Ordered Subset Expectation Maximization Reconstruction Research Articles

Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm.

Positron Emission Tomography (PET) imaging after Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing parameter lowered RC, COV and BV, while CNR was maximized at = 4000; further increase resulted in oversmoothing. For quantification purposes, = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use.

Comparison of quantitative whole body PET parameters on [68Ga]Ga-PSMA-11 PET/CT using ordered Subset Expectation Maximization (OSEM) vs. bayesian penalized likelihood (BPL) reconstruction algorithms in men with metastatic castration-resistant prostate cancer

BackgroundPSMA PET/CT is a predictive and prognostic biomarker for determining response to [177Lu]Lu-PSMA-617 in patients with metastatic castration resistant prostate cancer (mCRPC). Thresholds defined to date may not be generalizable to newer image reconstruction algorithms. Bayesian penalized likelihood (BPL) reconstruction algorithm is a novel reconstruction algorithm that may improve contrast whilst preventing introduction of image noise. The aim of this study is to compare the quantitative parameters obtained using BPL and the Ordered Subset Expectation Maximization (OSEM) reconstruction algorithms.MethodsFifty consecutive patients with mCRPC who underwent [68Ga]Ga-PSMA-11 PET/CT using OSEM reconstruction to assess suitability for [177Lu]Lu-PSMA-617 therapy were selected. BPL algorithm was then used retrospectively to reconstruct the same PET raw data. Quantitative and volumetric measurements such as tumour standardised uptake value (SUV)max, SUVmean and Molecular Tumour Volume (MTV-PSMA) were calculated on both reconstruction methods. Results were compared (Bland-Altman, Pearson correlation coefficient) including subgroups with low and high-volume disease burdens (MTV-PSMA cut-off 40 mL).ResultsThe SUVmax and SUVmean were higher, and MTV-PSMA was lower in the BPL reconstructed images compared to the OSEM group, with a mean difference of 8.4 (17.5%), 0.7 (8.2%) and − 21.5 mL (-3.4%), respectively. There was a strong correlation between the calculated SUVmax, SUVmean, and MTV-PSMA values in the OSEM and BPL reconstructed images (Pearson r values of 0.98, 0.99, and 1.0, respectively). No patients were reclassified from low to high volume disease or vice versa when switching from OSEM to BPL reconstruction.Conclusions[68Ga]Ga-PSMA-11 PET/CT quantitative and volumetric parameters produced by BPL and OSEM reconstruction methods are strongly correlated. Differences are proportional and small for SUVmean, which is used as a predictive biomarker. Our study suggests that both reconstruction methods are acceptable without clinical impact on quantitative or volumetric findings. For longitudinal comparison, committing to the same reconstruction method would be preferred to ensure consistency.

PET/CT Reconstruction and Its Impact on [Measures of] Metabolic Tumor Volume

In oncological imaging, the use of metabolic tumor volume (MTV) for further prognostic differentiation and the development of risk adapted strategies appears promising. The aim of this analysis was to evaluate ultra-high definition (UHD) and ordered subset expectation maximization (OSEM) PET/CT reconstructions for their potential impact on different methods of MTV measurement. We analyzed positron emission tomography combined with computed tomography (PET/CT) scans of 40 Hodgkin lymphoma patients before first-line treatment who had undergone fluorodeoxyglucose (FDG) PET/CT. The MTVs were determined taking an SUV of 4.0 (MTV4.0) as a fixed threshold or 41% of the single hottest voxel (MTV41%) as an adaptive threshold for automated lymphoma delineation in both UHD and OSEM reconstructions. We then compared the absolute and relative differences between MTV4.0 and MTV41% in UHD and OSEM reconstructions. The relative distribution of MTV4.0 and MTV41% in relation to the reconstruction method applied was recorded and respective differences were tested for statistical significance using the paired sample t-test. A comparison of MTV4.0 and MTV41% showed smaller relative and absolute differences in MTV between different reconstruction settings for the MTV4.0 method. Conversely, the absolute as well as the relative differences between MTVs obtained from different reconstructions settings were significantly greater when the MTV41% method was applied (p<0001). MTV4.0 brings higher robustness between different reconstruction settings, while with MTV41% the deviation between volumes obtained with different reconstruction settings is greater. For clinical routine and for multicenter settings, the MTV4.0 therefore appears most promising.

Reduced count pediatric whole-body 18F-FDG PET imaging reconstruction with a Bayesian penalized likelihood algorithm.

Advanced positron emission tomography (PET) image reconstruction methods promise to allow optimized PET/CT protocols with improved image quality,decreased administered activity and/or acquisition times. To evaluate the impact of reducing counts(simulating reduced acquisition time) in block sequential regularized expectation maximization (BSREM) reconstructed pediatric whole-body 18F-fluorodeoxyglucose (FDG) PET images, and to compare BSERM with ordered-subset expectation maximization (OSEM) reconstructed reduced-count images. Twenty children (16 male) underwent clinical whole-body 18F-FDG PET/CT examinations using a 25-cm axial field-of-view (FOV) digital PET/CT system at 90s per bed (s/bed) with BSREM reconstruction (β=700). Reduced count simulations with varied BSREM β levels were generated from list-mode data: 60s/bed, β=800; 50s/bed, β=900; 40s/bed, β=1000; and 30s/bed, β=1300. In addition, a single OSEM reconstruction was created at 60s/bed based on prior literature. Qualitative (Likert scores) and quantitative (standardized uptake value [SUV]) analyses were performed to evaluate image quality and quantitation across simulated reconstructions. The mean patient age was 9.0 ± 5.5 (SD) years, mean weight was 38.5 ± 24.5kg, and mean administered 18F-FDG activity was 4.5 ± 0.7 (SD) MBq/kg. Between BSREM reconstructions, no qualitative measure showed a significant difference versus the 90s/bed β=700 standard (all P>0.05). SUVmax values for lesions were significantly lower from 90s/bed, β=700 only at a simulated acquisition time of 30s/bed, β=1300 (P=0.001). In a side-by-side comparison of BSREM versus OSEM reconstructions, 40s/bed, β=1000 images were generally preferred over 60s/bed TOF OSEM images. In children who undergo whole-body 18F-FDG PET/CT on a 25-cm FOV digital PET/CT scanner, reductions in acquisition time or, by corollary, administered radiopharmaceutical activity of >50% from a clinical standard of 90s/bed may be possible while maintaining diagnostic quality when a BSREM reconstruction algorithm is used.

Evaluation of image resolution and quantification parameters on fdg-pet/ct images in patients with metastatic breast cancer using Q. clear and osem reconstruction techniques

We compared the 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in a prospective setting using the ordered subset expectation maximization (OSEM) algorithm and the bayesian penalized likelihood reconstruction algorithm (Q.Clear) and the image quality and quantification parameters. 35 patients with metastatic breast cancer who were treated and followed up with 2-[18F]FDG-PET/CT were included. A total of 150 scans were evaluated on a five-point scale for the image quality parameters of noise, sharpness, contrast, diagnostic confidence, artefact, and blotchy look while being blinded to the Q.Clear and OSEM reconstruction algorithms. In scans with detectable disease, the lesion with the highest volume of interest was chosen, taking into account both reconstruction techniques' interest levels. For the same heated lesion, SULpeak (g/mL) and SUVmax (g/mL) were contrasted. The OSEM reconstruction had significantly less blotchy appearance than the Q.Clear reconstruction, while there was no significant difference between the two methods in terms of noise, diagnostic confidence, or artefact. Q.Clear had significantly better sharpness (p &amp;#60; 0.002) and contrast (p &amp;#60; 0.002) than the OSEM reconstruction. Quantitative examination of 75/150 scans revealed that Q.Clear reconstruction considerably outperformed OSEM reconstruction in terms of SULpeak (6.33 ± 1.8 vs. 5.85 ± 1.5, p &amp;#60; 0.002) and SUVmax (7.27 ± 5.8 vs. 3.90 ± 2.8, p 0.002). In conclusion, OSEM reconstruction was less blotchy, but Q.Clear reconstruction showed superior sharpness, better contrast, higher SUVmax, and higher SULpeak.

A proper protocol for routine 18F-FDG uEXPLORER total-body PET/CT scans

BackgroundConventional clinical PET scanners typically have an axial field of view (AFOV) of 15–30 cm, resulting in limited coverage and relatively low photon detection efficiency. Taking advantage of the development of long-axial PET/CT, the uEXPLORER PET/CT scanner with an axial coverage of 194 cm increases the effective count rate by approximately 40 times compared to that of conventional PET scanners. Ordered subset expectation maximization (OSEM) is the most widely used iterative algorithm in PET. The major drawback of OSEM is that the iteration process must be stopped before convergence to avoid image degradation due to excessive noise. A new Bayesian penalized-likelihood iterative PET reconstruction, named HYPER iterative, was developed and is now available on the uEXPLORER total-body PET/CT, which incorporates a noise control component by using a penalty function in each iteration and finds the maximum likelihood solution through repeated iterations. To date, its impact on lesion visibility in patients with a full injected dose or half injected dose is unclear. The goal of this study was to determine a proper protocol for routine 18F-FDG uEXPLORER total-body PET/CT scans.ResultsThe uEXPLORER total-body PET/CT images reconstructed using both OSEM and HYPER iterative algorithms of 20 tumour patients were retrospectively reviewed. The quality of the 5 min PET image was excellent (score 5) for all of the dose and reconstruction methods. Using the HYPER iterative method, the PET images reached excellent quality at 1 min with full-dose PET and at 2 min with half-dose PET. The PET image reached a similar excellent quality at 2 min with a full dose and at 3 min with a half dose using OSEM. The noise in the OSEM reconstruction was higher than that in the HYPER iterative. Compared to OSEM, the HYPER iterative had a slightly higher SUVmax and TBR of the lesions for large positive lesions (≥ 2 cm) (SUVmax: up to 9.03% higher in full dose and up to 12.52% higher in half dose; TBR: up to 8.69% higher in full dose and up to 23.39% higher in half dose). For small positive lesions (≤ 10 mm), the HYPER iterative had an obviously higher SUVmax and TBR of the lesions (SUVmax: up to 45.21% higher in full dose and up to 74.96% higher in half dose; TBR: up to 44.91% higher in full dose and up to 93.73% higher in half dose).ConclusionsA 1 min scan with a full dose and a 2 min scan with a half dose are optimal for clinical diagnosis using the HYPER iterative and 2 min and 3 min for OSEM. For quantification of the small lesions, HYPER iterative reconstruction is preferred.

Improved [68Ga]Ga-PSMA-11 image qualities reconstructed by total variation regularized expectation maximization on total-body PET/CT.

Total variation regularized expectation maximization (TVREM) reconstruction algorithm on the image quality of gallium (68GA) prostate-specific membrane antigen-11 ([68Ga]Ga-PSMA-11) total-body positron emission tomography/computed tomography (PET/CT). Images of a phantom with small hot sphere inserts and the total-body PET/CT scans of 51 prostate cancer patients undergoing [68Ga]Ga-PSMA-11 were reconstructed using TVREM with 5 different penalization factors between 0.09 and 0.45 and for 20-, 40-, 60-, 120-, and 300-second acquisition, respectively. As a comparison, the same data were also reconstructed using the ordered subset expectation maximization (OSEM) with 3 iterations, 20 subsets, and 300 second acquisition. The contrast recovery coefficients (CRC) and background variability (BV) of the phantom, the tumor-to-background ratios (TBR), the contrast recovery (CR) ratio, the image noise of the liver, and maximum standard uptake value (SUVmax) of the lesions were calculated to evaluate the image quality. The clinical performance of the images was evaluated by 2 radiologists with a 5-point scale (1-poor, 5-excellent). The TVREM reconstructions groups fwith 120 second acquisition and the penalization of 0.27 to 0.45 showed the best performance in terms of CR, TBR, image noise, and the gain of SUVmax compared to that obtained in the OSEM 300 second group. Even the image noise of the TVREM 120 second group with a penalization factor of 0.27 and 0.36 was comparable to the OSEM 300 second group; the lesions' SUVmax increased by 28% whereas the image noise decreased by 5% and 14%, respectively. The TVREM 120 second group with a penalization factor of 0.36 (5.00±0.00) had the highest qualitative score that equaled OSEM and TVREM for the 300 second (P>0.05) group. Our study has shown the potential of the TVREM reconstruction algorithm with optimized penalization factors to achieve comparable [68Ga]Ga-PSMA-11 total-body PET/CT image quality with a shorter acquisition time, compared with the conventional OSEM reconstruction algorithm.

Evaluation of reconstruction methods and image noise levels concerning visual assessment of simulated liver lesions in 111In-octreotide SPECT imaging

BackgroundEarly cancer detection is crucial for patients’ survival. The image quality in 111In-octreotide SPECT imaging could be improved by using Monte Carlo (MC)-based reconstruction. The aim of this observational study was to determine the detection rate of simulated liver lesions for MC-based ordered subset expectation maximization (OSEM) reconstruction compared to conventional attenuation-corrected OSEM reconstruction.MethodsThirty-seven SPECT/CT examinations with 111In-octreotide were randomly selected. The inclusion criterion was no liver lesions at the time of examination and for the following 3 years. SPECT images of spheres representing lesions were simulated using MC. The raw data of the spheres were added to the raw data of the established healthy patients in 26 of the examinations, and the remaining 11 examinations were not modified. The images were reconstructed using conventional OSEM reconstruction with attenuation correction and post filtering (fAC OSEM) and MC-based OSEM reconstruction without and with post filtering (MC OSEM and fMC OSEM, respectively). The images were visually and blindly evaluated by a nuclear medicine specialist. The criteria evaluated were liver lesion yes or no, including coordinates if yes, with confidence level 1–3. The percentage of detected lesions and accuracy (percentage of correctly classified cases), as well as tumor-to-normal tissue concentration (TNC) ratios and signal-to-noise ratios (SNRs), were evaluated.ResultsThe detection rates were 30.8% for fAC OSEM, 42.3% for fMC OSEM, and 50.0% for MC OSEM. The accuracies were 45.9% for fAC OSEM, 45.9% for fMC OSEM, and 54.1% for MC OSEM. The number of false positives was higher for fMC and MC OSEM. The observer’s confidence level was higher in filtered images than in unfiltered images. TNC ratios were significantly higher, statistically, with MC OSEM and fMC OSEM than with AC OSEM, but SNRs were similar due to higher noise with MC OSEM.ConclusionOne in two lesions were found using MC OSEM versus one in three using conventional reconstruction. TNC ratios were significantly improved, statistically, using MC-based reconstruction, but the noise levels increased and consequently the confidence level of the observer decreased. For further improvements, image noise needs to be suppressed.

Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer.

We compared the image quality and quantification parameters through bayesian penalized likelihood reconstruction algorithm (Q.Clear) and ordered subset expectation maximization (OSEM) algorithm for 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in prospective setting. We included 37 metastatic breast cancer patients diagnosed and monitored with 2-[18F]FDG-PET/CT at Odense University Hospital (Denmark). A total of 100 scans were analyzed blinded toward Q.Clear and OSEM reconstruction algorithms regarding image quality parameters (noise, sharpness, contrast, diagnostic confidence, artefacts, and blotchy appearance) using a five-point scale. The hottest lesion was selected in scans with measurable disease, considering the same volume of interest in both reconstruction methods. SULpeak (g/mL) and SUVmax (g/mL) were compared for the same hottest lesion. There was no significant difference regarding noise, diagnostic confidence, and artefacts within reconstruction methods; Q.Clear had significantly better sharpness (p < 0.001) and contrast (p = 0.001) than the OSEM reconstruction, while the OSEM reconstruction had significantly less blotchy appearance compared with Q.Clear reconstruction (p < 0.001). Quantitative analysis on 75/100 scans indicated that Q.Clear reconstruction had significantly higher SULpeak (5.33 ± 2.8 vs. 4.85 ± 2.5, p < 0.001) and SUVmax (8.27 ± 4.8 vs. 6.90 ± 3.8, p < 0.001) compared with OSEM reconstruction. In conclusion, Q.Clear reconstruction revealed better sharpness, better contrast, higher SUVmax, and higher SULpeak, while OSEM reconstruction had less blotchy appearance.

Q.Clear reconstruction for reducing the scanning time for 68Ga-DOTA-FAPI-04 PET/MR imaging.

This study aims to determine whether Q.Clear positron emission tomography (PET) reconstruction may reduce tracer injection dose or shorten scanning time in 68Gallium-labelled fibroblast activation protein inhibitor (68Ga-FAPI) PET/magnetic resonance (MR) imaging. We retrospectively collected cases of 68Ga-FAPI whole-body imaging performed on integrated PET/MR. PET images were reconstructed using three different methods: ordered subset expectation maximization (OSEM) reconstruction with full scanning time, OSEM reconstruction with half scanning time, and Q.Clear reconstruction with half scanning time. We then measured standardized uptake values (SUVs) within and around lesions, alongside their volumes. We also evaluated image quality using lesion-to-background (L/B) ratio and signal-to-noise ratio (SNR). We then compared these metrics across the three reconstruction techniques using statistical methods. Q.Clear reconstruction significantly increased SUVmax and SUVmean within lesions (more than 30%) and reduced their volumes in comparison with OSEM reconstruction. Background SUVmax also increased significantly, while background SUVmean showed no difference. Average L/B values for Q.Clear reconstruction were only marginally higher than those from OSME reconstruction with half-time. SNR decreased significantly in Q.Clear reconstruction compared with OSEM reconstruction with full time (but not half time). Differences between Q.Clear and OSEM reconstructions in SUVmax and SUVmean values within lesions were significantly correlated with SUVs within lesions. Q.Clear reconstruction was useful for reducing PET injection dose or scanning time while maintaining the image quality. Q.Clear may affect PET quantification, and it is necessary to establish diagnostic recommendations based on Q.Clear results for Q.Clear application.

Experimental study of noise level optimization in brain single-photon emission computed tomography images using non-local means approach with various reconstruction methods

The noise reduction algorithm using the non-local means (NLM) approach is very efficient in nuclear medicine imaging. In this study, the applicability of the NLM noise reduction algorithm in single-photon emission computed tomography (SPECT) images with a brain phantom and the optimization of the NLM algorithm by changing the smoothing factors according to various reconstruction methods are investigated. Brain phantom images were reconstructed using filtered back projection (FBP) and ordered subset expectation maximization (OSEM). The smoothing factor of the NLM noise reduction algorithm determined the optimal coefficient of variation (COV) and contrast-to-noise ratio (CNR) results at a value of 0.020 in the FBP and OSEM reconstruction methods. We confirmed that the FBP- and OSEM-based SPECT images using the algorithm applied with the optimal smoothing factor improved the COV and CNR by 66.94% and 8.00% on average, respectively, compared to those of the original image. In conclusion, an optimized smoothing factor was derived from the NLM approach-based algorithm in brain SPECT images and may be applicable to various nuclear medicine imaging techniques in the future.

Bayesian penalized likelihood PET reconstruction impact on quantitative metrics in diffuse large B-cell lymphoma.

Evaluate the quantitative, subjective (Deauville score [DS]) and reader agreement differences between standard ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL) positron emission tomography (PET) reconstruction methods. A retrospective review of 104 F-18 fluorodeoxyglucose PET/computed tomography (CT) exams among 52 patients with diffuse large B-cell lymphoma. An unblinded radiologist moderator reviewed both BPL and OSEM PET/CT exams. Four blinded radiologists then reviewed the annotated cases to provide a visual DS for each annotated lesion. Significant (P < .001) differences in BPL and OSEM PET methods were identified with greater standard uptake value (SUV) maximum and SUV mean for BPL. The DS was altered in 25% of cases when BPL and OSEM were reviewed by the same radiologist. Interobserver DS agreement was higher for OSEM (>1 cm lesion = 0.89 and ≤1 cm lesion = 0.84) compared to BPL (>1 cm lesion = 0.85 and ≤1 cm lesion = 0.81). Among the 4 readers, average intraobserver visual DS agreement between OSEM and BPL was 0.67 for lesions >1cm and 0.4 for lesions ≤1 cm. F-18 Fluorodeoxyglucose PET/CT of diffuse large B-cell lymphoma reconstructed with BPL has higher SUV values, altered DSs and reader agreement when compared to OSEM. This report finds volumetric PET measurements such as metabolic tumor volume to be similar between BPL and OSEM PET reconstructions. Efforts such as adoption of European Association Research Ltd accreditation should be made to harmonize PET data with an aim at balancing the need for harmonization and sensitivity for lesion detection.

Development of scalable lymphatic system in the 4D XCAT phantom: Application to quantitative evaluation of lymphoma PET segmentations.

Digital anthropomorphic phantoms, such as the 4D extended cardiac-torso (XCAT) phantom, are actively used to develop, optimize, and evaluate a variety of imaging applications, allowing for realistic patient modeling and knowledge of ground truth. The XCAT phantom defines the activity and attenuation for a simulated patient, which includes a complete set of organs, muscle, bone, and soft tissue, while also accounting for cardiac and respiratory motion. However, the XCAT phantom does not currently include the lymphatic system, critical for evaluating medical imaging tasks such as sentinel node detection, node density measurement, and radiation dosimetry. In this study, we aimed to develop a scalable lymphatic system in the XCAT phantom, to facilitate improved research of the lymphatic system in medical imaging. Using this scalable lymphatic system, we modeled the lymph node conglomerate pathology that is characteristically observed in primary mediastinal B-cell lymphoma (PMBCL). As an extended application, we evaluated positron emission tomography (PET) image quantification of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of these simulated lymphomas, though the phantoms may be applied to other imaging modalities and study design paradigms (e.g., image quality, detection). A template model for the lymphatic system was developed based on anatomical data from the Visible Human Project of the National Library of Medicine. The segmented nodes and vessels were fit with non-uniform rational basis spline surfaces, and multichannel large deformation diffeomorphic metric mapping was used to propagate the template to different XCAT anatomies. To model conglomerates observed in PMBCL, lymph nodes were enlarged, converged within the mediastinum, and tracer concentration was increased. We used the phantoms as inputs to a PET simulation tool, which generated images using ordered subsets expectation maximization reconstruction with 2-8mm Gaussian filters. Fixed thresholding (FT) and gradient segmentation were used to determine MTV and TLG. Percent bias (%Bias) and coefficient of variation (COV) were computed as measures of accuracy and precision, respectively, for each MTV and TLG measurement. Using the methodology described above, we introduced a scalable lymphatic system in the XCAT phantom, which allows for the radioactivity and attenuation ground truth to be generated in 116±2.5s using a 2.3GHz processor. Within the Rhinoceros interface, lymph node anatomy and function were modified to create a cohort of 10 phantoms with lymph node conglomerates. Using the lymphoma phantoms to evaluate PET quantification of MTV, mean %Bias values were -9.3%, -41.3%, and 20.9%, while COV values were 4.08%, 7.6%, and 3.4% using 25% FT, 40% FT, and gradient segmentations, respectively. Comparatively for TLG, mean %Bias values were -27.4%, -45.8%, and -16.0%, while COV values were 1.9%, 5.7%, and 1.4%, for the 25% FT, 40% FT, and gradient segmentations, respectively. In this work, we upgraded the XCAT phantom to include a lymphatic system, comprised of a network of 276 scalable lymph nodes and corresponding vessels. As an application, we created a cohort of phantoms with lymph node conglomerates to evaluate lymphoma quantification in PET imaging, which highlights an important application of this work.

Total variation regularized expectation maximization reconstruction improves 68Ga-FAPI-04 PET/CT image quality as compared to ordered subset expectation maximization reconstruction.

This study aimed to investigate improvements in 68Ga-FAPI PET/CT image quality due to using total variation regularized expectation maximization (TVR-EM) and ordered subset expectation maximization (OS-EM) reconstruction. Data from a total of 24 patients were retrospectively analyzed in this study. Positron emission tomography (PET) images were reconstructed using OS-EM and TVR-EM for 2 and 3 minutes-per-bed (min/bed) acquisition. The SUV<inf>mean</inf> of a region-of-interest on the liver, image noise, signal-to-noise ratios (SNR), the SUV<inf>max</inf> of the lesions and the tumour-to-background ratios (TBR) were measured and compared between the 2 methods. Subjective image qualities were evaluated by two experienced radiologists using a 5-point score scale (5-excellent, 1-poor). In total, 132 lesions were analyzed. The image noise in TVR-EM reconstruction groups was lower than in the OS-EM groups (all P<0.05). The SNR, SUV<inf>max</inf> of lesions and the TBR were higher for the TVR-EM reconstruction groups compared to OS-EM groups (all P<0.05). Also, the SUV<inf>max</inf> of the lesions in the TVR-EM groups increased by at least 12% compared to OS-EM 3 min/bed group. The SUV<inf>max</inf> for small (<10 mm) and large lesions (>10 mm) in the TVR-EM 2 min/bed group were significantly larger compared to the OS-EM 3 min/bed groups (all P<0.05). The highest image quality score resulted from the TVR-EM 3 min/bed group with a penalization factor of 0.25 (3.92±0.19). TVR-EM reduces image noise and improved the SNR, SUV<inf>max</inf> and TBR of the lesions. It also enables fast acquisition without compromising image quality compared to standard OS-EM.

Impact of Bayesian penalized likelihood reconstruction on quantitative and qualitative aspects for pulmonary nodule detection in digital 2-[18F]FDG-PET/CT

To evaluate the impact of block sequential regularized expectation maximization (BSREM) reconstruction on quantitative and qualitative aspects of 2-[18F]FDG-avid pulmonary nodules compared to conventional ordered subset expectation maximization (OSEM) reconstruction method. Ninety-one patients with 144 2-[18F]FDG-avid pulmonary nodules (all ≤ 20 mm) undergoing PET/CT for oncological (re-)staging were retrospectively included. Quantitative parameters in BSREM and OSEM (including point spread function modelling) were measured, including maximum standardized uptake value (SUVmax). Nodule conspicuity in BSREM and OSEM images was evaluated by two readers. Wilcoxon matched pairs signed-rank test was used to compare quantitative and qualitative parameters in BSREM and OSEM. Pulmonary nodule SUVmax was significantly higher in BSREM images compared to OSEM images [BSREM 5.4 (1.2–20.7), OSEM 3.6 (0.7–17.4); p = 0.0001]. In a size-based analysis, the relative increase in SUVmax was more pronounced in smaller nodules (≤ 7 mm) as compared to larger nodules (8–10 mm, or > 10 mm). Lesion conspicuity was higher in BSREM than in OSEM (p < 0.0001). BSREM reconstruction results in a significant increase in SUVmax and a significantly improved conspicuity of small 2-[18F]FDG-avid pulmonary nodules compared to OSEM reconstruction. Digital 2-[18F]FDG-PET/CT reading may be enhanced with BSREM as small lesion conspicuity is improved.

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network.

Dual respiratory-cardiac gating reduces respiratory and cardiac motion blur in myocardial perfusion single-photon emission computed tomography (MP-SPECT). However, image noise is increased as detected counts are reduced in each dual gate (DG). We aim to develop a denoising method for dual gating MP-SPECT images using a 3D conditional generative adversarial network (cGAN). Twenty extended cardiac-torso phantoms with various 99m Tc-sestamibi distributions, defect characteristics, and body and organ sizes were used in the simulation, modeling six respiratory and eight cardiac gates (CGs), that is, 48 DGs for ordered subset expectation maximization reconstruction. Twenty clinical 99m Tc-sestamibi SPECT/CT datasets were re-binned into 7 respiratory gates and 8 CGs, that is, 56 DGs for maximum likelihood expectation maximization reconstruction. We evaluated the use of (i) phantoms' own datasets (patient-specific denoising [PD]) or other phantoms' datasets (cross-patient denoising) for training; (ii) the CG or the static (non-gated [NG]) data as the training references for cGAN; and (iii) cGAN as compared to conventional 3D post-reconstruction filtering, cardiac gating methods, and convolutional neural network. Normalized mean squared error, noise as assessed by normalized standard deviation, spatial blurring measured as the full-width-at-half-maximum of left ventricular wall, ejection fraction, joint correlation histogram, and defect size were analyzed as metrics of image quality. Training using patients' own dataset is superior to conventional training based on other patients' data. Using CG image as training reference provides a better trade-off in terms of noise and image blur as compared to the use of NG. cGAN-CG-PD provides superior performance as compared to other denoising methods for all physical and diagnostic indices evaluated in both simulation and clinical studies. cGAN denoising is promising for dual gating MP-SPECT based on the metrics mentioned earlier.

Exploration of the total-body PET/CT reconstruction protocol with ultra-low 18F-FDG activity over a wide range of patient body mass indices

PurposeThe purpose of this study was to investigate the image quality and diagnostic performance of different reconstructions over a wide range of patient body mass indices (BMIs) obtained by total-body PET/CT with ultra-low 18F-FDG activity (0.37 MBq/kg).MethodsA total of 63 patients who underwent total-body PET/CT with ultra-low activity (0.37 MBq/kg) 18F-FDG were enrolled. Patients were grouped by their BMIs. Images were reconstructed with the following two algorithms: the ordered subset expectation maximization (OSEM) algorithm (2, 3 iterations), both with time of flight (TOF) and point spread function (PSF) corrections (hereinafter referred as OSEM2, OSEM3) and HYPER Iterative algorithm (β-values of 0.3, 0.4, 0.5, 0.6) embedded TOF and PSF technologies (hereinafter referred as HYPER0.3, HYPER0.4, HYPER0.5 and HYPER0.6, respectively). Subjective image quality was assessed by two experienced nuclear medicine physicians according to the Likert quintile, including overall image quality, image noise and lesion conspicuity. The standard deviation (SD) and signal-to-noise ratio (SNR) of the liver, and maximum standard uptake value (SUVmax), peak standard uptake value (SUVpeak), tumour background ratio (T/N) and the largest diameter of lesions were quantitatively analysed by a third reader who did not participate in the subjective image assessment.ResultsIncreased noise was associated with increased BMI in all reconstruction groups. Significant differences occurred in the liver SNR among BMI categories of OSEM reconstructions (P < 0.001) but no difference was seen in the HYPER Iterative reconstructions between any of the BMI categories (P > 0.05). With the increase in BMI, overall image quality and image noise scores decreased significantly in all reconstructions, but there was no statistically significant difference of lesion conspicuity. The overall image quality score of the obese group was not qualified (score = 2.7) in OSEM3, while the others were qualified. The lesion conspicuity scores were significantly higher in HYPER Iterative reconstructions and lower in OSEM2 than in OSEM3 (all P < 0.05). The values of SUVmax, SUVpeak and T/N in HYPER0.3, HYPER0.4 and HYPER0.5 were higher than those in OSEM3. In different reconstructions, there was a correlation between lesion size (median, 1.55 cm; range, 0.7–11.0 cm) and SUVpeak variation rate compared to OSEM3 (r = 0.388, − 0.515, − 0.495, − 0.464, and − 0.423, respectively, and all P < 0.001).ConclusionConsidering the image quality and lesion analysis in 18F-FDG total-body PET/CT with ultra-low activity injection, OSEM reconstructions with 3 iterations meet the clinical requirements in patients with BMI < 30. In patients with BMI ≥ 30, it is recommended that the HYPER Iterative algorithm (β-value of 0.3–0.5) be used to ensure consistent visual image quality and quantitative assessment.

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [11C]PHNO brain PET-MR studies

IntroductionQ.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR.MethodsSix [11C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100–1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BPND) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland–Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons.ResultsWhen comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43).ConclusionBPND results obtained from quantitative low count brain PET studies using [11C]PHNO and reconstructed with Q.Clear with β < 400, which is the value used for clinical [18F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM.

Impact of reconstruction parameters on spatial resolution and its comparison between cadmium-zinc-telluride SPECT/CT and conventional SPECT/CT.

To evaluate the impact of reconstruction parameters on the spatial resolution of the tomographic image in single photon emission computed tomography (SPECT)/computed tomography (CT), and compare spatial resolution between a new polyvalent whole-body Cadmium-Zinc-Telluride camera (CZT-SPECT/CT) and a conventional dual-head Anger camera (conventional SPECT/CT). Spatial resolution was evaluated with four-line sources filled with 99mTc in tomographic images reconstructed by varying reconstruction parameters. Ordered-subset expectation maximization (OSEM) algorithm was performed with varying iterations (1-20), the number of subsets was fixed at 10. Butterworth filter, Gauss filter and no-filter were selected, respectively. Computed tomography-based attenuation correction (CTAC), scatter correction, resolution recovery and no correction (NC) were adopted for image correction. Filtered back projection (FBP) with Butterworth filter and CTAC was performed in image reconstruction. Spatial resolution was expressed by the full width at half-maximum (FWHM) value. The impact of reconstruction parameters on the spatial resolution was identical in both cameras: FWHM values decreased with the increase of iterations and converged uniformly when the number of iterations was over 4. FWHM values decreased with the increase of cutoff frequency of the Butterworth filter and increased with the increase of the Gauss filter. scatter correction and resolution recovery improved spatial resolution, whereas CTAC had a negligible effect on spatial resolution when reconstructed by OSEM. FWHM was generally lower with OSEM reconstruction than FBP reconstruction. On the whole, under the same reconstruction conditions, CZT-SPECT/CT had a lower FWHM value than conventional SPECT/CT. The spatial resolution was improved with the increase of iterations. Increasing the cutoff frequency of the Butterworth filter and decreasing the Gauss filter enhanced spatial resolution. The spatial resolution was better reconstructed by OSEM associated with attenuation correction, scatter correction and resolution recovery than FBP. CZT-SPECT/CT had better spatial resolution than conventional SPECT/CT.

Feasibility of MMWF noise reduction algorithm in brain SPECT images according to various reconstruction methods: A phantom study

In order to improve image quality in single-photon emission computed tomography (SPECT)/computed tomography (CT), various studies focusing on reconstruction methods and noise reduction algorithms have been conducted. Therefore, the purpose of this study was to evaluate the efficiency of the filtered back projection (FBP) and ordered subset expectation maximization (OSEM) reconstruction methods, and to evaluate image quality after applying the median modified Wiener filter (MMWF) as a complementary noise reduction filter using 3 × 3 and 5 × 5 mask sizes. The 3D Hoffman brain phantom was used, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were evaluated in regions of interest for quantitative analysis. Based on the results, the image quality obtained by the OSEM reconstruction method was 37.7% and 25.9% higher than that of the FBP reconstruction method regarding SNR and CNR, respectively. In addition, the average SNR and CNR values obtained by applying the MMWF algorithm were 35.9% and 17.1% higher than those without the MMWF algorithm. Therefore, our results indicate that the OSEM reconstruction method is more efficient than the FBP reconstruction method, and that the MMWF algorithm improves image quality in SPECT images, regardless of which reconstruction method is used.