Nema Ve 2-2018 Pdf Free Download

[Emir Ballard]

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Our purpose was to evaluate the performance of the Biograph Vision Quadra PET/CT system. This new system is based on the Biograph Vision 600, using the same silicon photomultiplier-based detectors with 3.2 3.2 20 mm lutetium-oxoorthosilicate crystals. The 32 detector rings of the Quadra provide a 4-fold larger axial field of view (AFOV) of 106 cm, enabling imaging of major organs in 1 bed position. Methods: The physical performance of the scanner was evaluated according to the National Electrical Manufacturers Association NU 2-2018 standard, with additional experiments to characterize energy resolution. Image quality was assessed with foreground-to-background ratios of 4:1 and 8:1. Additionally, a clinical 18F-FDG PET study was reconstructed with varying frame durations. In all experiments, data were acquired using the maximum ring distance of 322 crystals (MRD 322), whereas image reconstructions could be performed with a maximum ring distance of only 85 crystals (MRD 85). Results: The spatial resolution at full width at half maximum in the radial, tangential, and axial directions was 3.3, 3.4, and 3.8 mm, respectively. The sensitivity was 83 cps/kBq for MRD 85 and 176 cps/kBq for MRD 322. The noise-equivalent count rates (NECRs) at peak were 1,613 kcps for MRD 85 and 2,956 kcps for MRD 322, both at 27.49 kBq/mL. The respective scatter fractions at peak NECR equaled 36% and 37%. The time-of-flight resolution at peak NECR was 228 ps for MRD 85 and 230 ps for MRD 322. Image contrast recovery ranged from 69.6% to 86.9% for 4:1 contrast ratios and from 77.7% to 92.6% for 8:1 contrast ratios reconstructed using point-spread function time of flight with 8 iterations and 5 subsets. Thirty-second frames provided readable lesion detectability and acceptable noise levels in clinical images. Conclusion: The Biograph Vision Quadra PET/CT device has spatial and time resolution similar to those of the Biograph Vision 600 but exhibits improved sensitivity and NECR because of its extended AFOV. The reported spatial resolution, time resolution, and sensitivity make it a competitive new device in the class of PET scanners with an extended AFOV.

Purpose: The DMI PET/CT is a modular silicon photomultiplier-based scanner with an axial field-of-view (FOV) between 15 and 25 cm depending on ring configuration (3, 4, or 5 rings). A new generation of the system includes a reengineered detector module, featuring improved electronics and an additional 6th ring, extending the axial FOV to 30 cm. We report on the performance evaluation of the 6-ring upgraded Generation 2 (Gen2) system while values are also reported for the 5-ring configuration of the very same system prior to the upgrade.

Methods: PET performance was evaluated using the NEMA NU 2-2018 standard for spatial resolution, sensitivity, image quality, count rate performance, timing resolution, and image co-registration accuracy. Patient images were used to assess image quality.

Conclusions: The higher sensitivity of the 6-ring DMI compared to the 5-ring configuration may lead to improved image quality of clinical images at reduced scan time. Additionally, it could equally be used to allow improved temporal sampling and/or reduced overall scan time in dynamic acquisitions. Conversely, temporal sampling and scan time could be traded per application to further drive injected dose at lower levels.

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.

Positron emission tomography (PET)/computed tomography (CT) is a well-established molecular imaging technique for cancer diagnosis and treatment response monitoring [1]. Its use in clinical routine has increased steadily in recent years and, and at the same time, its performance has never stopped evolving over time. Recent developments such as time-of-flight (TOF), point spread function modelling (PSF), digital PET detectors, and long axial field-of-view (FOV) have taken the imaging capabilities of the technique even further [2, 3].

The use of simulations in PET has long been recognised as a valuable tool for a number of applications, including detector design, evaluation of image reconstruction algorithms, correction techniques, dosimetry, and pharmacokinetic modelling [4]. In emission tomography, simulations can be useful to study the impact of different parameters (acquisition, reconstruction, corrections, etc.) on the quality of PET images with more flexibility than what could be achieved with physical phantoms [5], while requiring fewer resources and less expense.

There are several PET imaging simulators in the scientific community that differ in their level of complexity and computational resource requirements (for a complete review, see [6, 7]). Monte Carlo-based (MC) simulators are usually considered the gold standard as they can adequately model the physical processes that occur during radiation transport in media [8]. Among the MC simulators, Geant4 Application for Tomographic Emission (GATE) [9] is a well-known simulation toolkit, historically developed for nuclear imaging with specific layers for modelling sources, detection geometries, and detector electronic responses. GATE has been successfully used to validate the performance of several existing PET systems or to study the impact of different detector designs [7, 10,11,12,13,14,15,16,17,18].

Regarding the validation of MC models of recent digital PET systems, the Vereos machine (Philips Medical Systems, Eindhoven, The Netherlands) has been studied extensively by Salvadori et al. [7]. The Vision PET/CT system (Siemens, Erlangen, Germany), for its part, has been modelled by Zein et al. [24] in the context of a hypothetical model with sparse detector module rings and extended axial field of view. MC models of the Discovery MI (GE HealthCare, Chicago, Il, USA) have been investigated by two groups [23, 25]. Tiwari et al. [23] have used a GATE model to predict the performance of the system with extended axial FOVs. Kalaitzidis et al. [25] have worked on a pipeline to reconstruct PET images from data simulated in GATE using CASToR. None of these works provide a complete investigation of their models as the former did not study the reconstructed image quality, and the latter did not model the behaviour of the DMI at very high count rates.

When performing acquisitions on the DMI, experimental data could either be stored in list mode or in three-dimensional (3D) sinograms. The list mode data could then be rearranged in 3D sinograms using a proprietary offline reconstruction package, hereafter referred to as the PET toolbox (GE HealthCare, Chicago, Il, USA). The output of a GATE simulation provides a list mode with single and coincidence events, stored as a Python NumPy array. This list mode includes the exact position of the annihilations, and the type of each coincidence (i.e. true, random, or scatter). Simulated list modes were organised into 3D sinograms using GATE detector numbers and look-up tables provided by the manufacturer. The 3D sinograms were of dimensions 415 (radial bins) 1261 (planes) 272 (projections). The TOF sinograms were of dimensions 1261 (planes) 29 (time) 415 (radial bins) 272 (projections).

Image reconstruction of clinical data was done using the PET toolbox. It includes reconstruction algorithms such as filtered back-projection (FBP) and ordered-subsets expectation-maximisation (OSEM). Standard correction methods (implemented by the manufacturer) are provided within the PET toolbox: normalisation, decay, well-counter (calibration for quantification), attenuation, deadtime, random and scatter corrections [30,31,32,33].

In order to reconstruct simulated data, a specific interface has been added to the PET toolbox, allowing corrections for normalisation, attenuation, randoms and scatters. Currently, it does not support corrections for decay, deadtime and well-counter. This interface requires not only the sinograms to be reconstructed, but also calibration files for normalisation correction (geometrical factors and individual detector efficiencies), and an attenuation map for attenuation correction. The normalisation correction was performed according to a component-based method [34, 35]. To determine geometrical factors (each detector has the same detection efficiency in the model), two simulations were run at very high statistics where only true coincidences were recorded. The first simulation was performed with an annulus source of 32 cm radius, of 1 mm thickness, and 20 cm long, centred on the FOV of the scanner, where 3.2 billion true coincidences were collected over 253 CPU days of simulation. The normalisation factors were calculated with the help of a second simulation, using a flood source of 10 cm radius and 22 cm height positioned at the centre of the FOV, where 562 million of true counts were collected. This simulation took 137 CPU days to complete. The simulated attenuation map was generated in GATE using the MuMap actor. The output image contained the spatial distribution of the linear attenuation coefficient at 511 keV for the given study. Its dimensions and resolution were 256 256 71 pixels and 2 2 3 mm3, respectively. For the DMI, the estimation of random coincidences is performed on single events [30]. The proposed randoms from singles (RFS) formula shown in Eq. 1 has been applied to estimate random counts of the simulated data. For two given detectors x and y and their associated singles count rates \(s_x\) and \(s_y\), the estimated randoms rate between the two detectors \(r_xy\) is:

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