Partial volume correction of PET image data using geometric transfer matrices based on uniform B-splines

Objective. Most methods for partial volume correction (PVC) of positron emission tomography (PET) data employ anatomical segmentation of images into regions of interest. This approach is not optimal for exploratory functional imaging beyond regional hypotheses. Here, we describe a novel method for unbiased voxel-wise PVC.Approach.B-spline basis functions were combined with geometric transfer matrices to enable a method (bsGTM) that provides PVC or alternatively provides smoothing with minimal regional crosstalk. The efficacy of the proposed method was evaluated using Monte Carlo simulations, human PET data, and murine functional PET data.Main results.In simulations, bsGTM provided recovery of partial volume signal loss comparable to iterative deconvolution, while demonstrating superior resilience to noise. In a real murine PET dataset, bsGTM yielded much higher sensitivity for detecting amphetamine-induced reduction of [11C]raclopride binding potential. In human PET data, bsGTM smoothing enabled increased signal-to-noise ratios with less degradation of binding potentials relative to Gaussian convolution or non-local means.Significance.bsGTM offers improved performance for PVC relative to iterative deconvolution, the current method of choice for voxel-wise PVC, especially in the common PET regime of low signal-to-noise ratio. The new method provides an anatomically unbiased way to compensate partial volume errors in cases where anatomical segmentation is unavailable or of questionable relevance or accuracy.

Image Reconstruction Analysis for Positron Emission Tomography With Heterostructured Scintillators

The concept of structure engineering has been proposed for exploring the next generation of radiation detectors with improved performance. A TOF-PET geometry with heterostructured scintillators with a pixel size of 3.0 × 3.1 × 15 mm3 was simulated using Monte Carlo. The heterostructures consisted of alternating layers of BGO as a dense material with high stopping power and plastic (EJ232) as a fast light emitter. The detector time resolution was calculated as a function of the deposited and shared energy in both materials on an event-by-event basis. While sensitivity was reduced to 32% for 100-μm thick plastic layers and 52% for 50 μm, the coincidence time resolution (CTR) distribution improved to 204 ± 49 and 220 ± 41 ps, respectively, compared to 276 ps that we considered for bulk BGO. The complex distribution of timing resolutions was accounted for in the reconstruction. We divided the events into three groups based on their CTR and modeled them with different Gaussian TOF kernels. On an NEMA IQ phantom, the heterostructures had better contrast recovery in early iterations. On the other hand, BGO achieved a better contrast-to-noise ratio (CNR) after the 15th iteration due to the higher sensitivity. The developed simulation and reconstruction methods constitute new tools for evaluating different detector designs with complex time responses.

Data-driven, energy-based method for estimation of scattered events in positron emission tomography

Objective. Scattered events add bias in the reconstructed positron emission tomography (PET) images. Our objective is the accurate estimation of the scatter distribution, required for an effective scatter correction. Approach. In this paper, we propose a practical energy-based (EB) scatter estimation method that uses the marked difference between the energy distribution of the non-scattered and scattered events in the presence of randoms. In contrast to previous EB methods, we model the unscattered events using data obtained from measured point sources. Main results. We demonstrate feasibility using Monte Carlo simulated as well as experimental data acquired on the long axial field-of-view (FOV) PennPET EXPLORER scanner. Simulations show that the EB scatter estimated sinograms, for all phantoms, are in excellent agreement with the ground truth scatter distribution, known from the simulated data. Using the standard NEMA image quality (IQ) phantom we find that both the EB and single scatter simulation (SSS) provide good contrast recovery values. However, the EB correction gives better lung residuals. Significance. Application of the EB method on measured data showed, that the proposed method can be successfully translated to real-world PET scanners. When applied to a 20 cm diameter ×20 cm long cylindrical phantom the EB and SSS algorithms demonstrated very similar performance. However, on a larger 35 cm × 30 cm long cylinder the EB can better account for increased multiple scattering and out-of-FOV activity, providing more uniform images with 12%–36% reduced background variability. In typical PET ring sizes, the EB estimation can be performed in a matter of a few seconds compared to the several minutes needed for SSS, leading to efficiency advantages over the SSS implementation. as well.

Photon quantum entanglement in the MeV regime and its application in PET imaging

Positron Emission Tomography (PET) is a widely-used imaging modality for medical research and clinical diagnosis. Imaging of the radiotracer is obtained from the detected hit positions of the two positron annihilation photons in a detector array. The image is degraded by backgrounds from random coincidences and in-patient scatter events which require correction. In addition to the geometric information, the two annihilation photons are predicted to be produced in a quantum-entangled state, resulting in enhanced correlations between their subsequent interaction processes. To explore this, the predicted entanglement in linear polarisation for the two photons was incorporated into a simulation and tested by comparison with experimental data from a cadmium zinc telluride (CZT) PET demonstrator apparatus. Adapted apparati also enabled correlation measurements where one of the photons had undergone a prior scatter process. We show that the entangled simulation describes the measured correlations and, through simulation of a larger preclinical PET scanner, illustrate a simple method to quantify and remove the unwanted backgrounds in PET using the quantum entanglement information alone.

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 Simulations of the GE Signa PET/MR for Different Radioisotopes

NEMA characterization of PET systems is generally based on ¹⁸F because it is the most relevant radioisotope for the clinical use of PET. ¹⁸F has a half-life of 109.7 min and decays into stable ¹⁸O via β+ emission with a probability of over 96% and a maximum positron energy of 0.633 MeV. Other commercially available PET radioisotopes, such as ⁸²Rb and ⁶⁸Ga have more complex decay schemes with a variety of prompt gammas, which can directly fall into the energy window and induce false coincidence detections by the PET scanner.

TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO

Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO-Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.

Intensity normalization methods in brain FDG-PET quantification

BACKGROUND

The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output.

METHODS

Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings.

RESULTS

Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm³) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering.

CONCLUSIONS

Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.

PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library

This work demonstrates how computational and physical modelling of the positron emission tomography (PET) image acquisition process for a state-of-the-art integrated PET and magnetic resonance imaging (PET-MR) system can produce images comparable to the manufacturer. The GE SIGNA PET/MR scanner is manufactured by General Electric and has time-of-flight (TOF) capabilities of about 390 ps. All software development took place in the Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) library, which is a widely used open source software to reconstruct data as exported from emission tomography scanners. The new software developments will be integrated into STIR, providing the opportunity for researchers worldwide to establish and expand their image reconstruction methods. Furthermore, this work is of particular significance as it provides the first validation of TOF PET image reconstruction for real scanner datasets using the STIR library. This paper presents the methodology, analysis, and critical issues encountered in implementing an independent reconstruction software package. Acquired PET data were processed via several appropriate algorithms which are necessary to produce an accurate and precise quantitative image. This included mathematical, physical and anatomical modelling of the patient and simulation of various aspects of the acquisition. These included modelling of random coincidences using 'singles' rates per crystals, detector efficiencies and geometric effects. Attenuation effects were calculated by using the STIR's attenuation correction model. Modelling all these effects within the system matrix allowed the reconstruction of PET images which demonstrates the metabolic uptake of the administered radiopharmaceutical. These implementations were validated using measured phantom and clinical datasets. The developments are tested using the ordered subset expectation maximisation (OSEM) and the more recently proposed kernelised expectation maximisation (KEM) algorithm which incorporates anatomical information from MR images into PET reconstruction.

Spill-in counts in the quantification of 18F-florbetapir on Aβ-negative subjects: the effect of including white matter in the reference region

Background

We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβ-negative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volume correction (PVC) methods are applied and evaluated.

Methods

We evaluated magnetic resonance imaging and ¹⁸F-AV-45 positron emission tomography data from 122 cognitively normal (CN) patients recruited at the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cortex SUVRs were obtained by using the cerebellar grey matter (CGM) (SUVR_CGM) and the whole cerebellum (SUVR_WC) as reference regions. The correlations between the different SUVRs and the WM uptake (WM-SUVR_CGM) were studied in patients, and in a well-controlled framework based on Monte Carlo (MC) simulation. Activity maps for the MC simulation were derived from ADNI patients by using a voxel-wise iterative process (BrainViset). Ten WM uptakes covering the spectrum of WM values obtained from patient data were simulated for different patients. Three different PVC methods were tested (a) the regional voxel-based (RBV), (b) the iterative Yang (iY), and (c) a simplified analytical correction derived from our MC simulation.

Results

WM-SUVR_CGM followed a normal distribution with an average of 1.79 and a standard deviation of 0.243 (13.6%). SUVR_CGM was linearly correlated to WM-SUVR_CGM (r = 0.82, linear fit slope = 0.28). SUVR_WC was linearly correlated to WM-SUVR_CGM (r = 0.64, linear fit slope = 0.13). Our MC results showed that these correlations are compatible with those produced by isolated spill-in effect (slopes of 0.23 and 0.11). The impact of the spill-in was mitigated by using PVC for SUVR_CGM (slopes of 0.06 and 0.07 for iY and RBV), while SUVR_WC showed a negative correlation with SUVR_CGM after PVC. The proposed analytical correction also reduced the observed correlations when applied to patient data (r = 0.27 for SUVR_CGM, r = 0.18 for SUVR_WC).

Conclusions

There is a high correlation between WM uptake and the measured SUVR due to spill-in effect, and that this effect is reduced when including WM in the reference region. We also evaluated the performance of PVC, and we proposed an analytical correction that can be applied to preprocessed data.

Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

Anatomically-driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This work, consider the effect of spatial inconsistency between MR and PET images in hot and cold regions of the PET image. We investigate these effects on the kernel method from machine learning, in particular, the hybrid kernelized expectation maximization (HKEM). These were applied to Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the 'spill-in' of the affected cold regions and the 'spill-out' from the hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using KEM, against the 37% and 8% obtained with HKEM. The findings of this work suggest that including PET information in the kernel enhances the flexibility of the reconstruction in case of spatial inconsistency. Nevertheless, accurate registration and choice of the appropriate MR image for the creation of the kernel is essential to avoid artifacts, blurring, and bias.

Appropriately regularized OSEM can improve the reconstructed PET images of data with low count statistics

OBJECTIVE

With the increasing number of patients undergoing positron emission tomography (PET) scans and the fact that multiple whole body acquisitions are performed during therapy monitoring, the reduction of scan time as well as of the injected radioactive dose are important issues. However, short scan time and reduction of the injected radiation dose result in low count statistics, which significantly affects the quality of the reconstructed images and accurate diagnosis.

SUBJECTS AND METHODS

The aim of this study was to explore the effect of low count statistics on ordered subset expectation maximization regularized with median root prior (OS-MRP-OSL) reconstructed images. By optimizing OS-MRP-OSL we determine whether a satisfactory handling of the noise properties and bias can be achieved compared to post-filtered ordered subset expectation maximization (OSEM), which will lead to improved image quality in simulations with more noise. We used realistic simulated PET data of a thorax with lesions corresponding to tumors with different intensities.

RESULTS

OS-MRP-OSL provided reduced noise from post-filtered OSEM, without having the negative effect of blurring. On the other hand, bias presented no significant difference.

CONCLUSION

This work is relevant to future PET reconstruction of clinical images and PET-magnetic resonance investigations where the reduced injected dose will allow imaging a larger cohort of humans.

Effect of 176Lu intrinsic radioactivity on dual head PET system imaging and data acquisition, simulation, and experimental measurements

PURPOSE

In this work, the authors aim for the estimation of the effect of (176)Lu intrinsic radioactivity on the performance of a dual head PET system dedicated to small animal imaging.

METHODS

A prototype camera has been used as a reference system in order to validate two GATE simulation models, which were used for the investigation. The first model includes the (176)Lu intrinsic radioactivity, while the second does not. The two models have been designed in order to provide similar sensitivities, in terms of count rate performance and scatter fraction, in the linear range of activities. In addition, the model with the (176)Lu intrinsic radioactivity, has been validated in terms of background count rate. Different acquisition schemes have been examined in order to determine the optimum conditions to minimize the (176)Lu effects, while maintaining a high trues count rate. In addition, the effect on the image quality, in terms of spatial resolution, signal-to-noise ratio, and minimum detectable activity, was investigated.

RESULTS

Both models are in good agreement with the measured data. While, the presence of the (176)Lu altered the dead time of the model, it also affected the singles, trues, and randoms count rates. The noise equivalent count rate curves of the two models indicate that for low activities, the lack of (176)Lu radioactivity leads to better noise properties due to the underestimation of the randoms. Signal-to-noise ratio measurement on coincidence images confirm the aforementioned claim, since the model without the (176)Lu provides better less noisy images. Furthermore, the spatial resolution and the minimum detectable activity are overestimated.

CONCLUSIONS

It has been proven that the lack of the (176)Lu intrinsic radioactivity has an impact on the design of the simulation model's dead time. Even if there is an alignment with experimental results still the noise properties, for a wide range of activities, are overestimated. In addition, for low activities, better image quality, is presented, mainly due to the lack of the (176)Lu photons which blur the images. Furthermore, it was shown that the use of a high hardware threshold allows the application of a wide energy window, which improves the statistics without a significant compromise on the quality of the detected photons, mainly, due to the early rejection of the low energy photons and the low scatter factor.