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

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.

An automatic pipeline for PET/MRI attenuation correction validation in the brain

PURPOSE

Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity.

METHODS

Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images.

RESULTS

The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images' 16 FreeSurfer brain ROIs.

CONCLUSION

The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques.

Evaluation of a method based on synthetic data inserted into raw data prior to reconstruction for the assessment of PET scanners

Background

Performance assessment of positron emission tomography (PET) scanners is crucial to guide clinical practice with efficiency. Even though clinical data are the final target, their use to characterize systems response is constrained by the lack of ground truth. Phantom tests overcome this limitation by controlling the object of study, but remain simple and are not representative of patient complexity. The objective of this study is to evaluate the accuracy of a simulation method using synthetic spheres inserted into acquired raw data prior to reconstruction, simulating multiple scenarios in comparison with equivalent physical experiments.

Methods

We defined our experimental framework using the National Electrical Manufacturers Association NU-2 2018 Image Quality standard, but replaced the standard sphere set with more appropriate sizes (4, 5, 6, 8, 10 and 13 mm) better suited to current PET scanner performance. Four experiments, with different spheres-to-background ratios (2:1, 4:1, 6:1 and 8:1), were performed. An additional dataset was acquired with a radioactive background but no activity within the spheres (water only) to establish a baseline. Then, we artificially simulated radioactive spheres to reproduce other experiments using synthetic data inserted into the original sinogram. Images were reconstructed following standard guidelines using ordered subset expectation maximization algorithm along with a Bayesian penalized likelihood algorithm. We first visually compared experimental and simulated images. Afterward, we measured the activity concentration values into the spheres to calculate the mean and maximum recovery coefficients (RC_mean and RC_max) which we used in a quantitative analysis.

Results

No significant visual differences were identified between experimental and simulated series. Mann–Whitney U tests comparing simulated and experimental distributions showed no statistical differences for both RC_mean (P value = 0.611) and RC_max (P value = 0.720). Spearman tests revealed high correlation for RC_mean (ρ = 0.974, P value < 0.001) and RC_max (ρ = 0.974, P value < 0.001) between both datasets. From Bland–Altman plots, we highlighted slight shifts in RC_mean and RC_max of, respectively, 2.1 ± 16.9% and 3.3 ± 22.3%.

Conclusions

We evaluated the efficiency of our hybrid method in faithfully mimicking practical situations producing satisfactory results compared to equivalent experimental data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00496-6.

Towards efficient Monte Carlo N-Particle simulation of a positron emission tomography (PET) via source volume definition

Monte Carlo N-Particle (MCNP) simulation has been extensively proven in nuclear medicine imaging systems, most notably in designing and optimizing new medical imaging tools. It enables more complicated geometries and the simulation of particles passing through and interacting with materials. However, a relatively long simulation time is a drawback of Monte Carlo simulation, mainly when complex geometry exists. The current study presents an alternative variance reduction technique for a modeled positron emission tomography (PET) camera by reducing the height of the source volume definition while maintaining the geometry of the simulated model. The National Electrical Manufacturers Association (NEMA) of the International Electrotechnical Commission (IEC) PET's phantom was used with a 1 cm diameter and 7 cm height of line source placed in the middle. The first geometry was fully filled the line source with 0.50 mCi radioactivity. In contrast, the second geometry decreased the source definition to 2.4 cm in height, covering 1 cm above and below the sub-block detector level. The source volume definition approach led to a 71% reduction in the total photons to be simulated. Results showed that the proposed variance reduction strategy could produce spatial resolution as precise as fully filled geometry and sped up the simulation time by approximately 65%. Hence, this strategy can be utilized for further PET optimizing simulation studies.

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning

Objective. In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake. Approach. In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with ‘ground truth’ radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network. Results. When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities. Significance. Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.

3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans

Acquisition time and injected activity of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET should ideally be reduced. However, this decreases the signal-to-noise ratio (SNR), which impairs the diagnostic value of these PET scans. In addition, ⁸⁹Zr-antibody PET is known to have a low SNR. To improve the diagnostic value of these scans, a Convolutional Neural Network (CNN) denoising method is proposed. The aim of this study was therefore to develop CNNs to increase SNR for low-count ¹⁸F-FDG and ⁸⁹Zr-antibody PET. Super-low-count, low-count and full-count ¹⁸F-FDG PET scans from 60 primary lung cancer patients and full-count ⁸⁹Zr-rituximab PET scans from five patients with non-Hodgkin lymphoma were acquired. CNNs were built to capture the features and to denoise the PET scans. Additionally, Gaussian smoothing (GS) and Bilateral filtering (BF) were evaluated. The performance of the denoising approaches was assessed based on the tumour recovery coefficient (TRC), coefficient of variance (COV; level of noise), and a qualitative assessment by two nuclear medicine physicians. The CNNs had a higher TRC and comparable or lower COV to GS and BF and was also the preferred method of the two observers for both ¹⁸F-FDG and ⁸⁹Zr-rituximab PET. The CNNs improved the SNR of low-count ¹⁸F-FDG and ⁸⁹Zr-rituximab PET, with almost similar or better clinical performance than the full-count PET, respectively. Additionally, the CNNs showed better performance than GS and BF.

Minimizing the need for coil attenuation correction in integrated PET/MRI at 1.5 T using low-density MR-linac receive arrays

The simultaneous use of positron emission tomography (PET) and magnetic resonance imaging (MRI) requires attenuation correction (AC) of photon-attenuating objects, such as MRI receive arrays. However, AC of flexible, on-body arrays is complex and therefore often omitted. This can lead to significant, spatially varying PET signal losses when conventional MRI receive arrays are used. Only few dedicated, photon transparent PET/MRI arrays exist, none of which are compatible with our new, wide-bore 1.5 T PET/MRI system dedicated to radiotherapy planning. In this work, we investigated the use of 1.5 T MR-linac (MRL) receive arrays for PET/MRI, as these were designed to have a low photon attenuation for accurate dose delivery and can be connected to the new 1.5 T PET/MRI scanner. Three arrays were assessed: an 8-channel clinically-used MRL array, a 32-channel prototype MRL array, and a conventional MRI receive array. We experimentally determined, simulated, and compared the impact of these arrays on the PET sensitivity and image reconstructions. Furthermore, MRI performance was compared. Overall coil-induced PET sensitivity losses were reduced from 8.5% (conventional) to 1.7% (clinical MRL) and 0.7% (prototype MRL). Phantom measurements showed local signal errors of up to 32.7% (conventional) versus 3.6% (clinical MRL) and 3.5% (prototype MRL). Simulations with data of eight cancer patients showed average signal losses were reduced from 14.3% (conventional) to 1.2% (clinical MRL) and 1.0% (prototype MRL). MRI data showed that the signal-to-noise ratio of the MRL arrays was slightly lower at depth (110 versus 135). The parallel imaging performance of the conventional and prototype MRL arrays was similar, while the clinical MRL array's performance was lower. In conclusion, MRL arrays reducein-vivoPET signal losses >10×, which decreases, or eliminates, the need for coil AC on a new 1.5 T PET/MRI system. The prototype MRL array allows flexible coil positioning without compromising PET or MRI performance. One limitation of MRL arrays is their limited radiolucent PET window (field of view) in the craniocaudal direction.

Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion

Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About < 3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of - 5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.

Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods:: Framework, Strategies, and Role of the Physician

Artificial intelligence-based methods are showing promise in medical imaging applications. There is substantial interest in clinical translation of these methods, requiring that they be evaluated rigorously. We lay out a framework for objective task-based evaluation of artificial intelligence methods. We provide a list of available tools to conduct this evaluation. We outline the important role of physicians in conducting these evaluation studies. The examples in this article are proposed in the context of PET scans with a focus on evaluating neural network-based methods. However, the framework is also applicable to evaluate other medical imaging modalities and other types of artificial intelligence methods.

SimPET-An open online platform for the Monte Carlo simulation of realistic brain PET data. Validation for 18 F-FDG scans

Purpose

SimPET (www.sim‐pet.org) is a free cloud‐based platform for the generation of realistic brain positron emission tomography (PET) data. In this work, we introduce the key features of the platform. In addition, we validate the platform by performing a comparison between simulated healthy brain FDG‐PET images and real healthy subject data for three commercial scanners (GE Advance NXi, GE Discovery ST, and Siemens Biograph mCT).

Methods

The platform provides a graphical user interface to a set of automatic scripts taking care of the code execution for the phantom generation, simulation (SimSET), and tomographic image reconstruction (STIR). We characterize the performance using activity and attenuation maps derived from PET/CT and MRI data of 25 healthy subjects acquired with a GE Discovery ST. We then use the created maps to generate synthetic data for the GE Discovery ST, the GE Advance NXi, and the Siemens Biograph mCT. The validation was carried out by evaluating Bland‐Altman differences between real and simulated images for each scanner. In addition, SPM voxel‐wise comparison was performed to highlight regional differences. Examples for amyloid PET and for the generation of ground‐truth pathological patients are included.

Results

The platform can be efficiently used for generating realistic simulated FDG‐PET images in a reasonable amount of time. The validation showed small differences between SimPET and acquired FDG‐PET images, with errors below 10% for 98.09% (GE Discovery ST), 95.09% (GE Advance NXi), and 91.35% (Siemens Biograph mCT) of the voxels. Nevertheless, our SPM analysis showed significant regional differences between the simulated images and real healthy patients, and thus, the use of the platform for converting control subject databases between different scanners requires further investigation.

Conclusions

The presented platform can potentially allow scientists in clinical and research settings to perform MC simulation experiments without the need for high‐end hardware or advanced computing knowledge and in a reasonable amount of time.

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.