Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed

Development and assessment of estimate methods for internal dosimetry using PET/CT

The aim of this thesis was to assess and develop internal dose calculations methods in diagnostic and therapeutic nuclear medicine procedures to patients undergone PET/CT explorations. Towards this objective, the accuracy and precision of different classical methods commonly used to estimate internal dosimetry were investigated. Biodistribution studies were used in order to compare these methods. The main study aspects included region-of-interest (ROI) delineation methods, reconstruction algorithms, scatter correction and radiopharmaceutical's biokinetic. Optimization of internal dosimetry in this thesis was completed with the development of a Monte Carlo (MC) technique for estimating the patient-specific PET/CT dosimetry. The development of a mathematical model using MC techniques allowed us to have a gold standard to which compare classical techniques and study the aspects discussed previously. It was observed that effective dose (ED) estimations were sensitive to whichever delineation ROI method was applied. Furthermore, it was perceived that the biokinetics of the radioligand also influences in the ED estimation. On the other hand, similar quantitative accuracy was found regarding image reconstruction (FBP and OSEM) and scatter correction methods studied (FSC and SSC). Analysis of the impact of inter- and intra-operator variability in dose estimations revealed higher reproducibility in 3D methods in comparison with 2D planar method. The last one, showed the highest interoperator variability, which implies an overestimation of the ED. In this dissertation, specific routines were developed to be applied with the MC code PENELOPE/penEasy to perform individualized internal dosimetry estimations. Voxel-level absorbed dose maps which include self- and cross-irradiation doses were generated from the morphological and functional patient images. Further parameters such as cumulative organ dose, maximum and minimum voxel organ values, volume of the organ and dose-volume histograms of interest were reported. The model implemented was applied to a theoretical study using simulated PET images of a voxelized Zubal phantom. The results were benchmarked with the ones obtained using the OLINDA/EXM software. The comparison was in good agreement for those organs were both phantoms considered (Zubal and the reference one in OLINDA/EXM) were close. Undoubtedly, the implementation of a patient-specific internal dosimetry method not only leads to an improvement in diagnostic examinations where the risk could be quantified, but also NM therapy could become more effective in terms that patients receiving an optimal care.L'objectiu d'aquesta tesi va ser avaluar i desenvolupar mètodes de càlcul de dosis interna en procediments de diagnòstic i terapèutics de medicina nuclear per a pacients sotmesos a exploracions PET / TC. Amb aquest objectiu, es va investigar l'exactitud i la precisió dels diferents mètodes clàssics utilitzats habitualment per estimar la dosimetria interna. Es van utilitzar estudis de biodistribució per comparar aquests mètodes. Els principals aspectes d'estudi incloïen mètodes de delimitació de la regió d'interès (ROI), algoritmes de reconstrucció, correcció de dispersió i biocinètiques de radiofàrmacs. L'optimització de la dosimetria interna en aquesta tesi es va completar amb el desenvolupament d'una tècnica de Monte Carlo (MC) per a estimar la dosimetria PET / TC específica del pacient. El desenvolupament d'un model matemàtic amb tècniques de MC ens va permetre tenir una referència amb la que comparar les tècniques clàssiques i estudiar els aspectes descrits anteriorment. Es va observar que les estimacions de la dosi efectiva (DE) eren sensibles a qualsevol mètode de delimitació de la ROI aplicada. A més a més, es va percebre que la biocinètica del radiolligand també influeix en l'estimació de la DE. D'altra banda, es va trobar una exactitud quantitativament similar pel que fa a la reconstrucció d'imatges (FBP i OSEM) i els mètodes de correcció de dispersió estudiats (FSC i SSC). L'anàlisi de l'impacte de la variabilitat entre operadors i intra-operadors en les estimacions de dosis va mostrar una major reproductibilitat en els mètodes 3D en comparació amb el mètode planar 2D. Aquest últim, va mostrar la màxima variabilitat entre operadors, la qual cosa implica una sobreestimació de la DE. En aquesta tesi, es van desenvolupar rutines específiques per aplicar-les amb el codi MC PENELOPE / penEasy per a realitzar estimacions de dosimetria interna individualitzades. Es van generar mapes de dosis absorbida a nivell de voxel que incloïen dosis d? autoirradiació i irradiació creuada a partir de les imatges morfològiques i funcionals del pacient. Es van reportar altres paràmetres d?interès com la dosi d'òrgan acumulada, els valors màxims i mínims de l'òrgan i del vòxel, el volum de l'òrgan i els histogrames de dosi-volum. El model implementat es va aplicar a un estudi teòric mitjançant imatges simulades de PET d'un maniquí de Zubal voxelitzat. Els resultats es van comparar amb els obtinguts mitjançant el programa OLINDA / EXM. Es va observar un bon acord per a aquells òrgans semblants entre el maniquí de Zubal i el maniquí de referència del software OLINDA/EXM. Sens dubte, la implementació d'un mètode de dosimetria interna específic per al pacient no només condueix a una millora en les exploracions de diagnòstic on es pot quantificar el risc d?irradiació, sinó que la teràpia amb medicina nuclear podria ser més eficaç en termes que els pacients rebin un tractament òptim.Postprint (published version

Realistic tissue dosimetry models using Monte Carlo simulations. Applications for radionuclide therapies

Radionuclide therapy (RNT) is a generic term for treatment modalities that use a radionuclide labeled to a target-specific molecule. This so-called radiopharmaceutical accumulates in the target, where the ionizing radiation damages the cells. At sufficient levels of radiation, the cells cannot repair themselves. The quantity of the energy deposited in a target region is referred to as the absorbed dose [Gy]. Absorbed dose calculations in RNTs are associated with large uncertainties, originating from determination of the activity as well as uncertainties in absorbed dose conversion factors (S factors). S factors are derived for mathematical described source-target combinations (so called phantoms) using Monte Carlo techniques to simulate the particle transport from various radionuclides. The accuracy of the S factor depends on how well the phantom reflects the patient anatomy. The phantoms most used in conventional dosimetry models rely on crude anatomic descriptions; therefore, calculated absorbed doses and radiation-induced biological effects are rarely well correlated. The aim of this thesis was to develop more realistic phantoms to create more accurate dosimetry models. Most preclinical evaluations of new radiopharmaceuticals or treatment strategies are performed on small animals, and the efficacy should be evaluated with the absorbed dose. In practice, dosimetry calculations are not a standard procedure; instead, activity levels below those reported to produce severe side effects are used. Papers I, II, and III present dosimetry models based on Monte Carlo simulations using realistic phantoms of mice and rats that produce reliable S factors, which could be useful in dosimetry studies. In Paper III, we used our rat dosimetry model with data from an activity-escalating study of 90Y- and 177Lu-BR96 monoclonal antibodies. Two novel parameters that can be used to quantify decreases in peripheral blood cells were derived. We showed that the data derived with these parameters correlated well with the absorbed dose in red bone marrow. In Papers IV and V, we propose two small-scale anatomic models for the small intestine and the testis, respectively. The large difference from conventional models is that different tissue structures are incorporated, allowing for the calculation of absorbed doses to the most radiosensitive cells in the tissue while considering heterogeneous uptake therein. Differences in order of magnitude are possible when calculating absorbed doses using these new dosimetry models. These dosimetry models will be important when making correlations with biological effects

Fast Monte Carlo Simulation for Patient-specific CT/CBCT Imaging Dose Calculation

Recently, X-ray imaging dose from computed tomography (CT) or cone beam CT (CBCT) scans has become a serious concern. Patient-specific imaging dose calculation has been proposed for the purpose of dose management. While Monte Carlo (MC) dose calculation can be quite accurate for this purpose, it suffers from low computational efficiency. In response to this problem, we have successfully developed a MC dose calculation package, gCTD, on GPU architecture under the NVIDIA CUDA platform for fast and accurate estimation of the x-ray imaging dose received by a patient during a CT or CBCT scan. Techniques have been developed particularly for the GPU architecture to achieve high computational efficiency. Dose calculations using CBCT scanning geometry in a homogeneous water phantom and a heterogeneous Zubal head phantom have shown good agreement between gCTD and EGSnrc, indicating the accuracy of our code. In terms of improved efficiency, it is found that gCTD attains a speed-up of ~400 times in the homogeneous water phantom and ~76.6 times in the Zubal phantom compared to EGSnrc. As for absolute computation time, imaging dose calculation for the Zubal phantom can be accomplished in ~17 sec with the average relative standard deviation of 0.4%. Though our gCTD code has been developed and tested in the context of CBCT scans, with simple modification of geometry it can be used for assessing imaging dose in CT scans as well.Comment: 18 pages, 7 figures, and 1 tabl

A GPU Tool for Efficient, Accurate, and Realistic Simulation of Cone Beam CT Projections

Simulation of x-ray projection images plays an important role in cone beam CT (CBCT) related research projects. A projection image contains primary signal, scatter signal, and noise. It is computationally demanding to perform accurate and realistic computations for all of these components. In this work, we develop a package on GPU, called gDRR, for the accurate and efficient computations of x-ray projection images in CBCT under clinically realistic conditions. The primary signal is computed by a tri-linear ray-tracing algorithm. A Monte Carlo (MC) simulation is then performed, yielding the primary signal and the scatter signal, both with noise. A denoising process is applied to obtain a smooth scatter signal. The noise component is then obtained by combining the difference between the MC primary and the ray-tracing primary signals, and the difference between the MC simulated scatter and the denoised scatter signals. Finally, a calibration step converts the calculated noise signal into a realistic one by scaling its amplitude. For a typical CBCT projection with a poly-energetic spectrum, the calculation time for the primary signal is 1.2~2.3 sec, while the MC simulations take 28.1~95.3 sec. Computation time for all other steps is negligible. The ray-tracing primary signal matches well with the primary part of the MC simulation result. The MC simulated scatter signal using gDRR is in agreement with EGSnrc results with a relative difference of 3.8%. A noise calibration process is conducted to calibrate gDRR against a real CBCT scanner. The calculated projections are accurate and realistic, such that beam-hardening artifacts and scatter artifacts can be reproduced using the simulated projections. The noise amplitudes in the CBCT images reconstructed from the simulated projections also agree with those in the measured images at corresponding mAs levels.Comment: 21 pages, 11 figures, 1 tabl

TOPAS-MC Extension for Nuclear Medicine Applications

Monte Carlo (MC) techniques are currently deemed the gold standard for internal dosimetry, since the simulations can perform full radiation transport and reach a precision level not attainable by analytical methods. In this study, a custom voxelized particle source was developed for the TOPAS-MC toolkit to be used for internal dosimetry purposes. The source was designed to allow the use of clinical functional scans data to simulate events that reproduce the patient-specific tracer biodistribution. Simulation results are very promising, showing that this can be a first step towards the extension of TOPAS-MC to nuclear medicine applications. In the future more studies are needed to further ascertain the applicability and accuracy of the developed routines.Comment: 5 pages, 3 figure

Investigation of the Effects of Image Signal-to-Noise Ratio on TSPO PET Quantification of Neuroinflammation

Neuroinflammation may be imaged using positron emission tomography (PET) and the tracer [11C]-PK11195. Accurate and precise quantification of 18 kilodalton Translocator Protein (TSPO) binding parameters in the brain has proven difficult with this tracer, due to an unfavourable combination of low target concentration in tissue, low brain uptake of the tracer and relatively high non-specific binding, all of which leads to higher levels of relative image noise. To address these limitations, research into new radioligands for the TSPO, with higher brain uptake and lower non-specific binding relative to [11C]-PK11195, is being conducted world-wide. However, factors other than radioligand properties are known to influence signal-to-noise ratio in quantitative PET studies, including the scanner sensitivity, image reconstruction algorithms and data analysis methodology. The aim of this thesis was to investigate and validate computational tools for predicting image noise in dynamic TSPO PET studies, and to employ those tools to investigate the factors that affect image SNR and reliability of TSPO quantification in the human brain. The feasibility of performing multiple (n≥40) independent Monte Carlo simulations for each dynamic [11C]-PK11195 frame- with realistic modelling of the radioactivity source, attenuation and PET tomograph geometries- was investigated. A Beowulf-type high performance computer cluster, constructed from commodity components, was found to be well suited to this task. Timing tests on a single desktop computer system indicated that a computer cluster capable of simulating an hour-long dynamic [11C]-PK11195 PET scan, with 40 independent repeats, and with a total simulation time of less than 6 weeks, could be constructed for less than 10,000 Australian dollars. A computer cluster containing 44 computing cores was therefore assembled, and a peak simulation rate of 2.84x105 photon pairs per second was achieved using the GEANT4 Application for Tomographic Emission (GATE) Monte Carlo simulation software. A simulated PET tomograph was developed in GATE that closely modelled the performance characteristics of several real-world clinical PET systems in terms of spatial resolution, sensitivity, scatter fraction and counting rate performance. The simulated PET system was validated using adaptations of the National Electrical Manufacturers Association (NEMA) quality assurance procedures within GATE. Image noise in dynamic TSPO PET scans was estimated by performing n=40 independent Monte Carlo simulations of an hour-long [11C]-PK11195 scan, and of an hour- long dynamic scan for a hypothetical TSPO ligand with double the brain activity concentration of [11C]-PK11195. From these data an analytical noise model was developed that allowed image noise to be predicted for any combination of brain tissue activity concentration and scan duration. The noise model was validated for the purpose of determining the precision of kinetic parameter estimates for TSPO PET. An investigation was made into the effects of activity concentration in tissue, radionuclide half-life, injected dose and compartmental model complexity on the reproducibility of kinetic parameters. Injecting 555 MBq of carbon-11 labelled TSPO tracer produced similar binding parameter precision to 185 MBq of fluorine-18, and a moderate (20%) reduction in precision was observed for the reduced carbon-11 dose of 370 MBq. Results indicated that a factor of 2 increase in frame count level (relative to [11C]-PK11195, and due for example to higher ligand uptake, injected dose or absolute scanner sensitivity) is required to obtain reliable binding parameter estimates for small regions of interest when fitting a two-tissue compartment, four-parameter compartmental model. However, compartmental model complexity had a similarly large effect, with the reduction of model complexity from the two-tissue compartment, four-parameter to a one-tissue compartment, two-parameter model producing a 78% reduction in coefficient of variation of the binding parameter estimates at each tissue activity level and region size studied. In summary, this thesis describes the development and validation of Monte Carlo methods for estimating image noise in dynamic TSPO PET scans, and analytical methods for predicting relative image noise for a wide range of tissue activity concentration and acquisition durations. The findings of this research suggest that a broader consideration of the kinetic properties of novel TSPO radioligands, with a view to selection of ligands that are potentially amenable to analysis with a simple one-tissue compartment model, is at least as important as efforts directed towards reducing image noise, such as higher brain uptake, in the search for the next generation of TSPO PET tracers