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.
Results
The average system sensitivity was measured at 32.76 cps/kBq (~ 47% increase to 5 rings at 22.29 cps/kBq) while noise equivalent count rate peaked at 434.3 kcps corresponding to 23.6 kBq/mL (~ 60% increase to Generation 1 (Gen1) and 39% to Gen2 5 rings). Contrast recovery ranged between 54.5 and 85.8% similar to 5 rings, while the 6 rings provided lower background variability (2.3–8.5% for 5 rings vs 1.9–6.8% for 6 rings) and lower lung error (4.0% for the 5 rings and 3.16% for the 6 rings). Transverse/axial full width at half-maximum (FWHM) at 1 cm (3.79/4.26 mm) and 10 cm (4.29/4.55 mm), scatter fraction (40.2%), energy resolution (9.63%), and time-of-flight (TOF) resolution (389.6 ps at 0 kBq/mL) were in line to previously reported values measured across different system configurations. Improved patient image quality is obtained with the 6 rings compared to the 5 rings, while image quality is retained even at reduced scan times, enabling WB dynamic acquisitions.
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.
Purpose
To enhance the image quality of oncology
18
F-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks.
Methods
List-mode data from 277
18
...F-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (
n
= 237), validation (
n
= 15) and testing (
n
= 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series).
Results
OSEM reconstructions demonstrated up to 22% difference in lesion SUV
max
, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time.
Conclusion
Deep learning–based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.
Purpose
To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).
...Methods
A total of 273
18
F-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation–maximisation (BSREM) algorithm with and without ToF. The images were then split into training (
n
= 208), validation (
n
= 15), and testing (
n
= 50) sets. Three DL-ToF models were trained to transform non-ToF BSREM images to their target ToF images with different levels of DL-ToF strength (low, medium, high). The models were objectively evaluated using the testing set based on standardised uptake value (SUV) in 139 identified lesions, and in normal regions of liver and lungs. Three radiologists subjectively rated the models using testing sets based on lesion detectability, diagnostic confidence, and image noise/quality.
Results
The non-ToF, DL-ToF low, medium, and high methods resulted in − 28 ± 18, − 28 ± 19, − 8 ± 22, and 1.7 ± 24% differences (mean; SD) in the SUV
max
for the lesions in testing set, compared to ToF-BSREM image. In background lung VOIs, the SUV
mean
differences were 7 ± 15, 0.6 ± 12, 1 ± 13, and 1 ± 11% respectively. In normal liver, SUV
mean
differences were 4 ± 5, 0.7 ± 4, 0.8 ± 4, and 0.1 ± 4%. Visual inspection showed that our DL-ToF improved feature sharpness and convergence towards ToF reconstruction. Blinded clinical readings of testing sets for diagnostic confidence (scale 0–5) showed that non-ToF, DL-ToF low, medium, and high, and ToF images scored 3.0, 3.0, 4.1, 3.8, and 3.5 respectively. For this set of images, DL-ToF medium therefore scored highest for diagnostic confidence.
Conclusion
Deep learning–based image enhancement models may provide converged ToF-equivalent image quality without ToF reconstruction. In clinical scoring DL-ToF-enhanced non-ToF images (medium and high) on average scored as high as, or higher than, ToF images. The model is generalisable and hence, could be applied to non-ToF images from BGO-based PET/CT scanners.
Abstract
To evaluate whether quantitative PET parameters of motion-corrected
68
Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine ...tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent
68
Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans. PET images were reconstructed using ordered-subsets expectation maximization (OSEM) and a fully convergent iterative image reconstruction algorithm with β-values of 1000 (BSREM
1000
). A data-driven gating (DDG) technique (MOTIONFREE, GE Healthcare) was applied to extract respiratory triggers and use them for PET motion correction within both reconstructions. PET parameters among different samples were compared using non-parametric tests. Receiver operating characteristics (ROC) analyzed the ability of PET parameters to differentiate IPAS and pNETs. SUVmax was able to distinguish pNET from accessory spleens and IPAs in BSREM
1000
reconstructions (p < 0.05). This result was more reliable using DDG-based motion correction (p < 0.003) and was achieved in both OSEM and BSREM
1000
reconstructions. For differentiating accessory spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0.765 (sensitivity 56%)/0.846 (sensitivity 62%)/0.840 (sensitivity 63%) for SUVmax 36.7/41.9/36.9/41.7 in OSEM/BSREM
1000
/OSEM + DDG/BSREM
1000
+ DDG, respectively. BSREM
1000
+ DDG can accurately differentiate pNET from accessory spleen. Both BSREM
1000
and DDG lead to a significant SUV increase compared to OSEM and non-motion-corrected data.
Background: Investigation of the clinical feasibility of dynamic whole-body (WB) 18FFDG PET, including standardized uptake value (SUV), rate of irreversible uptake (Ki), and apparent distribution ...volume (Vd) in physiologic tissues, and comparison between inflammatory/infectious and cancer lesions. Methods: Twenty-four patients were prospectively included to undergo dynamic WB 18FFDG PET/CT for clinically indicated re-/staging of oncological diseases. Parametric maps of Ki and Vd were generated using Patlak analysis alongside SUV images. Maximum parameter values (SUVmax, Kimax, and Vdmax) were measured in liver parenchyma and in malignant or inflammatory/infectious lesions. Lesion-to-background ratios (LBRs) were calculated by dividing the measurements by their respective mean in the liver tissue. Results: Seventy-seven clinical target lesions were identified, 60 malignant and 17 inflammatory/infectious. Kimax was significantly higher in cancer than in inflammatory/infections lesions (3.0 vs. 2.0, p = 0.002) while LBRs of SUVmax, Kimax, and Vdmax did not differ significantly between the etiologies: LBR (SUVmax) 3.3 vs. 2.9, p = 0.06; LBR (Kimax) 5.0 vs. 4.4, p = 0.05, LBR (Vdmax) 1.1 vs. 1.0, p = 0.18). LBR of inflammatory/infectious and cancer lesions was higher in Kimax than in SUVmax (4.5 vs. 3.2, p < 0.001). LBRs of Kimax and SUVmax showed a strong correlation (Spearman’s rho = 0.83, p < 0.001). Conclusions: Dynamic WB 18FFDG PET/CT is feasible in a clinical setting. LBRs of Kimax were higher than SUVmax. Kimax was higher in malignant than in inflammatory/infectious lesions but demonstrated a large overlap between the etiologies.
Highlights • Dynamic image acquisition protocols are increasingly used in emission tomography. • Need for computational phantoms to describe both the spatial and temporal distribution of ...radiotracers. • A 5D anthropomorphic phantom was developed, for parametric imaging simulations in emission tomography. • The phantom is based on real 4D MR data and a detailed multi-compartmental pharmacokinetic modelling simulator. • Example applications are shown in parametric 18 FFDG and 15 OH2 O PET imaging.
Purpose:
Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short ...half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms.
Methods:
In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT.
Results:
The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM.
Conclusions:
Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution recovery image reconstruction. The benefits are expected to be more substantial for more energetic positron emitting isotopes such as Oxygen-15 and Rubidium-82.
Objective
Estimation of nonlinear micro-parameters is a computationally demanding and fairly challenging process, since it involves the use of rather slow iterative nonlinear fitting algorithms and ...it often results in very noisy voxel-wise parametric maps. Direct reconstruction algorithms can provide parametric maps with reduced variance, but usually the overall reconstruction is impractically time consuming with common nonlinear fitting algorithms.
Methods
In this work we employed a recently proposed direct parametric image reconstruction algorithm to estimate the parametric maps of all micro-parameters of a two-tissue compartment model, used to describe the kinetics of
18
FFDG. The algorithm decouples the tomographic and the kinetic modelling problems, allowing the use of previously developed post-reconstruction methods, such as the generalised linear least squares (GLLS) algorithm.
Results
Results on both clinical and simulated data showed that the proposed direct reconstruction method provides considerable quantitative and qualitative improvements for all micro-parameters compared to the conventional post-reconstruction fitting method. Additionally, region-wise comparison of all parametric maps against the well-established filtered back projection followed by post-reconstruction non-linear fitting, as well as the direct Patlak method, showed substantial quantitative agreement in all regions.
Conclusions
The proposed direct parametric reconstruction algorithm is a promising approach towards the estimation of all individual microparameters of any compartment model. In addition, due to the linearised nature of the GLLS algorithm, the fitting step can be very efficiently implemented and, therefore, it does not considerably affect the overall reconstruction time.
Purpose:
The Ingenuity time-of-flight (TF) PET/MR is a recently developed hybrid scanner combining the molecular imaging capabilities of PET with the excellent soft tissue contrast of MRI. It is ...becoming common practice to characterize the system's point spread function (PSF) and understand its variation under spatial transformations to guide clinical studies and potentially use it within resolution recovery image reconstruction algorithms. Furthermore, due to the system's utilization of overlapping and spherical symmetric Kaiser-Bessel basis functions during image reconstruction, its image space PSF and reconstructed spatial resolution could be affected by the selection of the basis function parameters. Hence, a detailed investigation into the multidimensional basis function parameter space is needed to evaluate the impact of these parameters on spatial resolution.
Methods:
Using an array of 12 × 7 printed point sources, along with a custom made phantom, and with the MR magnet on, the system's spatially variant image-based PSF was characterized in detail. Moreover, basis function parameters were systematically varied during reconstruction (list-mode TF OSEM) to evaluate their impact on the reconstructed resolution and the image space PSF. Following the spatial resolution optimization, phantom, and clinical studies were subsequently reconstructed using representative basis function parameters.
Results:
Based on the analysis and under standard basis function parameters, the axial and tangential components of the PSF were found to be almost invariant under spatial transformations (∼4 mm) while the radial component varied modestly from 4 to 6.7 mm. Using a systematic investigation into the basis function parameter space, the spatial resolution was found to degrade for basis functions with a large radius and small shape parameter. However, it was found that optimizing the spatial resolution in the reconstructed PET images, while having a good basis function superposition and keeping the image representation error to a minimum, is feasible, with the parameter combination range depending upon the scanner's intrinsic resolution characteristics.
Conclusions:
Using the printed point source array as a MR compatible methodology for experimentally measuring the scanner's PSF, the system's spatially variant resolution properties were successfully evaluated in image space. Overall the PET subsystem exhibits excellent resolution characteristics mainly due to the fact that the raw data are not under-sampled/rebinned, enabling the spatial resolution to be dictated by the scanner's intrinsic resolution and the image reconstruction parameters. Due to the impact of these parameters on the resolution properties of the reconstructed images, the image space PSF varies both under spatial transformations and due to basis function parameter selection. Nonetheless, for a range of basis function parameters, the image space PSF remains unaffected, with the range depending on the scanner's intrinsic resolution properties.
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