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Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols, and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at the address http://www-lphe.epfl.ch/GATE/.
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We study energy deposition by light nuclei in tissue-like media taking into account nuclear fragmentation reactions, in particular, production of secondary neutrons. The calculations are carried out within a Monte Carlo model for Heavy-Ion Therapy (MCHIT) based on the GEANT4 toolkit. Experimental data on depth-dose distributions for 135A-400A MeV C-12 and O-18 beams are described very well without any adjustment of the model parameters. This gives confidence in successful use of the GEANT4 toolkit for MC simulations of cancer therapy with beams of light nuclei. The energy deposition due to secondary neutrons produced by C-12 and Ne-20 beams in a (40-50 cm)^3 water phantom is estimated to 1-2% of the total dose, that is only slightly above the neutron contribution (~1%) induced by a 200 MeV proton beam.
We study the spatial distributions of $beta^+$-activity produced by therapeutic beams of $^3$He and $^{12}$C ions in various tissue-like materials. The calculations were performed within a Monte Carlo model for Heavy-Ion Therapy (MCHIT) based on the GEANT4 toolkit. The contributions from $^{10,11}$C, $^{13}$N, $^{14,15}$O, $^{17,18}$F and $^{30}$P positron-emitting nuclei were calculated and compared with experimental data obtained during and after irradiation. Positron emitting nuclei are created by $^{12}$C beam in fragmentation reactions of projectile and target nuclei. This leads to a $beta^+$-activity profile characterised by a noticeable peak located close to the Bragg peak in the corresponding depth-dose distribution. On the contrary, as the most of positron-emitting nuclei are produced by $^3$He beam in target fragmentation reactions, the calculated total $beta^+$-activity during or soon after the irradiation period is evenly distributed within the projectile range. However, we predict also the presence of $^{13}$N, $^{14}$O, $^{17,18}$F created in charge-transfer reactions by low-energy $^3$He ions close to the end of their range in several tissue-like media. The time evolution of $beta^+$-activity profiles was investigated for both kinds of beams. Due to the production of $^{18}$F nuclide the $beta^+$-activity profile measured 2 or 3 hours after irradiation with $^{3}$He ions will have a distinct peak correlated with the maximum of depth-dose distribution. We found certain advantages of low-energy $^{3}$He beams over low-energy proton beams for reliable PET monitoring during particle therapy of shallow located tumours. In this case the distal edge of $beta^+$-activity distribution from $^{17}$F nuclei clearly marks the range of $^{3}$He in tissues.
Reliable attenuation and scatter compensation (ASC) is a prerequisite for quantification and beneficial for visual interpretation tasks in SPECT. In this paper, we develop a reconstruction method that uses the entire SPECT emission data, i.e. data in both the photopeak and scatter windows, acquired in list-mode format and including the energy attribute of the detected photon, to perform ASC. We implemented a GPU-based version of this method using an ordered subsets expectation maximization (OSEM) algorithm. The method was objectively evaluated using realistic simulation studies on the task of estimating uptake in the striatal regions of the brain in a 2-D dopamine transporter (DaT)-scan SPECT study. We observed that inclusion of data from the scatter window and using list-mode data yielded improved quantification compared to using data only from the photopeak window or using binned data. These results motivate further development of list-mode-based ASC methods that include scatter-window data for SPECT.
Attenuation compensation (AC) is a pre-requisite for reliable quantification and beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT). Typical AC methods require the availability of an attenuation map obtained using a transmission scan, such as a CT scan. This has several disadvantages such as increased radiation dose, higher costs, and possible misalignment between SPECT and CT scans. Also, often a CT scan is unavailable. In this context, we and others are showing that scattered photons in SPECT contain information to estimate the attenuation distribution. To exploit this observation, we propose a physics and learning-based method that uses the SPECT emission data in the photopeak and scatter windows to perform transmission-less AC in SPECT. The proposed method uses data acquired in the scatter window to reconstruct an initial estimate of the attenuation map using a physics-based approach. A convolutional neural network is then trained to segment this initial estimate into different regions. Pre-defined attenuation coefficients are assigned to these regions, yielding the reconstructed attenuation map, which is then used to reconstruct the activity map using an ordered subsets expectation maximization-based reconstruction approach. We objectively evaluated the performance of this method using a highly realistic simulation study conducted on the clinically relevant task of detecting perfusion defects in myocardial perfusion SPECT. Our results showed no statistically significant differences between the performance achieved using the proposed method and that with the true attenuation maps. Visually, the images reconstructed using the proposed method looked similar to those with the true attenuation map. Overall, these results provide evidence of the capability of the proposed method to perform transmission-less AC and motivate further evaluation.
Reliable (accurate and precise) quantification of dose requires reliable absolute quantification of regional activity uptake. This is especially challenging for alpha-particle emitting radiopharmaceutical therapies ({alpha}-RPTs) due to the complex emission spectra, the very low number of detected counts, the impact of stray-radiation-related noise at these low counts, and other image-degrading processes such as attenuation, scatter, and collimator-detector response. The conventional reconstruction-based quantification methods are observed to be erroneous for {alpha}-RPT SPECT. To address these challenges, we developed an ultra-low-count quantitative SPECT (ULC-QSPECT) method that incorporates multiple strategies to perform reliable quantification. First, the method directly estimates the regional activity uptake from the projection data, obviating the reconstruction step. This makes the problem more well-posed and avoids reconstruction-related information loss. Next, the method compensates for radioisotope and SPECT physics, including the isotope spectra, scatter, attenuation, and collimator-detector response, using a Monte Carlo-based approach. Further, the method compensates for stray-radiation-related noise that becomes substantial at these low-count levels. The method was validated in the context of three-dimensional SPECT with 223Ra. Validation was performed using both realistic simulation studies, as well as synthetic and anthropomorphic physical-phantom studies. Across all studies, the ULC-QSPECT method yielded reliable estimates of regional uptake and outperformed conventional ordered subset expectation maximization (OSEM)-based reconstruction and geometric transfer matrix (GTM)-based partial-volume compensation methods. Further, the method yielded reliable estimates of mean uptake in lesions with varying intra-lesion heterogeneity in uptake.
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