Effects of injected dose, BMI and scanner type on NECR and image noise in PET imaging

Molecular imaging of arterial fibroblast activation protein: association with calcified plaque burden and cardiovascular risk factors

PURPOSE

We aimed to assess prevalence, distribution, and intensity of in-vivo arterial wall fibroblast activation protein (FAP) uptake, and its association with calcified plaque burden, cardiovascular risk factors (CVRFs), and FAP-avid tumor burden.

METHODS

We analyzed 69 oncologic patients who underwent [68 Ga]Ga-FAPI-04 PET/CT. Arterial wall FAP inhibitor (FAPI) uptake in major vessel segments was evaluated. We then investigated the associations of arterial wall uptake with calcified plaque burden (including number of plaques, plaque thickness, and calcification circumference), CVRFs, FAP-positive total tumor burden, and image noise (coefficient of variation, from normal liver parenchyma).

RESULTS

High focal arterial FAPI uptake (FAPI +) was recorded in 64/69 (92.8%) scans in 800 sites, of which 377 (47.1%) exhibited concordant vessel wall calcification. The number of FAPI + sites per patient and (FAPI +)-derived target-to-background ratio (TBR) correlated significantly with the number of calcified plaques (FAPI + number: r = 0.45, P < 0.01; TBR: r =  - 0.26, P = 0.04), calcified plaque thickness (FAPI + number: r = 0.33, P < 0.01; TBR: r =  - 0.29, P = 0.02), and calcification circumference (FAPI + number: r = 0.34, P < 0.01; TBR: r =  - 0.26, P = 0.04). In univariate analysis, only body mass index was significantly associated with the number of FAPI + sites (OR 1.06; 95% CI, 1.02 - 1.12, P < 0.01). The numbers of FAPI + sites and FAPI + TBR, however, were not associated with other investigated CVRFs in univariate and multivariate regression analyses. Image noise, however, showed significant correlations with FAPI + TBR (r = 0.30) and the number of FAPI + sites (r = 0.28; P = 0.02, respectively). In addition, there was no significant interaction between FAP-positive tumor burden and arterial wall FAPI uptake (P ≥ 0.13).

CONCLUSION

[68 Ga]Ga-FAPI-04 PET identifies arterial wall lesions and is linked to marked calcification and overall calcified plaque burden, but is not consistently associated with cardiovascular risk. Apparent wall uptake may be partially explained by image noise.

PET Image Denoising using a Deep-Learning Method for Extremely Obese Patients

The image quality in clinical PET scan can be severely degraded due to high noise levels in extremely obese patients. Our work aimed to reduce the noise in clinical PET images of extremely obese subjects to the noise level of lean subject images, to ensure consistent imaging quality. The noise level was measured by normalized standard deviation (NSTD) derived from a liver region of interest. A deep learning-based noise reduction method with a fully 3D patch-based U-Net was used. Two U-Nets, U-Nets A and B, were trained on datasets with 40% and 10% count levels derived from 100 lean subjects, respectively. The clinical PET images of 10 extremely obese subjects were denoised using the two U-Nets. The results showed the noise levels of the images with 40% counts of lean subjects were consistent with those of the extremely obese subjects. U-Net A effectively reduced the noise in the images of the extremely obese patients while preserving the fine structures. The liver NSTD improved from 0.13±0.04 to 0.08±0.03 after noise reduction (p = 0.01). After denoising, the image noise level of extremely obese subjects was similar to that of lean subjects, in terms of liver NSTD (0.08±0.03 vs. 0.08±0.02, p = 0.74). In contrast, U-Net B over-smoothed the images of extremely obese patients, resulting in blurred fine structures. In a pilot reader study comparing extremely obese patients without and with U-Net A, the difference was not significant. In conclusion, the U-Net trained by datasets from lean subjects with matched count level can provide promising denoising performance for extremely obese subjects while maintaining image resolution, though further clinical evaluation is needed.

Relating18F-FDG image signal-to-noise ratio to time-of-flight noise-equivalent count rate in total-body PET using the uEXPLORER scanner

Objective.This work assessed the relationship between image signal-to-noise ratio (SNR) and total-body noise-equivalent count rate (NECR)-for both non-time-of-flight (TOF) NECR and TOF-NECR-in a long uniform water cylinder and 14 healthy human subjects using the uEXPLORER total-body PET/CT scanner.Approach.A TOF-NEC expression was modified for list-mode PET data, and both the non-TOF NECR and TOF-NECR were compared using datasets from a long uniform water cylinder and 14 human subjects scanned up to 12 h after radiotracer injection.Main results.The TOF-NECR for the uniform water cylinder was found to be linearly proportional to the TOF-reconstructed image SNR²in the range of radioactivity concentrations studied, but not for non-TOF NECR as indicated by the reducedR²value. The results suggest that the use of TOF-NECR to estimate the count rate performance of TOF-enabled PET systems may be more appropriate for predicting the SNR of TOF-reconstructed images.Significance.Image quality in PET is commonly characterized by image SNR and, correspondingly, the NECR. While the use of NECR for predicting image quality in conventional PET systems is well-studied, the relationship between SNR and NECR has not been examined in detail in long axial field-of-view total-body PET systems, especially for human subjects. Furthermore, the current NEMA NU 2-2018 standard does not account for count rate performance gains due to TOF in the NECR evaluation. The relationship between image SNR and total-body NECR in long axial FOV PET was assessed for the first time using the uEXPLORER total-body PET/CT scanner.

Two birds with one stone: can [68Ga]Ga-DOTANOC PET/CT image quality be improved through BMI-adjusted injected activity without increasing acquisition times?

OBJECTIVES

To assess how patients' dependent parameters may affect [68Ga]Ga-DOTANOC image quality and to propose a theoretical body mass index (BMI)-adjusted injected activity (IA) scheme, to improve imaging of high weight patients.

METHODS

Among patients prospectively enrolled (June-2019 and May-2020) in an Institutional Ethical Committee-approved electronic archive, we included those affected by primary gastro-entero-pancreatic (GEP) or lung neuroendocrine tumour and referred by our Institutional clinicians (excluding even minimal radiopharmaceutical extravasation, movement artefacts, renal insufficiency). All PET/CT images were acquired following EANM guidelines and rated for visual quality (1 = non-diagnostic, 2 = poor, 3 = moderate, 4 = good). Collected data included patient's body mass, height, BMI, age, IA (injected activity), IA/Kg (IAkg), IA/BMI (IABMI), liver SUVmean, liver SUVmax standard deviation, liver-signal-to-noise (LSNR), normalised_LSNR (LSNR_norm) and contrast-to-noise ratio (CNR) for positive scans and were compared to image rating (poor vs moderate/good).

RESULTS

Overall, 77 patients were included. Rating concordance was high (agreement = 81.8%, Fleiss k score = 0.806). All patients' dependent parameters resulted significantly different between poor-rated and moderate/good-rated scans (IA: p = 0.006, IAkg: p =< 0.001, body weight: p =< 0.001, BMI: p =< 0.001, IABMI: p =< 0.001). Factors significantly associated with moderate/good rating were BMI (p =< 0.001), body weight (p =< 0.001), IABMI (p =< 0.001), IAkg (p = 0.001), IA (p = 0.003), LSNR_norm (p = 0.01). The BMI-based model presented the best predictive efficiency (81.82%). IABMI performance to differentiate moderate/good from poor rating resulted statistically significant (IA-AUC = 0.78; 95% CI: 0.68-0.89; cut-off value of 4.17 MBq*m2/kg, sensitivity = 81.1%, specificity = 66.7%). If BMI-adjusted IA (=4.17*BMI) would have been applied in this population, the median IA would have slightly inferior (-4.8%), despite a different IA in each patient.

ADVANCES IN KNOWLEDGE

BMI resulted the best predictor of image quality. The proposed theoretical BMI-adjusted IA scheme (4.17*BMI) should yield images of better quality (especially in high-BMI patients) maintaining practical scanning times (3 min/bed).

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

Purpose

The 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).

Methods

A 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 (SUV_max), peak standard uptake value (SUV_peak), 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.

Results

Increased 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 SUV_max, SUV_peak 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 SUV_peak variation rate compared to OSEM3 (r = 0.388, − 0.515, − 0.495, − 0.464, and − 0.423, respectively, and all P < 0.001).

Conclusion

Considering 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.

Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, such DNN methods focus on applications involving test data that match the statistical characteristics of the training data very closely and give little attention to evaluating the performance of these DNNs on new out-of-distribution (OOD) acquisitions. We propose a novel DNN formulation that models the (i) underlying sinogram-based physics of the PET imaging system and (ii) the uncertainty in the DNN output through the per-voxel heteroscedasticity of the residuals between the predicted and the high-quality reference images. Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. Results on in vivo simultaneous PET-MRI, and various forms of OOD data in PET-MRI, show the benefits of suDNN over the current state of the art, quantitatively and qualitatively.

Reducing Radiation Exposure to Paediatric Patients Undergoing [18F]FDG-PET/CT Imaging

PURPOSE

To investigate the possibility of reducing the injected activity for whole-body [18F]FDG-PET/CT studies of paediatric oncology patients and to assess the usefulness of time-of-flight (TOF) acquisition on PET image quality at reduced count levels.

PROCEDURES

Twenty-nine paediatric oncology patients (12F/17M, 3-18 years old (median age 13y), weight 45±20 kg, BMI 19±4 kg/m²), who underwent routine whole-body PET/CT examinations on a Siemens Biograph mCT TrueV system with TOF capability (555ps) were included in this study. The mean injected activity was 156 ± 45 MBq (3.8 ± 0.8 kg/MBq) and scaled to patient weight. The raw data was collected in listmode (LM) format and pre-processed to simulate reduced levels of [18F]FDG activity (75, 50, 35, 20 and 10% of the original counts) by randomly removing events from the original LM data. All data were reconstructed using the vendor-specific e7-tools with standard OSEM only, with OSEM plus resolution recovery (PSF). The reconstructions were repeated with added TOF (TOF) and PSF+TOF. The benefit of TOF together with the reduced count levels was evaluated by calculating the gains in signal-to-noise ratio (SNR) in the liver and contrast-to-noise ratio (CNR) in all PET-positive lesions before and after TOF employed at every simulated reduced count level. Finally, the PSF+TOF images at 50, 75 and 100% of counts were evaluated clinically on a 5-point scale by three nuclear medicine physicians.

RESULTS

The visual inspection of the reconstructed images did not reveal significant differences in image quality between 75 and 100% count levels for PSF+TOF. The improvements in SNR and CNR were the greatest for TOF reconstruction and PSF combined. Both SNR and CNR gains did increase linearly with the patients BMI for both OSEM only and PSF reconstruction. These benefits were observed until reducing the counts to 50 and 35% for SNR and CNR, respectively.

CONCLUSIONS

The benefit of using TOF was noticeable when using 50% or greater of the counts when evaluating the CNR and SNR. For [18F]FDG-PET/CT, whole-body paediatric imaging the injected activity can be reduced to 75% of the original dose without compromising PET image quality.

The clinical effectiveness of reconstructing 18F-sodium fluoride PET/CT bone using Bayesian penalized likelihood algorithm for evaluation of metastatic bone disease in obese patients

OBJECTIVE

A new Bayesian penalized likelihood reconstruction algorithm for positron emission tomography (PET) (Q.Clear) is now in clinical use for fludeoxyglucose (FDG) PET/CT. However, experience with non-FDG tracers and in special patient populations is limited. This pilot study aims to compare Q.Clear to standard PET reconstructions for ¹⁸F sodium fluoride (¹⁸F-NaF) PET in obese patients.

METHODS

30 whole body ¹⁸F-NaF PET/CT scans (10 patients with BMI 30-40 Kg/m² and 20 patients with BMI >40 Kg/m²) and a NEMA image quality phantom scans were analyzed using ordered subset expectation maximization (OSEM) and Q.Clear reconstructions methods with B400, 600, 800 and 1000. The images were assessed for overall image quality (IQ), noise level, background soft tissue, and lesion detectability, contrast recovery (CR), background variability (BV) and contrast-to-noise ratio (CNR) for both algorithms.

RESULTS

CNR for clinical cases was higher for Q.Clear than OSEM (p < 0.05). Mean CNR for OSEM was (21.62 ± 8.9), and for Q.Clear B400 (31.82 ± 14.6), B600 (35.54 ± 14.9), B800 (39.81 ± 16.1), and B1000 (40.9 ± 17.8). As the β value increased the CNR increased in all clinical cases. B600 was the preferred β value for reconstruction in obese patients. The phantom study showed Q.Clear reconstructions gave lower CR and lower BV than OSEM. The CNR for all spheres was significantly higher for Q.Clear (independent of β) than OSEM (p < 0.05), suggesting superiority of Q.Clear.

CONCLUSION

This pilot clinical study shows that Q.Clear reconstruction algorithm improves overall IQ of ¹⁸F-NaF PET in obese patients. Our clinical and phantom measurement results demonstrate improved CNR and reduced BV when using Q.Clear. A β value of 600 is preferred for reconstructing ¹⁸F-NaF PET/CT with Q.Clear in obese patients.

ADVANCES IN KNOWLEDGE

¹⁸F-NaF PET/CT is less susceptible to artifacts induced by body habitus. Bayesian penalized likelihood reconstruction with¹⁸F-NaF PET improves overall IQ in obese patients.

18F-NaF PET/CT of Obese Patients on a Lutetium-Yttrium Oxyorthosilicate PET/CT System: Patient Dosimetry, Optimization of Injected Activity, and Acquisition Time

¹⁸F-NaF PET/CT has a rapid single-pass extraction and fast clearance from soft tissues, resulting in a good target-to-background ratio. This study aimed to establish the optimum acquisition time and dosimetry for ¹⁸F-NaF PET/CT to evaluate bone metastases in obese patients. A secondary objective was to evaluate the impact of acquisition time on image quality, lesion detection rate, noise level, and radiation burden in this patient group. Methods: In total, 60 patients were included in the study (20 patients with a body mass index (BMI) of 30.0-34.9 kg/m², 20 with a BMI of 35-39.9 kg/m², and 20 with a BMI of >40 kg/m²). Images were acquired after intravenous injection of a 2.2 MBq/kg (0.06 mCi/kg) dose of ¹⁸F-NaF. Data were acquired in list mode using ordered-subset expectation maximization reconstruction. The raw data were rebinned to simulate scans with acquisition times of 2, 2.5, and 3 min per bed position. The scans were visually analyzed by 2 observers and scored by rank against a panel of parameters (overall image quality, noise level, background soft tissue, and lesion detectability), and the contrast-to-noise ratio (CNR) was calculated. Results: The mean CNR was 20.19 ± 8.39 for a 2-min acquisition, 21.03 ± 8.35 for 2.5 min, and 22.16 ± 8.37 for 3 min. There were no statistically significant differences in CNR among the 3 different acquisition durations (P > 0.05). Lesion delineation was excellent and independent of the acquisition time. All relevant lesions could be identified with all 3 acquisition times. A mean activity of 215.4 ± 31.3 MBq was injected, with estimated mean effective absorbed doses of 4.09 ± 0.59 mSv for ¹⁸F-NaF PET and 7.88 ± 1.66 mSv for CT alone. Conclusion: ¹⁸F-NaF PET/CT can be beneficial in obese patients because of its good pharmacokinetics. Optimal osseous staging can be achieved with relatively low doses and radiation burden. Lesion delineation was excellent regardless of acquisition time. However, it is recommended that an acquisition of 3 min per bed position be used in patients with a BMI of more than 40.

Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET

Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron emission tomography (PET). This work aimed to investigate the feasibility of noise reduction for tracers without sufficient training datasets using a deep transfer learning approach, which can utilize existing networks trained by the widely available FDG datasets. In this study, the deep transfer learning strategy based on a fully 3D patch-based U-Net was investigated on a 18F-fluoromisonidazole (18F-FMISO) dataset using single-bed scanning and a 68Ga-DOTATATE dataset using whole-body scanning. The datasets of 18F-FDG by single-bed scanning and whole-body scanning were used to obtain pre-trained U-Nets separately for subsequent cross-tracer and cross-protocol transfer learning. The full-dose PET images were used as the labels while low-dose PET images from 10% counts were used as the inputs. Three types of U-Nets were obtained: a U-Net trained by FDG dataset, a pre-trained FDG U-Net fine-tuned by another less-available tracer (FMISO/DOATATE), and a U-Net completely trained by a large number of less-available tracer datasets (FMISO/DOATATE), used as the reference U-Net. The denoising performance of the three types of U-Nets was evaluated on single-bed 18F-FMISO and whole-body 68Ga-DOTATATE separately and compared using normalized root-mean-square error (NRMSE), signal-to-noise ratio (SNR), and relative bias of region of interest (ROI). For cross-tracer transfer learning, all the U-Nets provided denoised images with similar quality for both tracers. There was no significant difference in terms of NRMSE and SNR when comparing the former two U-Nets with the reference U-Net. The ROI biases for these U-Nets were similar. For cross-tracer and cross-protocol transfer learning, the pre-trained single-bed FDG U-Net fine-tuned by whole-body DOTATATE data provided the most consistent images with the reference U-Net. Fine-tuning significantly reduced the NRMSE and the ROI bias and improved the SNR when comparing the fine-tuned U-Net with the U-Net trained by single-bed FDG only (NRMSE: 96.3% ± 21.1% versus 120.6% ± 18.5%, ROI bias: -10.5% ± 13.0% versus -14.7% ± 6.4%, SNR: 4.2 ± 1.4 versus 3.9 ± 1.6, for fine-tuned U-Net and the U-Net trained by single-bed FDG, respectively, with p < 0.01 in all cases). This work demonstrated that it is feasible to utilize existing networks well-trained by FDG datasets to reduce the noise for other less-available tracers and other scanning protocols by using the fine-tuning strategy.

Dose Optimization in 18F-FDG PET Based on Noise-Equivalent Count Rate Measurement and Image Quality Assessment

The present study aimed to optimize the injected dose of ¹⁸F-FDG in whole-body PET/CT scans and assess its effect on noise-equivalent count rate (NECR) and visual image quality (IQ). Methods: Patients scheduled to undergo ¹⁸F-FDG PET/CT were prospectively recruited in the study from January to December 2019, regardless of the indication or underlying disease. Patients were divided into 4 groups and injected with different amounts of ¹⁸F-FDG radioactivity per kilogram of body weight (1.85, 3.7, 5.5, and 7.4 MBq/kg). All patients underwent ¹⁸F-FDG PET/CT studies, and NECR_local was calculated by noting the trues rate, total prompts, and randoms rate for each bed position. Whole-body NECR_global was calculated as the average NECR for all bed positions. IQ was qualitatively assessed for each bed position (IQ_local) and for whole-body PET (IQ_global) by 2 readers using 5-point scores based on prevalence of noise, contrast, and lesion detectability. NECR and IQ were compared among all 4 activity groups. Patients were also subdivided into 4 body-mass-index groups (group I, 15-20 kg/m²; group II, 20.1-25 kg/m²; group III, 25.1-30 kg/m²; and group IV, 30.1-35 kg/m²) for comparison. A P value of less than 0.05 was considered significant. Results: In total, 109 patients underwent ¹⁸F-FDG PET/CT studies after injection of different amounts of ¹⁸F-FDG radioactivity and a mean uptake time of 62.32 min. The mean NECR_global and IQ_global for each group were significantly different from other groups (P < 0.05), with NECR and IQ being higher in high-activity groups than in low-activity groups. The overall IQ was acceptable in all patients, even in the lowest-activity group (1.84 MBq/kg). The mean NECR_global and IQ_global were significantly different in all 4 body-mass-index groups (P < 0.05), except between groups II and III (P > 0.05). NECR_local and IQ_local correlated moderately (r = 0.64). Conclusion: Optimization of injected ¹⁸F-FDG radioactivity from 7.4 MBq/kg (200 μCi/kg) to 1.85 MBq/kg (50 μCi/kg) resulted in acceptable IQ, despite a reduction in NECR.

PET Counting Response Variability Depending on Tumor Location, Activity, and Patient Obesity: A Feasibility Study of Solitary Pulmonary Nodule Using Monte Carlo

We aim to investigate the counting response variations of positron emission tomography (PET) scanners with different detector configurations in the presence of solitary pulmonary nodule (SPN). Using experimentally validated Monte Carlo simulations, the counting performance of four different scanner models with varying tumor activity, location, and patient obesity is represented using a noise equivalent count rate (NECR). NECR is a well-established quantitative metric which has positive correlation with clinically perceived image quality. The combined effect of tumor displacement and increased activity shows a linear ascending trend for NECR with slope ranges of (12.5-18.2)*10^-3 (kBq/cm³)^-1 for three-ring (3R) scanners and (15.3-21.5)*10^-3 (kBq/cm³)^-1 for four-ring (4R). The trend for the combined effect of tumor displacement and patient obesity is exponential decay with 3R configurations weakly dependent on the patient obesity if the tumor is located at the center of the field of view with exponent's range of (6.6-33.8)*10^-2cm^-1. The dependence is stronger for 4R scanners (9.6-38.5)*10^-2cm^-1. The analysis indicates that quantitative PET data from the same SPN patient possibly examined in different time points (e.g., during staging or for the evaluation of treatment response) are affected by the different detector configurations and need to be normalized with patient weight, activity, and tumor location to reduce unwanted bias of the diagnosis. This paper provides also with a proof of concept for the ability of properly tuned simulations to provide additional insights into the counting response variability especially in tumor types where often borderline decisions have to be made regarding their characterization.

Clinical Evaluation of 68Ga-PSMA-II and 68Ga-RM2 PET Images Reconstructed With an Improved Scatter Correction Algorithm

OBJECTIVE

Gallium-68-labeled radiopharmaceuticals pose a challenge for scatter estimation because their targeted nature can produce high contrast in these regions of the kidneys and bladder. Even small errors in the scatter estimate can result in washout artifacts. Administration of diuretics can reduce these artifacts, but they may result in adverse events. Here, we investigated the ability of algorithmic modifications to mitigate washout artifacts and eliminate the need for diuretics or other interventions.

MATERIALS AND METHODS

The model-based scatter algorithm was modified to account for PET/MRI scanner geometry and challenges of non-FDG tracers. Fifty-three clinical 68Ga-RM2 and 68Ga-PSMA-11 whole-body images were reconstructed using the baseline scatter algorithm. For comparison, reconstruction was also processed with modified sampling in the single-scatter estimation and with an offset in the scatter tail-scaling process. None of the patients received furosemide to attempt to decrease the accumulation of radiopharmaceuticals in the bladder. The images were scored independently by three blinded reviewers using the 5-point Likert scale.

RESULTS

The scatter algorithm improvements significantly decreased or completely eliminated the washout artifacts. When comparing the baseline and most improved algorithm, the image quality increased and image artifacts were reduced for both 68Ga-RM2 and for 68Ga-PSMA-11 in the kidneys and bladder regions.

CONCLUSION

Image reconstruction with the improved scatter correction algorithm mitigated washout artifacts and recovered diagnostic image quality in 68Ga PET, indicating that the use of diuretics may be avoided.

A clinical evaluation of the impact of the Bayesian penalized likelihood reconstruction algorithm on PET FDG metrics

PURPOSE

The aim of this study was to evaluate the impact of using the Bayesian penalized likelihood (BPL) algorithm on a bismuth germanium oxide positron emission tomography (PET)/computed tomography (CT) system for F-FDG PET/CT exams in case of low injected activity and scan duration.

MATERIALS AND METHODS

F-FDG respiratory gated PET/CT performed on 102 cancer patients, injected with ∼2 MBq/kg of F-FDG, were reconstructed using two algorithms: ordered subset expectation maximization (OSEM) and BPL. The signal-to-noise ratio (SNR) was calculated as the ratio of mean standard uptake value (SUV) over the standard deviation in a reference volume defined automatically in the liver. The peak SUV and volumes were also measured in lesions larger than 2 cm thanks to the automated segmentation method.

RESULTS

On 85 respiratory gated patients, the median SNR was significantly higher with BPL (P<0.0001) and it is even better when the BMI of the patient increases (odds ratio=1.26).For the 55 lesions, BPL significantly increased the SUVpeak [difference: (-0.5; 1.4), median=0.4, P<0.0001] compared with OSEM in 83.6% of the cases. With BPL, the volume was lower in 61.8% of the cases compared with OSEM, but this was not statistically significant.

CONCLUSION

The BPL algorithm improves the image quality and lesion contrast and appears to be particularly appropriate for patients with a high BMI as it improves the SNR. However, it will be important for patient follow-up or multicenter studies to use the same algorithm and preferably BPL.

Effect of Scatter Limitation Correction with Misregistration between Computed Tomography and Positron Emission Tomography on Scatter Correction: A Physical Phantom Study

PURPOSE

This study aimed to evaluate the advantage of scatter limitation correction with misregistration between μ-map in the computed tomography attenuation correction and positron emission tomography in PET/CT study.

METHODS

We used torso phantom including simulated tumor and arms phantom. The CT scan was performed by changing the position of arms phantom after PET scan. Arms phantom movement was out-side direction, in-side direction, and top-side direction by 1-12 cm, respectively. The standardized uptake value (SUV) of simulated tumor and background (B.G.) were evaluated for three specific parameters. Two scatter corrections were performed with scatter correction (SC), and scatter limitation correction (SLC).

RESULTS

The SUV_max of simulated tumor was increased by 2.80% (SC), and 2.78% (SLC) on out-side arms movement. In the SUV_max, SC and SLC were decreased by 28.6%, 9.04% on in-side arms, respectively. SUV_max of the SC, and SLC were increased on top-side arms. The scatter fraction factor (SFF) of SC and SLC were 0.25, 0.25 on out-side 5 cm and were 0.732, 0.391 on in-side 5 cm and were 0.785, 0.434 on top-side 12 cm, respectively.

CONCLUSION

SLC improved the overestimation of the SUV_max by SC. However, it is necessary to pay attention, in order not to be improved completely. The finding results indicated that SFF was setting 0.40-0.45 in our institute PET/CT system.

Evaluation of 68 Ga-Glutamate Carboxypeptidase II Ligand Positron Emission Tomography for Clinical Molecular Imaging of Atherosclerotic Plaque Neovascularization

Objective—

Intraplaque neovascularization contributes to the progression and rupture of atherosclerotic lesions. Glutamate carboxypeptidase II (GCPII) is strongly expressed by endothelial cells of tumor neovasculature and plays a major role in hypoxia-induced neovascularization in rodent models of benign diseases. We hypothesized that GCPII expression may play a role in intraplaque neovascularization and may represent a target for imaging of atherosclerotic lesions. The aim of this study was to determine frequency, pattern, and clinical correlates of vessel wall uptake of a 68 Ga-GCPII ligand for positron emission tomographic imaging.

Approach and Results—

Data from 150 patients undergoing 68 Ga-GCPII ligand positron emission tomography were evaluated. Tracer uptake in various arterial segments was analyzed and was compared with calcified plaque burden, cardiovascular risk factors, and immunohistochemistry of carotid specimens. Focal arterial uptake of 68 Ga-GCPII ligand was identified at 5776 sites in 99.3% of patients. The prevalence of uptake sites was highest in the thoracic aorta; 18.4% of lesions with tracer uptake were colocalized with calcified plaque. High injected dose ( P =0.0005) and obesity ( P =0.007) were significantly associated with 68 Ga-GCPII ligand accumulation, but other cardiovascular risk factors showed no association. The number of 68 Ga-GCPII ligand uptake sites was significantly associated with overweight condition ( P =0.0154). Immunohistochemistry did not show GCPII expression. Autoradiographic blocking studies indicated nonspecific tracer binding.

Conclusions—

68 Ga-GCPII ligand positron emission tomography does not identify vascular lesions associated with atherosclerotic risk. Foci of tracer accumulation are likely caused by nonspecific tracer binding and are in part noise-related. Taken together, GCPII may not be a priority target for imaging of atherosclerotic lesions.

Optimization of oncological ¹⁸F-FDG PET/CT imaging based on a multiparameter analysis

PURPOSE

This paper describes a method to achieve consistent clinical image quality in (18)F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques.

METHODS

Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects.

RESULTS

The adoption of different schemes for three body mass ranges (<60 kg, 60-90 kg, >90 kg) allows improved image quality with both point spread function and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice.

CONCLUSIONS

Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.

Acquisition time optimization of positron emission tomography studies by use of a regression function derived from torso cross-sections and noise-equivalent counts

In this study, we aimed to optimize the positron emission tomography (PET) acquisition time for individual patients by employing a regression function derived from torso cross-sections by using computed tomography (CT) attenuation corrections and the noise-equivalent counts (NECs). We initially determined the standard image quality or the standard NEC at our institution by visually assessing the images acquired from 61 patients. We measured the NECs of the livers and the torso cross-sections of 165 patients who were evaluated with PET/CT with (18)F-2-fluoro-2-deoxy-D-glucose on the basis of our standard protocol of 120 s/bed position. The optimal acquisition time (OPT) was calculated as the product of the ratio of the standard NEC to the estimated NEC multiplied by 120 s. The estimated NEC was derived from the oval cross-section of each patient by use of the regression function. We evaluated the validity of the OPT equation in 59 additional patients. We determined 5.83 Mcounts as the standard NEC at our institution. The mean OPTs in a group of 59 patients of whom 20, 19, and 20 were underweight, normal-weight, and overweight, respectively, were 106.3 ± 18.0, 137.1 ± 4.6, and 172.1 ± 24.3 s, respectively. After optimization, the NECs for normal-weight and overweight patients increased by 14 and 43 %, respectively, compared with the NECs attained with use of the conventional acquisition time (120 s). Using the regression function based on the torso cross-sections and the NECs enabled optimizations of the PET acquisition times for individual patients.

Dose Optimization in TOF-PET/MR Compared to TOF-PET/CT

Purpose

To evaluate the possible activity reduction in FDG-imaging in a Time-of-Flight (TOF) PET/MR, based on cross-evaluation of patient-based NECR (noise equivalent count rate) measurements in PET/CT, cross referencing with phantom-based NECR curves as well as initial evaluation of TOF-PET/MR with reduced activity.

Materials and Methods

A total of 75 consecutive patients were evaluated in this study. PET/CT imaging was performed on a PET/CT (time-of-flight (TOF) Discovery D 690 PET/CT). Initial PET/MR imaging was performed on a newly available simultaneous TOF-PET/MR (Signa PET/MR). An optimal NECR for diagnostic purposes was defined in clinical patients (NECR_P) in PET/CT. Subsequent optimal activity concentration at the acquisition time ([A]₀) and target NECR (NECR_T) were obtained. These data were used to predict the theoretical FDG activity requirement of the new TOF-PET/MR system. Twenty-five initial patients were acquired with (retrospectively reconstructed) different imaging times equivalent for different activities on the simultaneous PET/MR for the evaluation of clinically realistic FDG-activities.

Results

The obtained values for NECR_P, [A]₀ and NECR_T were 114.6 (± 14.2) kcps (Kilocounts per second), 4.0 (± 0.7) kBq/mL and 45 kcps, respectively. Evaluating the NECR_T together with the phantom curve of the TOF-PET/MR device, the theoretical optimal activity concentration was found to be approximately 1.3 kBq/mL, which represents 35% of the activity concentration required by the TOF-PET/CT. Initial evaluation on patients in the simultaneous TOF-PET/MR shows clinically realistic activities of 1.8 kBq/mL, which represent 44% of the required activity.

Conclusion

The new TOF-PET/MR device requires significantly less activity to generate PET-images with good-to-excellent image quality, due to improvements in detector geometry and detector technologies. The theoretically achievable dose reduction accounts for up to 65% but cannot be fully translated into clinical routine based on the coils within the FOV and MR-sequences applied at the same time. The clinically realistic reduction in activity is slightly more than 50%. Further studies in a larger number of patients are needed to confirm our findings.

Intra-individual comparison of PET/CT with different body weight-adapted FDG dosage regimens

Background

18F-2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET)/ computed tomography (CT) imaging demands guidelines to safeguard sufficient image quality at low radiation exposure. Various FDG dose regimes have been investigated; however, body weight-adapted dose regimens and related image quality (IQ) have not yet been compared in the same patient.

Purpose

To investigate the relationship between FDG dosage and image quality in PET/CT in the same patient and determine prerequisites for low dosage scanning.

Material and Methods

This study included 61 patients undergoing a clinically indicated PET/CT imaging study and follow-up with a normal (NDS, 5 MBq/kg body weight [BW]) and low dosage scanning protocol (LDS, 4 MBq/kg BW), respectively, using a Discovery VCT64 scanner. Two blinded and independent readers randomly assessed IQ of PET using a 5-point Likert scale and signal-to-noise ratio (SNR) of the liver.

Results

Body mass index (BMI) was significantly lower at LDS (P = 0.021) and represented a significant predictor of SNR at both NDS (P < 0.001) and LDS (P = 0.005). NDS with a mean administered activity of 340 MBq resulted in significantly higher IQ (P < 0.001) and SNR as compared with LDS with a mean of 264 MBq (F-value = 23.5, P < 0.001, mixed model ANOVA adjusted for covariate BMI). Non-diagnostic IQ at LDS was associated with a BMI > 22 kg/m².

Conclusion

FDG dosage significantly predicts IQ and SNR in PET/CT imaging as demonstrated in the same patient with optimal IQ achieved at 5 MBq/kg BM. PET/CT imaging at 4 MBq/kg BW may only be recommended in patients with a BMI ≤ 22 kg/m² to maintain diagnostic IQ.

Dose optimization in nuclear medicine

The practice of diagnostic nuclear medicine involves the use of ionizing radiation, and thus the potential risk associated with such exposure must be weighed against the benefits to the patient. This requires that the right test with the right dose be administered to the right patient at the right time. Therefore the procedure should be performed only if it is deemed most appropriate for the clinical question being asked. If appropriate, the procedure should be performed in the most optimum manner that keeps the radiation dose to the patient as low as possible while providing the patient's clinician with information that is needed to devise a plan of medical management. If this approach is followed, the benefits to the patient will far outweigh the small potential risks associate with the procedure. This article discusses these issues, particularly in the context of cardiovascular nuclear medicine and hybrid imaging including PET/CT and SPECT/CT.

Clinical image quality perception and its relation to NECR measurements in PET

Background

The purpose of this study is to describe a clinical relation of noise equivalent count rate (NECR) - an objective measurement of positron emission tomography (PET) systems - measured in a large number of patients, to clinical image quality of PET and their relation to 18F-fluoro-2-deoxyglucose (FDG) activity and patient's weight.

Methods

A total of 71 consecutive patients were evaluated in this retrospective study. All data was automatically analysed using Matlab to estimate the noise equivalent count rate. Then, image quality was evaluated according to two subjective scores: the IQ local score was a 3-point scale assigned to each bed position in all patients and the IQ global score was a 10-point scale assigned after evaluating the coronal whole-body PET. Patient data was also analysed concerning weight, body mass index, FDG dose at the start of acquisition (D_Acq), presence of bowel uptake and presence of FDG-positive pathologic lesions. Two additional parameters were defined for each patient: the ratio between D_Acq and patient weight (R_DW) and the ratio between D_Acq and patient BMI (R_DBMI).

Results

Clinically perceived image quality in PET has a significant positive correlation with NECR measured in patients, R_DW, R_DBMI and presence of pathologic lesions. Clinical image quality furthermore has significant negative correlation with weight, body mass index (BMI) and presence of bowel uptake. Thresholds of R_DW and R_DBMI in which clinical IQ is good to excellent in more than 90% of the patients were 2.6 and 8.0, respectively.

Conclusions

Clinically perceived image quality in PET systems is positively and significantly related to NECR measured in patients. An optimal threshold for the R_DW and R_DBMI was defined in which clinical IQ is good to excellent in more than 90% of patients. With this data, it is possible to extrapolate technical as well as clinical image quality to other PET system and to predict clinical image perception.

Reliability of predicting image signal-to-noise ratio using noise equivalent count rate in PET imaging

PURPOSE

Several investigators have shown that noise equivalent count rate (NECR) is linearly proportional to the square of image signal-to-noise ratio (SNR) when PET images are reconstructed using filtered back-projection. However, to our knowledge, none have shown a similar relationship in fully 3D ordered-subset expectation maximization (OSEM) reconstruction. This paper has two aims. The first is to investigate the NECR-SNR relationship for 3D-OSEM reconstruction using phantom studies while the second aim is to evaluate the NECR-SNR relationship using patient data.

METHODS

An anthropomorphic phantom was scanned on a GE Discovery-STE (DSTE) PET∕CT scanner in 3D mode with an initial activity concentration of 66.34 kBq∕cc. PET data were acquired over the lower chest∕upper abdomen region in dynamic mode. The experiment was repeated with the same activity concentration on a GE Discovery-RX (DRX) scanner. Care was taken to place the phantom at identical positions in both scanners. PET data were then reconstructed using 3D Reprojection (3D-RP) and 3D-OSEM with different reconstruction parameters and the NECR and SNR for each frame∕image were calculated. SNR(2) was then plotted versus the NECR for each scanner, reconstruction method and parameters. In addition, 40 clinical PET∕CT studies from the two scanners (20 patients∕scanner) were evaluated retrospectively. The patient studies from each scanner were further divided into two subgroups of body mass indices (BMI). Each PET study was acquired in 3D mode and reconstructed using both 3D-OSEM and 3D-RP. The NECR and SNR of the bed position covering the patient liver were calculated for each patient and averaged for each subgroup. Comparisons of the NECR and SNR between scanner types and BMIs were performed using a t-test and a p value less than 0.05 was considered significant.

RESULTS

Phantom results showed that SNR(2) versus NECR was linear for 3D-RP reconstruction across all activity concentration on both scanners, as expected. However, when 3D-OSEM was used, this relationship was nonlinear at activity concentrations beyond the peak NECR on both scanners. On the other hand, the plot of SNR(2) versus trues count rate was linear for 3D-OSEM across all activity concentrations on both scanners independent of reconstruction parameters used. In addition, for activity concentrations <30kBq∕cc, phantom results showed a higher SNR (by 12 ± 10%; p < 0.05) and NECR for the DRX scanner compared to DSTE for 3D-RP reconstruction. However, for 3D-OSEM reconstruction, these two scanners had similar SNRs (different by 2% ± 9%; p > 0.05), despite having different NECRs. Patient studies showed a statistically significant difference in NECR as well as the SNR for 3D-RP reconstruction between the two scanners. However, no statistically significant difference was found for 3D-OSEM. A statistically significant difference in both NECR and SNR were found between the different BMI subgroups for both 3D-RP and 3D-OSEM reconstructions.

CONCLUSIONS

For the scanners and reconstruction algorithm used in this study, our results suggest that the image SNR cannot be predicted by the NEC when using 3D-OSEM reconstruction particularly for those clinical applications requiring high activity concentration. Instead, our results suggest that image SNR varies with activity concentration and is dominated by the 3D-OSEM reconstruction algorithm and its associated parameters, while not being affected by the scanner type for the range of activity concentrations usually found in the clinic.