Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE

PET digitization chain for Monte Carlo simulation in GATE

Objective. We introduce a versatile methodology for the accurate modelling of PET imaging systems via Monte Carlo simulations, using the Geant4 application for tomographic emission (GATE) platform. Accurate Monte Carlo modelling involves the incorporation of a complete analytical signal processing chain, called the digitizer in GATE, to emulate the different count rates encountered in actual positron emission tomography (PET) systems.Approach. The proposed approach consists of two steps: (1) modelling the digitizer to replicate the detection chain of real systems, covering all available parameters, whether publicly accessible or supplied by manufacturers; (2) estimating the remaining parameters, i.e. background noise level, detection efficiency, and pile-up, using optimisation techniques based on experimental single and prompt event rates. We show that this two-step optimisation reproduces the other experimental count rates (true, scatter, and random), without the need for additional adjustments. This method has been applied and validated with experimental data derived from the NEMA count losses test for three state-of-the-art SiPM-based time-of-flight (TOF)-PET systems: Philips Vereos, Siemens Biograph Vision 600 and GE Discovery MI 4-ring.Main results. The results show good agreement between experiments and simulations for the three PET systems, with absolute relative discrepancies below 3%, 6%, 6%, 7% and 12% for prompt, random, true, scatter and noise equivalent count rates, respectively, within the 0-10 kBq·ml-1activity concentration range typically observed in whole-body18F scans.Significance. Overall, the proposed digitizer optimisation method was shown to be effective in reproducing count rates and NECR for three of the latest generation SiPM-based TOF-PET imaging systems. The proposed methodology could be applied to other PET scanners.

Measurement of the12C(p,n)12N reaction cross section below 150 MeV

Objective. Proton therapy currently faces challenges from clinical complications on organs-at-risk due to range uncertainties. To address this issue, positron emission tomography (PET) of the proton-induced11C and15O activity has been used to provide feedback on the proton range. However, this approach is not instantaneous due to the relatively long half-lives of these nuclides. An alternative nuclide,12N (half-life 11 ms), shows promise for real-timein vivoproton range verification. Development of12N imaging requires better knowledge of its production reaction cross section.Approach. The12C(p,n)12N reaction cross section was measured by detecting positron activity of graphite targets irradiated with 66.5, 120, and 150 MeV protons. A pulsed beam delivery with 0.7-2 × 108protons per pulse was used. The positron activity was measured during the beam-off periods using a dual-head Siemens Biograph mCT PET scanner. The12N production was determined from activity time histograms.Main results. The cross section was calculated for 11 energies, ranging from 23.5 to 147 MeV, using information on the experimental setup and beam delivery. Through a comprehensive uncertainty propagation analysis, a statistical uncertainty of 2.6%-5.8% and a systematic uncertainty of 3.3%-4.6% were achieved. Additionally, a comparison between measured and simulated scanner sensitivity showed a scaling factor of 1.25 (±3%). Despite this, there was an improvement in the precision of the cross section measurement compared to values reported by the only previous study.Significance. Short-lived12N imaging is promising for real-timein vivoverification of the proton range to reduce clinical complications in proton therapy. The verification procedure requires experimental knowledge of the12N production cross section for proton energies of clinical importance, to be incorporated in a Monte Carlo framework for12N imaging prediction. This study is the first to achieve a precise measurement of the12C(p,n)12N nuclear cross section for such proton energies.

Application of NEMA protocols to verify GATE models based on the Digital Biograph Vision and the Biograph Vision Quadra scanners

PURPOSE

The Monte Carlo method is an effective tool to simulate and verify PET systems. Furthermore, it can help in the design and optimization of new medical imaging devices and algorithms. In this framework, the goal of this work is to verify the GATE toolkit performance when applied to simulate two Siemens Healthineers PET scanners: a standard axial field-of-view Biograph Vision scanner and the new long axial field-of-view Biograph Vision Quadra scanner.

METHODS

The simulation toolkit GATE is based on GEANT4, comprising its main functionalities and a set of domain-specific features in the field of medical physics. To accomplish our purpose, the guidelines described in the NEMA NU 2-2018 protocol are reproduced. Then the simulated results are compared to experimental data available in the literature for both PET scanners. The assessment of the models includes different studies of sensitivity, count rate performances, spatial resolution and image quality. These tests are intended to evaluate the image quality of PET devices.

RESULTS

In the spatial resolution test, relative errors lower than 8% are obtained between the experiments and GATE models. The sensitivity is 17.2 cps/kBq (Vision) and 175.9 cps/kBq (Quadra), representing relative differences with the experiment of 6% and 0.3%, respectively. Deviations from peak NECR are less than 9%. In the Image Quality test, the contrast recovery coefficient for hot spheres, with 8 iterations and 5 subsets, ranges between 57-83% for Vision and 54-86% for Quadra. These values represent a maximum deviation between the simulations and the experiments of 10% for the Quadra scanner. In the case of the Vision scanner, the highest difference is observed for the 10 mm sphere (∼38%) due to the higher contrast recovery of the experiment caused by the Gibbs artifact from the use of PSF reconstruction.

CONCLUSIONS

The results of the simulations have provided evidence of a good agreement between the experimental data and the results obtained with GATE. Thus, this work supports the capability of this MC toolkit to accurately simulate the models of the Vision and Quadra scanners. This study has laid the basis for further research in this field and has identified several areas that could be explored.

Validation of a discovery MI 4-ring model according to the NEMA NU 2-2018 standards: from Monte Carlo simulations to clinical-like reconstructions

BACKGROUND

We propose a comprehensive evaluation of a Discovery MI 4-ring (DMI) model, using a Monte Carlo simulator (GATE) and a clinical reconstruction software package (PET toolbox). The following performance characteristics were compared with actual measurements according to NEMA NU 2-2018 guidelines: system sensitivity, count losses and scatter fraction (SF), coincidence time resolution (CTR), spatial resolution (SR), and image quality (IQ). For SR and IQ tests, reconstruction of time-of-flight (TOF) simulated data was performed using the manufacturer's reconstruction software.

RESULTS

Simulated prompt, random, true, scatter and noise equivalent count rates closely matched the experimental rates with maximum relative differences of 1.6%, 5.3%, 7.8%, 6.6%, and 16.5%, respectively, in a clinical range of less than 10 kBq/mL. A 3.6% maximum relative difference was found between experimental and simulated sensitivities. The simulated spatial resolution was better than the experimental one. Simulated image quality metrics were relatively close to the experimental results.

CONCLUSIONS

The current model is able to reproduce the behaviour of the DMI count rates in the clinical range and generate clinical-like images with a reasonable match in terms of contrast and noise.

Validation of a computational chain from PET Monte Carlo simulations to reconstructed images

The study aimed to create a pipeline from Monte Carlo simulated projections of a Gate PET system to reconstructed images. The PET system was modelled after the GE Discovery MI (DMI) PET/CT, and the simulated projections were reconstructed with the stand-alone reconstruction software CASToR. Attenuation correction, normalisation calibration, random estimation, and scatter estimation for the simulations were computed with in-house programs. The pipeline was compared in both projection and image space with data acquired on a clinical DMI and reconstructed with GE's off-line PET reconstruction software (PET Toolbox) and CASToR. The simulated and measured data were compared for the number of prompt coincidences, scatter fraction, contrast recovery coefficient (CRC), signal-to-noise ratio (SNR), background variability, residual lung error, and image profiles. A slight discrepancy was noted in the projection space, but good agreements were generally achieved in image space between simulated and measured data. The CRC was found to be 81 % for Gate – CASToR, 84 % for GE – CASToR, and 84 % for GE - PET Toolbox for the largest sphere of the NEMA image quality (IQ) phantom, and the SNR was found to be 98 for Gate – CASToR, 91 for GE – CASToR, and 93 for GE – PET Toolbox. Profiles drawn over the spheres for the NEMA IQ phantom and the Data Spectrum (DS) phantom show a good match between measurement and simulation. The results indicate feasibility to utilise the pipeline as a tool for off-line simulation-based studies. A complete pipeline introduces possibilities to study the impact of single parameters in the whole chain from simulation to reconstructed images.

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.

Monte Carlo simulation and performance assessment of GE Discovery 690 VCT positron emission tomography/computed tomography scanner

The aim of this study is to simulate GE Discovery 690 VCT positron emission tomography/computed tomography (PET/CT) scanner using Geant4 Application for Tomographic Emission (GATE) simulation package (version 8). Then, we assess the performance of scanner by comparing measured and simulated parameter results. Detection system and geometry of PET scanner that consists of 13,824 LYSO crystals designed in 256 blocks and 24 ring detectors were modeled. In order to achieve a precise model, we verified scanner model. Validation was based on a comparison between simulation data and experimental results obtained with this scanner in the same situation. Parameters used for validation were sensitivity, spatial resolution, and contrast. Image quality assessment was done based on comparing the contrast recovery coefficient (CRC) of simulated and measured images. The findings demonstrate that the mean difference between simulated and measured sensitivity is <7%. The simulated spatial resolution agreed to within <5.5% of the measured values. Contrast results had a slight divergence within the range below 4%. The image quality validation study demonstrated an acceptable agreement in CRC for 8:1 and 2:1 source-to-background activity ratio. Validated performance parameters showed good agreement between experimental data and simulated results and demonstrated that GATE is a valid simulation tool for simulating this scanner model. The simulated model of this scanner can be used for future studies regarding optimization of image reconstruction algorithms and emission acquisition protocols.

Monte Carlo simulation of digital photon counting PET

A GATE Monte Carlo model of the Philips Vereos digital photon counting PET imaging system using silicon photo-multiplier detectors was proposed. It was evaluated against experimental data in accordance with NEMA guidelines. Comparisons were performed using listmode data in order to remain independent of image reconstruction algorithms. An original line of response-based method is proposed to estimate intrinsic spatial resolution without reconstruction. Four sets of experiments were performed: (1) count rates and scatter fraction, (2) energy and timing resolutions, (3) sensitivity, and (4) intrinsic spatial resolution. Experimental and simulated data were found to be in good agreement, with overall differences lower than 10% for activity concentrations used in most standard clinical applications. Illustrative image reconstructions were provided. In conclusion, the proposed Monte Carlo model was validated and can be used for numerous studies such as optimizing acquisition parameters or reconstruction algorithms.

A Prototype VP-PET Imaging System Based on Highly Pixelated CdZnTe Detectors

We investigated a prototype virtual-pinhole positron emission tomography (PET) system for small-animal imaging applications. The PET detector modules were made up of 1.3 mm lutetium-yttrium oxyorthosilicate (LYSO) arrays, and the insert detectors consisted of 0.6 mm pixelated cadmium zinc telluride (CdZnTe). To validate the imaging experiment, we did a Monte Carlo simulation for the virtual-pinhole PET (VP-PET) system in the Geant4 Application for Emission Tomography (GATE). For a point source of ²²Na with a 0.5 mm diameter, the filtered back-projection algorithm-reconstructed PET image showed a resolution of 0.7 mm full-width-at-half-maximum. The system sensitivity was 0.46 cps/kBq at the center of the field view of the PET system with a source activity of 0.925 MBq and an energy window of 350 to 650 keV. A rod source phantom and a Derenzo phantom with ¹⁸F were also simulated to investigate the PET imaging ability. GATE simulation indicated that sources with 0.5 mm diameter could be clearly detected using 0.6 mm pixelated CdZnTe detectors as insert devices in a VP-PET system.

Count rate performance of brain-dedicated PET scanners: a Monte Carlo simulation study

In recent years, there has been a renewed interest in brain-dedicated PET imaging systems, particularly in the context of combined PET/MR imaging. We are currently designing a brain-dedicated PET insert suitable for an ultra-high field brain-dedicated MR scanner, the Siemens Magnetom 7T MR scanner. In this paper, an investigation on the count rate performance of several possible detectors through a series of Monte Carlo simulations is reported. Brain-dedicated PET scanners with a lutetium oxyorthosilicate scintillator and a detector area of 0.04 (1 crystal per detector) to 101.37 (2500 crystals per detector) cm², detector thickness of 10 to 20 mm and a fixed crystal pitch of ~2 mm were simulated. The count rate performance of each scanner was evaluated as a function of detector deadtime type and constant, coincidence timing window and lower level discriminator. Also, the effects of activity outside the field-of-view (FOV) on the count rate performance of each scanner were studied. For each detector geometry and performance metric, the scanner singles rate, scanner sensitivity and noise equivalent count rate as a function of activity in the FOV were measured. It was seen that scanners with detectors comprised a few crystal elements showed reduced scanner sensitivity due to a high number of inter-detector scattering. The count rate performance of scanners with large detectors, on the other hand, was mainly determined by the deadtime properties of the detectors. A model for the count rate performance of the scanner with each studied detector is presented in this work.

Bragg peak characteristics of proton beams within therapeutic energy range and the comparison of stopping power using the GATE Monte Carlo simulation and the NIST data

Purpose:

To examine detail depth dose characteristics of ideal proton beams using the GATE Monte Carlo technique.

Methods:

In this study, in order to improve simulation efficiency, we used pencil beam geometry instead of parallel broad-field geometry. Depth dose distributions for beam energies from 5 to 250 MeV in a water phantom were obtained. This study used parameters namedRpeak,R90,R80,R73,R50, full width at half maximum (FWHM), width of 80–20% distal fall-off (W(80–20)) and peak-to-entrance ratio to represent Bragg peak characteristics. The obtained energy–range relationships were fitted into third-order polynomial formulae. The present study also used the GATE Monte Carlo code to calculate the stopping power of proton pencil beams in a water cubic phantom and compared results with the National Institute of Standards and Technology (NIST) standard reference database.

Results:

The study results revealed deeper penetration, broader FWHM and distal fall-off and decreased peak-to-entrance dose ratio with increasing beam energy. Study results for monoenergetic proton beams showed thatR73can be a good indicator to characterise a range of incident beams. These also suggest FWHM is more sensitive thanW(80–20)distal fall-off in finding initial energy spread. Furthermore, the difference between the obtained stopping power from simulation and NIST data almost in all energies was lower than 1%.

Conclusion:

Detail depth dose characteristics for monoenergetic proton beams within therapeutic energy ranges were reported. These results can serve as a good reference for clinical practitioners in their daily practice.

PET Counting Response Variability Depending on Tumor Location, Activity, and Patient Obesity: A Feasibility Study of Solitary Pulmonary Nodule Using Monte Carlo

We aim to investigate the counting response variations of positron emission tomography (PET) scanners with different detector configurations in the presence of solitary pulmonary nodule (SPN). Using experimentally validated Monte Carlo simulations, the counting performance of four different scanner models with varying tumor activity, location, and patient obesity is represented using a noise equivalent count rate (NECR). NECR is a well-established quantitative metric which has positive correlation with clinically perceived image quality. The combined effect of tumor displacement and increased activity shows a linear ascending trend for NECR with slope ranges of (12.5-18.2)*10^-3 (kBq/cm³)^-1 for three-ring (3R) scanners and (15.3-21.5)*10^-3 (kBq/cm³)^-1 for four-ring (4R). The trend for the combined effect of tumor displacement and patient obesity is exponential decay with 3R configurations weakly dependent on the patient obesity if the tumor is located at the center of the field of view with exponent's range of (6.6-33.8)*10^-2cm^-1. The dependence is stronger for 4R scanners (9.6-38.5)*10^-2cm^-1. The analysis indicates that quantitative PET data from the same SPN patient possibly examined in different time points (e.g., during staging or for the evaluation of treatment response) are affected by the different detector configurations and need to be normalized with patient weight, activity, and tumor location to reduce unwanted bias of the diagnosis. This paper provides also with a proof of concept for the ability of properly tuned simulations to provide additional insights into the counting response variability especially in tumor types where often borderline decisions have to be made regarding their characterization.

A GATE Monte Carlo model for a newly developed small animal PET scanner: the IRI-microPET

Monte Carlo simulation is widely used in emission tomography, in order to assess image reconstruction algorithms and correction techniques, for system optimization, and study the parameters affecting the system performance. In the current study, the performance of the IRI-microPET system was simulated using the GATE Monte Carlo code and a number of performance parameters, including spatial resolution, scatter fraction, sensitivity, RMS contrast, and signal-to-noise ratio, evaluated and compared to the corresponding measured values. The results showed an excellent agreement between simulated and measured data: The experimental and simulated spatial resolutions (radial) for 18F in the center of the AFOV were 1.81 mm and 1.65 mm, respectively. The difference between the experimental and simulated sensitivities of the system was <7%. Simulated and experimental scatter fractions differed less than 9% for the mouse phantom in different timing windows. The validation study of the image quality indicated a good agreement in RMS contrast and signal-to-noise ratio. Also, system performance was compared with the two available commercial scanners which were simulated using GATE code. In conclusion, the assessment of the Monte Carlo modeling of the IRI-microPET system reveals that the GATE code is a flexible and accurate tool for describing the response of an animal PET system.

SMART (SiMulAtion and ReconsTruction) PET: an efficient PET simulation-reconstruction tool

Background

Positron-emission tomography (PET) simulators are frequently used for development and performance evaluation of segmentation methods or quantitative uptake metrics. To date, most PET simulation tools are based on Monte Carlo simulations, which are computationally demanding. Other analytical simulation tools lack the implementation of time of flight (TOF) or resolution modelling (RM). In this study, a fast and easy-to-use PET simulation-reconstruction package, SiMulAtion and ReconsTruction (SMART)-PET, is developed and validated, which includes both TOF and RM. SMART-PET, its documentation and instructions to calibrate the tool to a specific PET/CT system are available on Zenodo.

SMART-PET allows the fast generation of 3D PET images. As input, it requires one image representing the activity distribution and one representing the corresponding CT image/attenuation map. It allows the user to adjust different parameters, such as reconstruction settings (TOF/RM), noise level or scan duration. Furthermore, a random spatial shift can be included, representing patient repositioning. To evaluate the tool, simulated images were compared with real scan data of the NEMA NU 2 image quality phantom. The scan was acquired as a 60-min list-mode scan and reconstructed with and without TOF and/or RM. For every reconstruction setting, ten statistically equivalent images, representing 30, 60, 120 and 300 s scan duration, were generated. Simulated and real-scan data were compared regarding coefficient of variation in the phantom background and activity recovery coefficients (RCs) of the spheres. Furthermore, standard deviation images of each of the ten statistically equivalent images were compared.

Results

SMART-PET produces images comparable to actual phantom data. The image characteristics of simulated and real PET images varied in similar ways as function of reconstruction protocols and noise levels. The change in image noise with variation of simulated TOF settings followed the theoretically expected behaviour. RC as function of sphere size agreed within 0.3–11% between simulated and actual phantom data.

Conclusions

SMART-PET allows for rapid and easy simulation of PET data. The user can change various acquisition and reconstruction settings (including RM and TOF) and noise levels. The images obtained show similar image characteristics as those seen in actual phantom data.

Electronic supplementary material

The online version of this article (10.1186/s40658-018-0215-x) contains supplementary material, which is available to authorized users.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PURPOSE

The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.

APPROACH

A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.

FINDINGS

A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.

CONCLUSIONS

Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

Influences of 3D PET scanner components on increased scatter evaluated by a Monte Carlo simulation

Monte Carlo simulation is widely applied to evaluate the performance of three-dimensional positron emission tomography (3D-PET). For accurate scatter simulations, all components that generate scatter need to be taken into account. The aim of this work was to identify the components that influence scatter. The simulated geometries of a PET scanner were: a precisely reproduced configuration including all of the components; a configuration with the bed, the tunnel and shields; a configuration with the bed and shields; and the simplest geometry with only the bed. We measured and simulated the scatter fraction using two different set-ups: (1) as prescribed by NEMA-NU 2007 and (2) a similar set-up but with a shorter line source, so that all activity was contained only inside the field-of-view (FOV), in order to reduce influences of components outside the FOV. The scatter fractions for the two experimental set-ups were, respectively, 45% and 38%. Regarding the geometrical configurations, the former two configurations gave simulation results in good agreement with the experimental results, but simulation results of the simplest geometry were significantly different at the edge of the FOV. From the simulation of the precise configuration, the object (scatter phantom) was the source of more than 90% of the scatter. This was also confirmed by visualization of photon trajectories. Then, the bed and the tunnel were mainly the sources of the rest of the scatter. From the simulation results, we concluded that the precise construction was not needed; the shields, the tunnel, the bed and the object were sufficient for accurate scatter simulations.

Preclinical positron emission tomography scanner based on a monolithic annulus of scintillator: initial design study

Positron emission tomography (PET) scanners designed for imaging of small animals have transformed translational research by reducing the necessity to invasively monitor physiology and disease progression. Virtually all of these scanners are based on the use of pixelated detector modules arranged in rings. This design, while generally successful, has some limitations. Specifically, use of discrete detector modules to construct PET scanners reduces detection sensitivity and can introduce artifacts in reconstructed images, requiring the use of correction methods. To address these challenges, and facilitate measurement of photon depth-of-interaction in the detector, we investigated a small animal PET scanner (called AnnPET) based on a monolithic annulus of scintillator. The scanner was created by placing 12 flat facets around the outer surface of the scintillator to accommodate placement of silicon photomultiplier arrays. Its performance characteristics were explored using Monte Carlo simulations and sections of the NEMA NU4-2008 protocol. Results from this study revealed that AnnPET's reconstructed spatial resolution is predicted to be [Formula: see text] full width at half maximum in the radial, tangential, and axial directions. Peak detection sensitivity is predicted to be 10.1%. Images of simulated phantoms (mini-hot rod and mouse whole body) yielded promising results, indicating the potential of this system for enhancing PET imaging of small animals.

Prospective PET image quality gain calculation method by optimizing detector parameters

BACKGROUND

Lutetium-based scintillators with high-performance electronics introduced time-of-flight (TOF) reconstruction in the clinical setting. Let G' be the total signal to noise ratio gain in a reconstructed image using the TOF kernel compared with conventional reconstruction modes. G' is then the product of G1 gain arising from the reconstruction process itself and (n-1) other gain factors (G2, G3, … Gn) arising from the inherent properties of the detector.

METHODS

We calculated G2 and G3 gains resulting from the optimization of the coincidence and energy window width for prompts and singles, respectively. Both quantitative and image-based validated Monte Carlo models of Lu2SiO5 (LSO) TOF-permitting and Bi4Ge3O12 (BGO) TOF-nonpermitting detectors were used for the calculations.

RESULTS

G2 and G3 values were 1.05 and 1.08 for the BGO detector and G3 was 1.07 for the LSO. A value of almost unity for G2 of the LSO detector indicated a nonsignificant optimization by altering the energy window setting. G' was found to be ∼1.4 times higher for the TOF-permitting detector after reconstruction and optimization of the coincidence and energy windows.

CONCLUSION

The method described could potentially predict image noise variations by altering detector acquisition parameters. It could also further contribute toward a long-lasting debate related to cost-efficiency issues of TOF scanners versus the non-TOF ones. Some vendors re-engage nowadays to non-TOF product line designs in an effort to reduce crystal costs. Therefore, exploring the limits of image quality gain by altering the parameters of these detectors remains a topical issue.

Local respiratory motion correction for PET/CT imaging: Application to lung cancer

PURPOSE

Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications.

METHODS

Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions.

RESULTS

Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV.

CONCLUSIONS

The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.

SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

PURPOSE

Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity.

METHODS

The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor-Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB).

RESULTS

SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004).

CONCLUSIONS

SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.

Concept and simulation study of a novel localization method for robotic endoscopic capsules using multiple positron emission markers

PURPOSE

Over the last decade, wireless capsule endoscope has been the tool of choice for noninvasive inspection of the gastrointestinal tract, especially in the small intestine. However, the latest clinical products have not been equipped with a sufficiently accurate localization system which makes it difficult to determine the location of intestinal abnormalities, and to apply follow-up interventions such as biopsy or drug delivery. In this paper, the authors present a novel localization method based on tracking three positron emission markers embedded inside an endoscopic capsule.

METHODS

Three spherical(22)Na markers with diameters of less than 1 mm are embedded in the cover of the capsule. Gamma ray detectors are arranged around a patient body to detect coincidence gamma rays emitted from the three markers. The position of each marker can then be estimated using the collected data by the authors' tracking algorithm which consists of four consecutive steps: a method to remove corrupted data, an initialization method, a clustering method based on the Fuzzy C-means clustering algorithm, and a failure prediction method.

RESULTS

The tracking algorithm has been implemented inMATLAB utilizing simulation data generated from the Geant4 Application for Emission Tomography toolkit. The results show that this localization method can achieve real-time tracking with an average position error of less than 0.4 mm and an average orientation error of less than 2°.

CONCLUSIONS

The authors conclude that this study has proven the feasibility and potential of the proposed technique in effectively determining the position and orientation of a robotic endoscopic capsule.

Validation of the SimSET simulation package for modeling the Siemens Biograph mCT PET scanner

Monte Carlo simulation provides a valuable tool in performance assessment and optimization of system design parameters for PET scanners. SimSET is a popular Monte Carlo simulation toolkit that features fast simulation time, as well as variance reduction tools to further enhance computational efficiency. However, SimSET has lacked the ability to simulate block detectors until its most recent release. Our goal is to validate new features of SimSET by developing a simulation model of the Siemens Biograph mCT PET scanner and comparing the results to a simulation model developed in the GATE simulation suite and to experimental results. We used the NEMA NU-2 2007 scatter fraction, count rates, and spatial resolution protocols to validate the SimSET simulation model and its new features. The SimSET model overestimated the experimental results of the count rate tests by 11-23% and the spatial resolution test by 13-28%, which is comparable to previous validation studies of other PET scanners in the literature. The difference between the SimSET and GATE simulation was approximately 4-8% for the count rate test and approximately 3-11% for the spatial resolution test. In terms of computational time, SimSET performed simulations approximately 11 times faster than GATE simulations. The new block detector model in SimSET offers a fast and reasonably accurate simulation toolkit for PET imaging applications.

Monte Carlo simulations versus experimental measurements in a small animal PET system. A comparison in the NEMA NU 4-2008 framework

In this work a comparison between experimental and simulated data using GATE and PeneloPET Monte Carlo simulation packages is presented. All simulated setups, as well as the experimental measurements, followed exactly the guidelines of the NEMA NU 4-2008 standards using the microPET R4 scanner. The comparison was focused on spatial resolution, sensitivity, scatter fraction and counting rates performance. Both GATE and PeneloPET showed reasonable agreement for the spatial resolution when compared to experimental measurements, although they lead to slight underestimations for the points close to the edge. High accuracy was obtained between experiments and simulations of the system's sensitivity and scatter fraction for an energy window of 350-650 keV, as well as for the counting rate simulations. The latter was the most complicated test to perform since each code demands different specifications for the characterization of the system's dead time. Although simulated and experimental results were in excellent agreement for both simulation codes, PeneloPET demanded more information about the behavior of the real data acquisition system. To our knowledge, this constitutes the first validation of these Monte Carlo codes for the full NEMA NU 4-2008 standards for small animal PET imaging systems.

Monte-Carlo simulations of clinically realistic respiratory gated (18)F-FDG PET: application to lesion detectability and volume measurements

In PET/CT thoracic imaging, respiratory motion reduces image quality. A solution consists in performing respiratory gated PET acquisitions. The aim of this study was to generate clinically realistic Monte-Carlo respiratory PET data, obtained using the 4D-NCAT numerical phantom and the GATE simulation tool, to assess the impact of respiratory motion and respiratory-motion compensation in PET on lesion detection and volume measurement. To obtain reconstructed images as close as possible to those obtained in clinical conditions, a particular attention was paid to apply to the simulated data the same correction and reconstruction processes as those applied to real clinical data. The simulations required 140,000h (CPU) generating 1.5 To of data (98 respiratory gated and 49 ungated scans). Calibration phantom and patient reconstructed images from the simulated data were visually and quantitatively very similar to those obtained in clinical studies. The lesion detectability was higher when the better trade-off between lesion movement limitation (compared to ungated acquisitions) and image statistic preservation is considered (respiratory cycle sampling in 3 frames). We then compared the lesion volumes measured on conventional PET acquisitions versus respiratory gated acquisitions, using an automatic segmentation method and a 40%-threshold approach. A time consuming initial manual exclusion of noisy structures needed with the 40%-threshold was not necessary when the automatic method was used. The lesion detectability along with the accuracy of tumor volume estimates was largely improved with the gated compared to ungated PET images.

Towards component-based validation of GATE: aspects of the coincidence processor

GATE is public domain software widely used for Monte Carlo simulation in emission tomography. Validations of GATE have primarily been performed on a whole-system basis, leaving the possibility that errors in one sub-system may be offset by errors in others. We assess the accuracy of the GATE PET coincidence generation sub-system in isolation, focusing on the options most closely modeling the majority of commercially available scanners. Independent coincidence generators were coded by teams at Toshiba Medical Research Unit (TMRU) and UC Davis. A model similar to the Siemens mCT scanner was created in GATE. Annihilation photons interacting with the detectors were recorded. Coincidences were generated using GATE, TMRU and UC Davis code and results compared to "ground truth" obtained from the history of the photon interactions. GATE was tested twice, once with every qualified single event opening a time window and initiating a coincidence check (the "multiple window method"), and once where a time window is opened and a coincidence check initiated only by the first single event to occur after the end of the prior time window (the "single window method"). True, scattered and random coincidences were compared. Noise equivalent count rates were also computed and compared. The TMRU and UC Davis coincidence generators agree well with ground truth. With GATE, reasonable accuracy can be obtained if the single window method option is chosen and random coincidences are estimated without use of the delayed coincidence option. However in this GATE version, other parameter combinations can result in significant errors.

A new PET resolution measurement method through Monte-Carlo simulations

OBJECTIVE

The aim of this study was to propose a novel method for image quality assessment in PET scanners through estimation of the modulation transfer function (MTF) of a plane source. The simulation was implemented using the previously validated Monte-Carlo model. A comparison of the proposed method with the more traditional technique, based on a line source, was also performed.

MATERIALS AND METHODS

The Geant4 application for tomographic emission (GATE) Monte-Carlo package was used for model development, and reconstructed images were obtained using software for tomographic image reconstruction (STIR) with cluster computing. A novel plane source consisting of a radioactive ((18)F-fluorodeoxyglucose) thin-layer chromatography plate was simulated (total source activity: 44.4 MBq) to assess image quality through the MTF. All images were reconstructed with the three-dimensional filtered back projection (FBP3DRP) and ordered-subsets expectation maximization (OSEM) reprojection algorithms.

RESULTS

The MTFs obtained using ordered-subsets expectation maximization show, in all cases, that higher frequencies are preserved compared with those obtained using the FBP3DRP. In addition, the plane source method is less prone to noise than the conventional line source method (SD=0.0031 and 0.0203, respectively).

CONCLUSION

The thin-layer chromatography-based plane source presented requires materials commonly found in a clinical environment and could be used to assess image quality in nuclear medicine departments and to further develop PET and single-photon emission computed tomography scanners through Monte-Carlo simulations.

Exploitation of realistic computational anthropomorphic phantoms for the optimization of nuclear imaging acquisition and processing protocols

Monte Carlo (MC) simulations play a crucial role in nuclear medical imaging since they can provide the ground truth for clinical acquisitions, by integrating and quantifing all physical parameters that affect image quality. The last decade a number of realistic computational anthropomorphic models have been developed to serve imaging, as well as other biomedical engineering applications. The combination of MC techniques with realistic computational phantoms can provide a powerful tool for pre and post processing in imaging, data analysis and dosimetry. This work aims to create a global database for simulated Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) exams and the methodology, as well as the first elements are presented. Simulations are performed using the well validated GATE opensource toolkit, standard anthropomorphic phantoms and activity distribution of various radiopharmaceuticals, derived from literature. The resulting images, projections and sinograms of each study are provided in the database and can be further exploited to evaluate processing and reconstruction algorithms. Patient studies using different characteristics are included in the database and different computational phantoms were tested for the same acquisitions. These include the XCAT, Zubal and the Virtual Family, which some of which are used for the first time in nuclear imaging. The created database will be freely available and our current work is towards its extension by simulating additional clinical pathologies.

Least-squares dual characterization for ROI assessment in emission tomography

Our aim is to describe an original method for estimating the statistical properties of regions of interest (ROIs) in emission tomography. Drawn upon the works of Louis on the approximate inverse, we propose a dual formulation of the ROI estimation problem to derive the ROI activity and variance directly from the measured data without any image reconstruction. The method requires the definition of an ROI characteristic function that can be extracted from a co-registered morphological image. This characteristic function can be smoothed to optimize the resolution-variance tradeoff. An iterative procedure is detailed for the solution of the dual problem in the least-squares sense (least-squares dual (LSD) characterization), and a linear extrapolation scheme is described to compensate for sampling partial volume effect and reduce the estimation bias (LSD-ex). LSD and LSD-ex are compared with classical ROI estimation using pixel summation after image reconstruction and with Huesman's method. For this comparison, we used Monte Carlo simulations (GATE simulation tool) of 2D PET data of a Hoffman brain phantom containing three small uniform high-contrast ROIs and a large non-uniform low-contrast ROI. Our results show that the performances of LSD characterization are at least as good as those of the classical methods in terms of root mean square (RMS) error. For the three small tumor regions, LSD-ex allows a reduction in the estimation bias by up to 14%, resulting in a reduction in the RMS error of up to 8.5%, compared with the optimal classical estimation. For the large non-specific region, LSD using appropriate smoothing could intuitively and efficiently handle the resolution-variance tradeoff.

A detector response function design in pinhole SPECT including geometrical calibration

Clinical single photon emission computed tomography (SPECT) equipped with pinhole collimators have a magnification factor that results in high spatial resolution images for small animal imaging. Using Monte Carlo simulations to model the acquisition process and the propagation of the photons from their point of emission to their detection point then integrating the model into an iterative reconstruction algorithm improves the signal-to-noise ratio, the contrast and the spatial resolution in the reconstructed images. However, pinhole SPECT systems are known to be very sensitive to geometrical misalignments. Geometrical misalignments are defined as the radial or axial shift of the collimator pinhole and/or twist and tilt of the detector heads and are introduced in the system each time the collimation device is changed (pinhole to parallel holes or vice versa). In this work, we present a flexible detector response function table (DRFT) design that takes into account the geometrical misalignments and avoids performing new Monte Carlo simulations for each exam in order to calculate a geometrical study-dependent system matrix. The utilization of the DRFT for the calculation of the system matrix speeds up its computation time by two orders of magnitude making it acceptable for preclinical and clinical applications.

Diagnostic accuracy of lymph node metastasis depends on metabolic activity of the primary lesion in thoracic squamous esophageal cancer

The metabolic activity of the primary tumor is an important variable in (18)F-FDG PET interpretation for presurgical staging, because this activity is likely to affect the possibility of detection of malignant involvement in lymph nodes (LNs). The purpose of this study was to reevaluate the diagnostic accuracy of (18)F-FDG PET/CT for the presurgical staging of esophageal squamous cell carcinoma (SCC) in correlation with the (18)F-FDG avidity of the primary lesions.

METHODS

One hundred fifty-six patients (mean age ± SD, 61.4 ± 8.0 y) underwent (18)F-FDG PET/CT before surgical esophagectomy and LN dissection. LN metastasis was identified using the fusion of PET and CT images with increased (18)F-FDG uptake greater than the background activity of the adjacent structures. The results of the patients' (18)F-FDG PET/CT examinations for LN involvement were compared with the histopathologic results to investigate the diagnostic accuracy of (18)F-FDG PET/CT for tumor staging. In addition, we examined the correlation between the diagnostic accuracy of (18)F-FDG PET/CT for LN involvement and the (18)F-FDG avidity of the primary lesions, to investigate the effect of tumor aggressiveness on the diagnosis of LN metastasis.

RESULTS

The diagnostic accuracy of (18)F-FDG PET/CT for LN metastasis showed a low sensitivity, ranging from 29.3% to 53.3%, whereas the specificity was higher than 89.8% in regional thoracic nodes and in remote areas of the cervical and abdominal regions. The (18)F-FDG uptake of the primary lesions positively correlated with that of the metastatic LNs in the thoracic field (R = 0.52, P < 0.05). As a result, our receiver-operating-characteristic analyses demonstrated an area under the curve value of 0.73, with the optimal cutoff value at a maximum standardized uptake value of 3.3 in patients with mid to high (18)F-FDG avidity in the primary lesions (maximum standardized uptake value ≥ 5).

CONCLUSION

This study showed that the avidity of the primary esophageal SCCs affected the detectability of lymph nodal metastases. If primary lesions of esophageal SCC present with a low (18)F-FDG uptake, PET/CT may have a limited role for initial staging because of low sensitivity to detect lymph node metastases.

Generation of 4-dimensional CT images based on 4-dimensional PET-derived motion fields

Respiratory motion can potentially reduce accuracy in anatomic and functional image fusion from multimodality systems. It can blur the uptake of small lesions and lead to significant activity underestimation. Solutions presented to date include respiration-synchronized anatomic and functional acquisitions. To increase the signal-to-noise ratio of the synchronized PET images, methods using nonrigid transformations during the reconstruction process have been proposed. In most of these methods, 4-dimensional (4D) CT images were used to derive the required deformation matrices. However, variations between acquired 4D PET and corresponding CT image series due to differences in respiratory conditions during PET and CT acquisitions have been reported. In addition, the radiation dose burden resulting from a 4D CT acquisition may not be justifiable for every patient.

METHODS

In this paper, we present a method for the generation of dynamic CT images from the combination of one reference CT image and deformation matrices obtained from the elastic registration of 4D PET images not corrected for attenuation. On the one hand, our approach eliminates the need for the acquisition of dynamic CT. On the other hand, it also ensures a good match between CT and PET images, allowing accurate attenuation correction to be performed for respiration-synchronized PET acquisitions.

RESULTS

The proposed method was first validated on Monte Carlo-simulated datasets, and then on patient datasets (n = 4) by comparing generated 4D CT images with the corresponding acquired original CT images. Different levels of PET image statistical quality were considered in order to investigate the impact of image noise in the derivation of the 4D CT series.

CONCLUSION

Our results suggest that clinically relevant PET acquisition times can be used for the implementation of such an approach, making this an even more attractive solution considering the absence of the extra dose given by a standard 4D CT acquisition. Finally, this approach may be applicable to other multimodality devices such as PET/MR.

Modeling and Simulation of 4D PET-CT and PET-MR Images

The driving force in the research and development of new hybrid PET-CT/MR imaging scanners is the production of images with optimum quality, accuracy, and resolution. However, the acquisition process is limited by several factors. Key issues are the respiratory and cardiac motion artifacts that occur during an imaging session. In this article the necessary tools for modeling and simulation of realistic high-resolution four-dimensional PET-CT and PET-MR imaging data are described. Beyond the need for four-dimensional simulations, accurate modeling of the acquisition process can be included within the reconstruction algorithms assisting in the improvement of image quality and accuracy of estimation of physiologic parameters from four-dimensional hybrid PET imaging.

Comparison of different methods of incorporating respiratory motion for lung cancer tumor volume delineation on PET images: a simulation study

The interest of PET complementary information for the delineation of the target volume in radiotherapy of lung cancer is increasing. However, respiratory motion requires the determination of a functional internal target volume (ITV) on PET images for which several strategies have been proposed. The purpose of this study was the comparison of these strategies for taking into account respiratory motion and deriving the ITV: (1) adding fixed margins to the volume defined on a single binned image, (2) segmenting a motion averaged image and (3) considering the union of volumes delineated on binned frames. For this third strategy, binned frames were either non-corrected for motion, or corrected using two different methods: elastic registration or super resolution. The strategies' performances were assessed on realistic simulated datasets combining the NCAT phantom with a PET Philips GEMINI scanner model in GATE, and containing various configurations of tumor to background contrast, with both regular and irregular respiratory motion (with a range of motion amplitudes). The obtained ITVs' sensitivity (SE) and positive predictive value (PVE) with respect to the known true ITV were significantly higher (from 0.8 to 0.95) than all other techniques when using binned frames corrected for motion, independently of motion regularity, amplitude, or tumor to background contrast. Although the absolute difference was small and not always significant, images corrected using super resolution led to systematically better results than using elastic registration. The worst results were obtained when using the motion averaged image for SE (around 0.5-0.6) and using the margins added to a single frame for PPV (0.6-0.7), respectively. The best strategy to account for breathing motion for tumor ITV delineation in radiotherapy planning is to rely on the use of the union of volumes delineated on super resolution-corrected binned images.