUS small animal PETtCT scanner and reconstructed with a fully 3D iterative algorithm, with and without positron range corrections. This work was supported in part by Comunidad de Madrid (ARTEMIS S2009/DPI 1802), Spanish Ministry of Science and Innovation (grants FPA2010 17142 and ENTEPRASE, PSE 300000 2009 5), by European Regional Funds, by CDTI under the CENIT Programme (AMIT Project), UCM (grupos UCM, 910059) and by CPAN, CSPD 2007 00042@lngenio2010. Publicado