Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study.

Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameterßwas varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration.Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, variousßvalues (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV).Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasingß, peaking atß= 550. The CNR for allß, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withß= 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration.Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

Exploring ASL perfusion MRI as a substitutive modality for 18F-FDG PET in determining the laterality of mesial temporal lobe epilepsy.

OBJECTIVE: The aim of this investigation was to determine whether a correlation could be discerned between perfusion acquired through ASL MRI and metabolic data acquired via 18F-fluorodeoxyglucose (18F-FDG) PET in mesial temporal lobe epilepsy (mTLE). METHODS: ASL MRI and 18F-FDG PET data were gathered from 22 mTLE patients. Relative cerebral blood flow (rCBF) asymmetry index (AIs) were measured using ASL MRI, and standardized uptake value ratio (SUVr) maps were obtained from 18F-FDG PET, focusing on bilateral vascular territories and key bitemporal lobe structures (amygdala, hippocampus, and parahippocampus). Intra-group comparisons were carried out to detect hypoperfusion and hypometabolism between the left and right brain hemispheres for both rCBF and SUVr in right and left mTLE. Correlations between the two AIs computed for each modality were examined. RESULTS: Significant correlations were observed between rCBF and SUVr AIs in the middle temporal gyrus, superior temporal gyrus, and hippocampus. Significant correlations were also found in vascular territories of the distal posterior, intermediate anterior, intermediate middle, proximal anterior, and proximal middle cerebral arteries. Intra-group comparisons unveiled significant differences in rCBF and SUVr between the left and right brain hemispheres for right mTLE, while hypoperfusion and hypometabolism were infrequently observed in any intracranial region for left mTLE. CONCLUSION: The study's findings suggest promising concordance between hypometabolism estimated by 18F-FDG PET and hypoperfusion determined by ASL perfusion MRI. This raises the possibility that, with prospective technical enhancements, ASL perfusion MRI could be considered an alternative modality to 18F-FDG PET in the future.

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study.

BACKGROUND: The Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (ß) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies. METHODS: 18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (ß value of 100-500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM. RESULTS: OSEM had 11.5-18.6% higher BV than Q.Clear using ß value of 500. Conversely, RC from OSEM to Q.Clear using ß value of 500 decreased by 3.3-7.7% for a sphere with a diameter of 10 mm and 2.5-5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a ß value of 500 was 5.2-8.1% in the smallest spheres compared to OSEM. When the ß value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between ß value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as ß increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions. CONCLUSIONS: Implementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal ß value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal ß value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended ß value of 300 for small lesions and ß value of 500 for large lesions in clinical study.

Accurate modeling and performance evaluation of a total-body pet scanner using Monte Carlo simulations.

BACKGROUND: The limited axial field-of-view (FOV) of conventional clinical positron emission tomography (PET) scanners (∼15 to 26 cm) allows detecting only 1% of all coincidence photons, hence limiting significantly their sensitivity. To overcome this limitation, the EXPLORER consortium developed the world's first total-body PET/CT scanner that significantly increased the sensitivity, thus enabling to decrease the scan duration or injected dose. PURPOSE: The purpose of this study is to perform and validate Monte Carlo simulations of the uEXPLORER PET scanner, which can be used to devise novel conceptual designs and geometrical configurations through obtaining features that are difficult to obtain experimentally. METHODS: The total-body uEXPLORER PET scanner was modeled using GATE Monte Carlo (MC) platform. The model was validated through comparison with experimental measurements of various performance parameters, including spatial resolution, sensitivity, count rate performance, and image quality, according to NEMA-NU2 2018 standards. Furthermore, the effects of the time coincidence window and maximum ring difference on the count rate and noise equivalent count rate (NECR) were evaluated. RESULTS: Overall, the validation study showed that there was a good agreement between the simulation and experimental results. The differences between the simulated and experimental total sensitivity for the NEMA and extended phantoms at the center of the FOV were 2.3% and 0.0%, respectively. The difference in peak NECR was 9.9% for the NEMA phantom and 1.0% for the extended phantom. The average bias between the simulated and experimental results of the full-width-at-half maximum (FWHM) for six different positions and three directions was 0.12 mm. The simulations showed that using a variable coincidence time window based on the maximum ring difference can reduce the effect of random coincidences and improve the NECR compared to a constant time coincidence window. The NECR corresponding to 252-ring difference was 2.11 Mcps, which is larger than the NECR corresponding to 336-ring difference (2.04 Mcps). CONCLUSION: The developed MC model of the uEXPLORER PET scanner was validated against experimental measurements and can be used for further assessment and design optimization of the scanner.

Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study.

OBJECTIVES: This study aimed to measure standardized uptake value (SUV) variations across different PET/computed tomography (CT) scanners to harmonize quantification across systems. METHODS: We acquired images using the National Electrical Manufacturers Association International Electrotechnical Commission phantom from three PET/CT scanners operated using routine imaging protocols at each site. The SUVs of lesions were assessed in the presence of reference values by a digital reference object (DRO) and recommendations by the European Association of Nuclear Medicine (EANM/EARL) to measure inter-site variations. For harmonization, Gaussian filters with tuned full width at half maximum (FWHM) values were applied to images to minimize differences in SUVs between reference and images. Inter-site variation of SUVs was evaluated in both pre- and postharmonization situations. Test-retest analysis was also carried out to evaluate repeatability. RESULTS: SUVs from different scanners became significantly more consistent, and inter-site differences decreased for SUV mean , SUV max and SUV peak from 17.3, 20.7, and 15.5% to 4.8, 4.7, and 2.7%, respectively, by harmonization ( P values <0.05 for all). The values for contrast-to-noise ratio in the smallest lesion of the phantom verified preservation of image quality following harmonization (>2.8%). CONCLUSIONS: Harmonization significantly lowered variations in SUV measurements across different PET/CT scanners, improving reproducibility while preserving image quality.

Synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET radiomic features in non-small cell lung cancer: Phantom and clinical studies.

OBJECTIVES: This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET image radiomic features in non-small cell lung cancer (NSCLC) patients, and investigating the robustness of features performance in differentiating NSCLC histopathology subtypes. METHODS: An in-house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full-width at half-maximum of post-reconstruction smoothing filter and acquisition parameters, including injected activity and test-retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi-automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions. RESULTS: Overall, 29% of radiomic features showed a COV ≤5% against motion. Forty-five percent and 76% of the features showed a COV ≤ 5% against the test-retest with and without motion in large lesions, respectively. Thirty-three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p-value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting). CONCLUSIONS: Motion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non-reproducibility.

Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging.

The amount of radiotracer injected into laboratory animals is still the most daunting challenge facing translational PET studies. Since low-dose imaging is characterized by a higher level of noise, the quality of the reconstructed images leaves much to be desired. Being the most ubiquitous techniques in denoising applications, edge-aware denoising filters, and reconstruction-based techniques have drawn significant attention in low-count applications. However, for the last few years, much of the credit has gone to deep-learning (DL) methods, which provide more robust solutions to handle various conditions. Albeit being extensively explored in clinical studies, to the best of our knowledge, there is a lack of studies exploring the feasibility of DL-based image denoising in low-count small animal PET imaging. Therefore, herein, we investigated different DL frameworks to map low-dose small animal PET images to their full-dose equivalent with quality and visual similarity on a par with those of standard acquisition. The performance of the DL model was also compared to other well-established filters, including Gaussian smoothing, nonlocal means, and anisotropic diffusion. Visual inspection and quantitative assessment based on quality metrics proved the superior performance of the DL methods in low-count small animal PET studies, paving the way for a more detailed exploration of DL-assisted algorithms in this domain.

Tumor volume-adapted SUVN as an alternative to SUVpeak for quantification of small lesions in PET/CT imaging: a proof-of-concept study.

PURPOSE: SUVpeak is a recommended quantification metric except for small lesions. We aimed to assess the averaged standard uptake value (SUVN) as an alternative to SUVpeak for small-lesion quantification. MATERIALS AND METHODS: NEMA-like phantom images were reconstructed using OSEM, OSEM + PSF, OSEM + TOF and OSEM + TOF + PSF with two post-smoothing Gaussian filters for different background activity levels. SUVmax, SUVN (N = 5, 10, 15, 20, 25, 30, 35 or 40 hottest voxels), and SUVpeak, relative percent error, contrast recovery, and volume recovery coefficients were quantified and assessed. RESULTS: SUVN did not have the limitations of SUVpeak for smaller lesions. In the smallest insert at 2.68 kBq/ml, optimum N values for OSEM, OSEM + PSF, OSEM + TOF and OSEM + TOF + PSF were 10, 5, 15, and 10 for SUVN, respectively. The same N values were obtained for metabolic tumor volumes (MTVs) for all reconstruction algorithms. At 5.30 kBq/ml, N = 5 was optimum for SUVN and MTVs. For the larger inserts, the optimum N increased and tended towards the maximum (similar to SUVpeak). CONCLUSIONS: SUVN is more accurate than SUVmax or SUVpeak for small lesions, while being as accurate in larger ones. This harmonizing capacity of SUVN can be beneficial for the quantitative analysis of small tumor volumes.

Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network.

OBJECTIVE: We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body 18F-FDG PET imaging. METHODS: Two- and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body 18F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE %) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis). RESULTS: Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs (%) were - 1.72 ± 4.22%, 3.75 ± 6.91% and - 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 ± 3.96%) and liver/lung (11.18 ± 3.23%) regions for DL-2DS. DL-3DS and DL-3DP performed slightly better in the chest region, leading to AREs of 11.16 ± 3.42% and 11.69 ± 2.71%, respectively (p value < 0.05). The joint histogram analysis resulted in correlation coefficients of 0.985, 0.980 and 0.981 for DL-2DS, DL-3DS and DL-3DP approaches, respectively. CONCLUSION: This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 18F-FDG PET images using emission-only data via a deep residual network. The proposed approach achieved accurate attenuation and scatter correction without the need for anatomical images, such as CT and MRI. The technique is applicable in a clinical setting on standalone PET or PET/MRI systems. Nevertheless, Deep-JASC showing promising quantitative accuracy, vulnerability to noise was observed, leading to pseudo hot/cold spots and/or poor organ boundary definition in the resulting PET images.

Performance evaluation of developed dedicated breast PET scanner and improvement of the spatial resolution by wobbling: a Monte Carlo study.

PURPOSE: Molecular imaging, particularly PET scanning, has become an important cancer diagnostic tool. Whole-body PET is not effective for local staging of cancer because of their declining efficiency in detecting small lesions. The preliminary results of the performance evaluation of designed dedicated breast PET scanner presented. METHODS AND MATERIALS: A new scanner is based on LYSO crystals coupled with SiPM, and it consists of 14 compact modules with a transaxial FOV of 180 mm in diameter. In this study, initial GATE simulation studies were performed to predict the spatial resolution, absolute sensitivity, noise equivalent count rate (NECR) and scatter fraction (SF) of the new design. Spatial wobbling acquisitions were also implemented. Finally, the obtained projections were reconstructed using analytical and iterative algorithms. RESULTS: The simulation results indicate that absolute sensitivity is 1.42% which is appropriate than other commercial breast PET systems. The calculated SF and NECR in our design are 20.6% and 21.8 kcps. The initial simulation results demonstrate the potential of this design for breast cancer detection. A small wobble motion to improve spatial resolution and contrast. CONCLUSION: The performance of the dedicated breast PET scanner is considered to be reasonable enough to support its use in breast cancer imaging.

The impact of iterative reconstruction protocol, signal-to-background ratio and background activity on measurement of PET spatial resolution.

OBJECTIVES: The present study aims to assess the impact of acquisition time, different iterative reconstruction protocols as well as image context (including contrast levels and background activities) on the measured spatial resolution in PET images. METHODS: Discovery 690 PET/CT scanner was used to quantify spatial resolutions in terms of full width half maximum (FWHM) as derived (i) directly from capillary tubes embedded in air and (ii) indirectly from 10 mm-diameter sphere of the NEMA phantom. Different signal-to-background ratios (SBRs), background activity levels and acquisition times were applied. The emission data were reconstructed using iterative reconstruction protocols. Various combinations of iterations and subsets (it × sub) were evaluated. RESULTS: For capillary tubes, improved FWHM values were obtained for higher it × sub, with improved performance for PSF algorithms relative to non-PSF algorithms. For the NEMA phantom, by increasing acquisition times from 1 to 5 min, intrinsic FWHM for reconstructions with it × sub 32 (54) was improved by 15.3% (13.2%), 15.1% (13.8%), 14.5% (12.8%) and 13.7% (12.7%) for OSEM, OSEM + PSF, OSEM + TOF and OSEM + PSF + TOF, respectively. Furthermore, for all reconstruction protocols, the FWHM improved with more impact for higher it × sub. CONCLUSION: Our results indicate that PET spatial resolution is greatly affected by SBR, background activity and the choice of the reconstruction protocols.

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.

Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging.

OBJECTIVES: We aim to develop and rigorously evaluate an image-based deconvolution method to jointly compensate respiratory motion and partial volume effects (PVEs) for quantitative oncologic PET imaging, including studying the impact of various reconstruction algorithms on quantification performance. PROCEDURES: An image-based deconvolution method that incorporated wavelet-based denoising within the Lucy-Richardson algorithm was implemented and assessed. The method was evaluated using phantom studies with signal-to-background ratios (SBR) of 4 and 8, and clinical data of 10 patients with 42 lung lesions ≤30 mm in diameter. In each study, PET images were reconstructed using four different algorithms: OSEM-basic, PSF, TOF, and TOFPSF. The performance was quantified using contrast recovery (CR), coefficient of variation (COV) and contrast-to-noise-ratio (CNR) metrics. Further, in each study, variabilities arising due to the four different reconstruction algorithms were assessed. RESULTS: In phantom studies, incorporation of wavelet-based denoising improved COV in all cases. Processing images using proposed method yielded significantly higher CR and CNR particularly in small spheres, for all reconstruction algorithms and all SBRs (P < 0.05). In patient studies, processing images using the proposed method yielded significantly higher CR and CNR (P < 0.05). The choice of the reconstruction algorithm impacted quantification performance for changes in motion amplitude, tumor size and SBRs. CONCLUSIONS: Our results provide strong evidence that the proposed joint-compensation method can yield improved PET quantification. The choice of the reconstruction algorithm led to changes in quantitative accuracy, emphasizing the need to carefully select the right combination of reconstruction-image-based compensation methods.

NEMA NU-4 2008 performance evaluation of Xtrim-PET: A prototype SiPM-based preclinical scanner.

PURPOSE: Xtrim-PET is a newly designed Silicon Photomultipliers (SiPMs)-based prototype PET scanner dedicated for small laboratory animal imaging. We present the performance evaluation of the Xtrim-PET scanner following NEMA NU-4 2008 standards to help optimizing scanning protocols which can be achieved through standard and reliable system performance characterization. METHODS: The performance assessment was conducted according to the National Electrical Manufacturers Association (NEMA) NU-4 2008 standards in terms of spatial resolution, sensitivity, counting rate performance, scatter fraction and image quality. The in vivo imaging capability of the scanner is also showcased through scanning a normal mouse injected with 18 F-FDG. Furthermore, the performance characteristics of the developed scanner are compared with commercially available systems and current prototypes. RESULTS: The volumetric spatial resolution at 5 mm radial offset from the central axis of the scanner is 6.81 µl, whereas a peak absolute sensitivity of 2.99% was achieved using a 250-650 keV energy window and a 10 ns timing window. The peak noise-equivalent count rate (NECR) using a mouse-like phantom is 113.18 kcps at 0.34 KBq/cc with 12.5% scatter fraction, whereas the NECR peaked at 82.76 kcps for an activity concentration level of 0.048 KBq/cc with a scatter fraction of 25.8% for rat-like phantom. An excellent uniformity (3.8%) was obtained using NEMA image quality phantom. Recovery coefficients of 90%, 86%, 68%, 40% and 12% were calculated for rod diameters of 5, 4, 3, 2 and 1 mm, respectively. Spill-over ratios for air-filled and water-filled chambers were 35% and 25% without applying any correction for attenuation and Compton scattering effects. CONCLUSION: Our findings revealed that beyond compactness, lightweight, easy installation and good energy resolution, the Xtrim-PET prototype presents a reasonable performance making it suitable for preclinical molecular imaging-based research.

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC).

OBJECTIVE: To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. METHODS: Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images. RESULTS: Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUVmean was - 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, - 0.83 to 1.18). SUVmax had mean RE (%) of - 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of - 3.99 ± 2.11 (range, - 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99. CONCLUSIONS: Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners. KEY POINTS: • We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.

Impact of image reconstruction methods on quantitative accuracy and variability of FDG-PET volumetric and textural measures in solid tumors.

OBJECTIVE: This study aims to assess the impact of different image reconstruction methods on PET/CT quantitative volumetric and textural parameters and the inter-reconstruction variability of these measurements. METHODS: A total of 25 oncology patients with 65 lesions (between 2017 and 2018) and a phantom with signal-to-background ratios (SBR) of 2 and 4 were included. All images were retrospectively reconstructed using OSEM, PSF only, TOF only, and TOFPSF with 3-, 5-, and 6.4-mm Gaussian filters. The metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. The relative percent error (ΔMTV and ΔTLG) with respect to true values, volume recovery coefficients, and Dice similarity coefficient, as well as inter-reconstruction variabilities were quantified and assessed. In clinical scans, textural features (coefficient of variation, skewness, and kurtosis) were determined. RESULTS: Among reconstruction methods, mean ΔMTV differed by -163.5 ± 14.1% to 6.3 ± 6.2% at SBR2 and -42.7 ± 36.7% to 8.6 ± 3.1 at SBR4. Dice similarity coefficient significantly increased by increasing SBR from 2 to 4, ranging from 25.7 to 83.4% between reconstruction methods. Mean ΔTLG was -12.0 ± 1.7 for diameters > 17 mm and -17.8 ± 7.8 for diameters ≤ 17 mm at SBR4. It was -31.7 ± 4.3 for diameters > 17 mm and -14.2 ± 5.8 for diameters ≤ 17 mm at SBR2. Textural features were prone to variations by reconstruction methods (p < 0.05). CONCLUSIONS: Inter-reconstruction variability was significantly affected by the target size, SBR, and cut-off threshold value. In small tumors, inter-reconstruction variability was noteworthy, and quantitative parameters were strongly affected. TOFPSF reconstruction with small filter size produced greater improvements in performance and accuracy in quantitative PET/CT imaging. KEY POINTS: • Quantitative volumetric PET evaluation is critical for the analysis of tumors. • However, volumetric and textural evaluation is prone to important variations according to different image reconstruction settings. • TOFPSF reconstruction with small filter size improves quantitative analysis.

Capturing Bone Signal in MRI of Pelvis, as a Large FOV Region, Using TWIST Sequence and Generating a 5-Class Attenuation Map for Prostate PET/MRI Imaging.

PURPOSE: Prostate imaging is a major application of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). Currently, MRI-based attenuation correction (MRAC) for whole-body PET/MRI in which the bony structures are ignored is the main obstacle to successful implementation of the hybrid modality in the clinical work flow. Ultrashort echo time sequence captures bone signal but needs specific hardware-software and is challenging in large field of view (FOV) regions, such as pelvis. The main aims of the work are (1) to capture a part of the bone signal in pelvis using short echo time (STE) imaging based on time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence and (2) to consider the bone in pelvis attenuation map (µ-map) to MRAC for PET/MRI systems. PROCEDURES: Time-resolved angiography with interleaved stochastic trajectories, which is routinely used for MR angiography with high temporal and spatial resolution, was employed for fast/STE MR imaging. Data acquisition was performed in a TE of 0.88 milliseconds (STE) and 4.86 milliseconds (long echo time [LTE]) in pelvis region. Region of interest (ROI)-based analysis was used for comparing the signal-to-noise ratio (SNR) of cortical bone in STE and LTE images. A hybrid segmentation protocol, which is comprised of image subtraction, a Fuzzy-based segmentation, and a dedicated morphologic operation, was used for generating a 5-class µ-map consisting of cortical bone, air cavity, fat, soft tissue, and background (µ-mapMR-5c). A MR-based 4-class µ-map (µ-mapMR-4c) that considered soft tissue rather than bone was generated. As such, a bilinear (µ-mapCT-ref), 5 (µ-mapCT-5c), and 4 class µ-map (µ-mapCT-4c) based on computed tomography (CT) images were generated. Finally, simulated PET data were corrected using µ-mapMR-5c (PET-MRAC5c), µ-mapMR-4c (PET-MRAC4c), µ-mapCT-5c (PET-CTAC5c), and µ-mapCT-ref (PET-CTAC). RESULTS: The ratio of SNRbone to SNRair cavity in LTE images was 0.8, this factor was increased to 4.4 in STE images. The Dice, Sensitivity, and Accuracy metrics for bone segmentation in proposed method were 72.4% ± 5.5%, 69.6% ± 7.5%, and 96.5% ± 3.5%, respectively, where the segmented CT served as reference. The mean relative error in bone regions in the simulated PET images were -13.98% ± 15%, -35.59% ± 15.41%, and 1.81% ± 12.2%, respectively, in PET-MRAC5c, PET-MRAC4c, and PET-CTAC5c where PET-CTAC served as the reference. Despite poor correlation in the joint histogram of µ-mapMR-4c versus µ-mapCT-5c (R2 > 0.78) and PET-MRAC4c versus PET-CTAC5c (R2 = 0.83), high correlations were observed in µ-mapMR-5c versus µ-mapCT-5c (R2 > 0.94) and PET-MRAC5c versus PET-CTAC5c (R2 > 0.96). CONCLUSIONS: According to the SNRSTE, pelvic bone, the cortical bone can be separate from air cavity in STE imaging based on TWIST sequence. The proposed method generated an MRI-based µ-map containing bone and air cavity that led to more accurate tracer uptake estimation than MRAC4c. Uptake estimation in hybrid PET/MRI can be improved by employing the proposed method.

Reconstruction/segmentation of attenuation map in TOF-PET based on mixture models.

Attenuation correction is known as a necessary step in positron emission tomography (PET) system to have accurate and quantitative activity images. Emission-based method is known as a promising approach for attenuation map estimation on TOF-PET scanners. The proposed method in this study imposes additional histogram-based information as a mixture model prior on the emission-based approach using maximum a posteriori (MAP) framework to improve its performance and make such a nearly segmented attenuation map. To eliminate misclassification of histogram modeling, a Median root prior is incorporated on the proposed approach to reduce the noise between neighbor voxels and encourage spatial smoothness in the reconstructed attenuation map. The joint-MAP optimization is carried out as an iterative approach wherein an alteration of the activity and attenuation updates is followed by a mixture decomposition of the attenuation map histogram. Also, the proposed method can segment attenuation map during the reconstruction. The evaluation of the proposed method on the numerical, simulation and real contexts indicate that the presented method has the potential to be used as a stand-alone method or even combined with other methods for attenuation correction on PET/MR systems.

Quantification and reduction of respiratory induced artifacts in positron emission tomography/computed tomography using the time-of-flight technique.

OBJECTIVE: The aim of this study was to investigate the impact of time-of-flight (TOF) on quantification and reduction of respiratory artifacts. PATIENTS AND METHODS: The National Electrical Manufacturers Association phantom was used for optimization of reconstruction parameters. Twenty seven patients with lesions located in the diaphragmatic region were evaluated. The PET images were retrospectively reconstructed using non-TOF (routine protocol in our department) and TOF algorithms with different reconstruction parameters. Maximum standardized uptake value, estimated maximum tumor diameter, coefficient of variation, signal-to-noise ratio, and lesion-to-background-ratio were also evaluated. RESULTS: On the basis of phantom experiments, TOF algorithms with two iterations, 18 subsets, and 5.4 mm and 6.4 mm postsmoothing filter reduced the noise by 3.1 and 12.6% in phantom with 2 : 1 activity ratio, and 3.0 and 13.1% in phantom with 4 : 1 activity ratio. The TOF algorithm with two iterations, 18 subsets, and 6.4 mm postsmoothing filter had the highest signal-to-noise value, and was selected as the optimal TOF reconstruction. Mean relative difference for signal-to-noise between non-TOF and optimal TOF in phantom with 2 : 1 and 4 : 1 activity ratio were 11.6 and 18.7%, respectively. In clinical data, the mean relative difference for estimated maximum tumor diameter and maximum standardized uptake value between routine protocol and optimal TOF algorithm were -6.3% (range: -20.4 to -0.6%) and 13.2% (range: 0.3-57.6%), respectively. CONCLUSION: Integration of TOF in reconstruction algorithm remarkably improved the white band artifact in the diaphragmatic region. This technique affected the quantification accuracy and resulted in smaller tumor size and higher standardized uptake value in tumors located in/near the diaphragmatic region.

Development and validation of an accurate GATE model for NeuroPET scanner.

NeuroPET is a cylindrical full ring mobile PET/CT scanner for brain imaging that was developed by Photo Diagnostic Systems, Inc. The scanner has 7 modules, each with 3×4 detector blocks. The detectors have two layers of scintillator arrays with a half pixel pitch offset to realize two levels of depth of interaction. In this study, we evaluated the NeuroPET scanner modeled in the GATE simulation tool and analyzed the acquired data to better understand the contribution of inter-detector scattering (IDS). The results show that the average difference between simulated and measured data for a point-like source is 2.5%. The differences are 4.7% and 2.7% for NEMA line source in two data acquisition modes and 5.5% for peak NECR measurement. IDS evaluation indicated that the total fractions of the cross-layer crystal scatter (CLCS) and inter-layer crystal scatter (ILCS) events in singles detection mode are 1.98% and 7.98%, respectively. Approximately 90% of these CLCS events deposit most of their energy in the crystal layer other than the layer of first interaction. Additionally, no significant difference in ILCS fractions between the two layers (8.05% vs 7.35%) was observed. The simulation results demonstrate that ILCS events account for ∼79% of the total mis-positioned events.