Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner

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 parameterβwas 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, variousβvalues (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 increasingβ, peaking atβ= 550. The CNR for allβ, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withβ= 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.

Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan

Radiomic features are usually used to predict target variables such as the absence or presence of a disease, treatment response, or time to symptom progression. One of the potential clinical applications is in patients with Parkinson's disease. Robust radiomic features for this specific imaging method have not yet been identified, which is necessary for proper feature selection. Thus, we are assessing the robustness of radiomic features in dopamine transporter imaging (DaT). For this study, we made an anthropomorphic head phantom with tissue heterogeneity using a personal 3D printer (polylactide 82% infill); the bone was subsequently reproduced with plaster. A surgical cotton ball with radiotracer (123I-ioflupane) was inserted. Scans were performed on the two-detector hybrid camera with acquisition parameters corresponding to international guidelines for DaT single photon emission tomography (SPECT). Reconstruction of SPECT was performed on a clinical workstation with iterative algorithms. Open-source LifeX software was used to extract 134 radiomic features. Statistical analysis was made in RStudio using the intraclass correlation coefficient (ICC) and coefficient of variation (COV). Overall, radiomic features in different reconstruction parameters showed a moderate reproducibility rate (ICC = 0.636, p <0.01). Assessment of ICC and COV within CT attenuation correction (CTAC) and non-attenuation correction (NAC) groups and within particular feature classes showed an excellent reproducibility rate (ICC > 0.9, p < 0.01), except for an intensity-based NAC group, where radiomic features showed a good repeatability rate (ICC = 0.893, p <0.01). By our results, CTAC becomes the main threat to feature stability. However, many radiomic features were sensitive to the selected reconstruction algorithm irrespectively to the attenuation correction. Radiomic features extracted from DaT-SPECT showed moderate to excellent reproducibility rates. These results make them suitable for clinical practice and human studies, but awareness of feature selection should be held, as some radiomic features are more robust than others.

A position-adaptive noise-reduction method using a deep denoising filter bank for dedicated breast positron emission tomography images

Dedicated breast positron emission tomography (db-PET) is more sensitive than whole-body positron emission tomography and is thus expected to detect early stage breast cancer and determine treatment efficacy. However, it is challenging to decrease the sensitivity of the chest wall side at the edge of the detector, resulting in a relative increase in noise and a decrease in detectability. Longer acquisition times and injection of larger amounts of tracer improve image quality but increase the burden on the patient. Therefore, this study aimed to improve image quality via reconstruction with shorter acquisition time data using deep learning, which has recently been widely used as a noise reduction technique. In our proposed method, a multi-adaptive denoising filter bank structure was introduced by training the training data separately for each detector area because the noise characteristics of db-PET images vary at different locations. Input and ideal images were reconstructed based on 1- and 7-min collection data, respectively, using list mode data. The deep learning model used residual learning with an encoder-decoder structure. The image quality of the proposed method was superior to that of existing noise reduction filters such as Gaussian filters and nonlocal mean filters. Furthermore, there was no significant difference between the maximum standardized uptake values before and after filtering using the proposed method. Taken together, the proposed method is useful as a noise reduction filter for db-PET images, as it can reduce the patient burden, scan time, and radiotracer amount in db-PET examinations.

Kinetic Modeling of Brain [18-F]FDG Positron Emission Tomography Time Activity Curves with Input Function Recovery (IR) Method

Accurate positron emission tomography (PET) data quantification relies on high-quality input plasma curves, but venous blood sampling may yield poor-quality data, jeopardizing modeling outcomes. In this study, we aimed to recover sub-optimal input functions by using information from the tail (5th-100th min) of curves obtained through the frequent sampling protocol and an input recovery (IR) model trained with reference curves of optimal shape. Initially, we included 170 plasma input curves from eight published studies with clamp [18F]-fluorodeoxyglucose PET exams. Model validation involved 78 brain PET studies for which compartmental model (CM) analysis was feasible (reference (ref) + training sets). Recovered curves were compared with original curves using area under curve (AUC), max peak standardized uptake value (maxSUV). CM parameters (ref + training sets) and fractional uptake rate (FUR) (all sets) were computed. Original and recovered curves from the ref set had comparable AUC (d = 0.02, not significant (NS)), maxSUV (d = 0.05, NS) and comparable brain CM results (NS). Recovered curves from the training set were different from the original according to maxSUV (d = 3) and biologically plausible according to the max theoretical K1 (53//56). Brain CM results were different in the training set (p < 0.05 for all CM parameters and brain regions) but not in the ref set. FUR showed reductions similarly in the recovered curves of the training and test sets compared to the original curves (p < 0.05 for all regions for both sets). The IR method successfully recovered the plasma inputs of poor quality, rescuing cases otherwise excluded from the kinetic modeling results. The validation approach proved useful and can be applied to different tracers and metabolic conditions.

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

BACKGROUND

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.

OBJECTIVE

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.

MATERIALS AND METHODS

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 90 s per bed (s/bed) with BSREM reconstruction (β=700). Reduced count simulations with varied BSREM β levels were generated from list-mode data: 60 s/bed, β=800; 50 s/bed, β=900; 40 s/bed, β=1000; and 30 s/bed, β=1300. In addition, a single OSEM reconstruction was created at 60 s/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.

RESULTS

The mean patient age was 9.0 ± 5.5 (SD) years, mean weight was 38.5 ± 24.5 kg, 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 90 s/bed β=700 standard (all P>0.05). SUVmax values for lesions were significantly lower from 90 s/bed, β=700 only at a simulated acquisition time of 30 s/bed, β=1300 (P=0.001). In a side-by-side comparison of BSREM versus OSEM reconstructions, 40 s/bed, β=1000 images were generally preferred over 60 s/bed TOF OSEM images.

CONCLUSION

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 90 s/bed may be possible while maintaining diagnostic quality when a BSREM reconstruction algorithm is used.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

BACKGROUND

Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.

METHODS

A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data.

RESULTS

In general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki.

CONCLUSION

In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

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 (β) 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 (β 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 β value of 500. Conversely, RC from OSEM to Q.Clear using β 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 β value of 500 was 5.2-8.1% in the smallest spheres compared to OSEM. When the β 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 β value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as β 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 β value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal β value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended β value of 300 for small lesions and β value of 500 for large lesions in clinical study.

Phantom and clinical evaluation of Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm in 68Ga-PSMA PET-CT studies

In this study, we aimed to examine the effect of varying β-values in the block sequential regularized expectation maximization (BSREM) algorithm under differing lesion sizes to determine an optimal penalty factor for clinical application. The National Electrical Manufacturers Association phantom and 15 prostate cancer patients were injected with 68Ga-PSMA and scanned using a GE Discovery IQ PET/CT scanner. Images were reconstructed using ordered subset expectation maximization (OSEM) and BSREM with different β-values. Then, the background variability (BV), contrast recovery, signal-to-noise ratio, and lung residual error were measured from the phantom data, and the signal-to-background ratio (SBR) and contrast from the clinical data. The increment of BV using a β-value of 100 was 120.0%, and the decrement of BV using a β-value of 1000 was 40.5% compared to OSEM. As β decreased from 1000 to 100, the [Formula: see text] increased by 59.0% for a sphere with a diameter of 10 mm and 26.4% for a sphere with a diameter of 37 mm. Conversely, [Formula: see text] increased by 140.5% and 29.0% in the smallest and largest spheres, respectively. Furthermore, the Δ[Formula: see text] and Δ[Formula: see text] were - 41.1% and - 36.7%, respectively. In the clinical study, OSEM exhibited the lowest SBR and contrast. When the β-value was reduced from 500 to 100, the SBR and contrast increased by 69.7% and 71.8% in small and 35.6% and 33.0%, respectively, in large lesions. Moreover, the optimal β-value decreased as lesion size decreased. In conclusion, a β-value of 400 is optimal for small lesion reconstruction, while β-values of 600 and 500 are optimal for large lesions in phantom and clinical studies, respectively.

Rapid Whole-Body FDG PET/MRI in Oncology Patients: Utility of Combining Bayesian Penalised Likelihood PET Reconstruction and Abbreviated MRI

This study evaluated the diagnostic value of a rapid whole-body fluorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) approach, combining Bayesian penalised likelihood (BPL) PET with an optimised β value and abbreviated MRI (abb-MRI). The study compares the diagnostic performance of this approach with the standard PET/MRI that utilises ordered subsets expectation maximisation (OSEM) PET and standard MRI (std-MRI). The optimal β value was determined by evaluating the noise-equivalent count (NEC) phantom, background variability, contrast recovery, recovery coefficient, and visual scores (VS) for OSEM and BPL with β100-1000 at 2.5-, 1.5-, and 1.0-min scans, respectively. Clinical evaluations were conducted for NEC_patient, NEC_density, liver signal-to-noise ratio (SNR), lesion maximum standardised uptake value, lesion signal-to-background ratio, lesion SNR, and VS in 49 patients. The diagnostic performance of BPL/abb-MRI was retrospectively assessed for lesion detection and differentiation in 156 patients using VS. The optimal β values were β600 for a 1.5-min scan and β700 for a 1.0-min scan. BPL/abb-MRI at these β values was equivalent to OSEM/std-MRI for a 2.5-min scan. By combining BPL with optimal β and abb-MRI, rapid whole-body PET/MRI could be achieved in ≤1.5 min per bed position, while maintaining comparable diagnostic performance to standard PET/MRI.

Impact of patient's habitus on image quality and quantitative metrics in 18F-FDG PET/CT images

PURPOSE

To study how the quantitative parameters of 18F-FDG PET imaging change with the emission scan duration (ESD) and the body-mass-index (BMI) in phantom and patients on a time-of-flight (TOF)-PET/CT system.

METHODS

The image-quality phantom with (b-NEMA-IQ, BMI = 29.2 kg/m²) and without (NEMA-IEC, BMI = 21.4 kg/m²) a 'belt' of water-bags was filled with 18F-FDG activities to obtain nominal standardized uptake values (SUV) of 19, 8 and 5. Patients with BMI ≤ 25 kg/m² (L-BMI) and BMI > 25 kg/m² (H-BMI) were enrolled in this study. Phantom and patients underwent list-mode PET acquisition at 120 s/bed-position. Images reconstructed with clinical protocol and different ESD (120, 90, 75, 60, 45, 30 s) were analysed for comparison of maximum SUV (SUV_max), maximum standardized uptake value lean-body-mass corrected (SUL_max) and noise.

RESULTS

79 oncologic patients (45 L-BMI, 44 H-BMI) were analysed. From 90 s to 30 s, an increasing variation of SUV_max and SUL_max with respect to the reference 120 s time was observed, from 18% to 60% and from 16% to 37% for phantom and patients, respectively. SUV_max values were significantly higher (+50%) in b-NEMA-IQ than NEMA-IQ phantom and in H-BMI (+33%) than L-BMI patients. No significant difference was found in SUL_max for the two BMI categories in both phantom and patients. CV values decreased when increasing ESD, being higher in H-BMI patients (0.13-0.25) and b-NEMA-IQ phantom (0.15-0.28) than in L-BMI patients (0.11-0.21) and NEMA-IQ phantom (0.11-0.20).

CONCLUSIONS

Reduction of ESD may severely impact on the variations of SUV_max and SUL_max in 18F-FDG PET/CT imaging. This study confirms recommendations of using SUL for lesion uptake quantification, being unaffected by BMI variation.

BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients

The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM. Two independent blinded readers (R1 and R2) evaluated each reconstruction using a 4-point scale for general image quality, noise, and lesion detectability. SUVmax of metastases, brain background, target-to-background ratio (TBR), and contrast recovery (CR) ratio were recorded for each reconstruction. Among all reconstruction techniques, differences in qualitative parameters were analyzed using non-parametric Friedman test, while differences in quantitative parameters were compared using analysis of variances for repeated measures. Cohen's kappa (k) was used to measure inter-reader agreement. The overall detectability of brain metastases was highest for BSREM200 (R1: 2.83 ± 1.17; R2: 2.68 ± 1.32) and BSREM300 (R1: 2.78 ± 1.23; R2: 2.68 ± 1.36), followed by BSREM100, which had lower accuracy owing to noise. The highest median TBR was found for BSREM100 (R1: 2.19 ± 1.05; R2: 2.42 ± 1.08), followed by BSREM200 and BSREM300. Image quality ratings were significantly different among reconstructions (p < 0.001). The median quality score was higher for BSREM100-300, and both noise and metastases' SUVmax decreased with increasing β-value. Inter-reader agreement was particularly high for the detectability of photopenic metastases and blurring (all k > 0.65). BSREM200 and BSREM300 yielded the best results for the detection of brain metastases, surpassing both BSREM400 and OSEM, typically used in clinical practice.

Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image

This work was to explore the efficacy of intelligent algorithm-based computed tomography (CT) to evaluate platelet-rich plasma (PRP) combined with vacuum sealing drainage (VSD) in the treatment of patients with pressure ulcers. Based on the u-net network structure, an image denoising algorithm based on double residual convolution neural network (Dr-CNN) was proposed to denoise the CT images. A total of 84 patients who were hospitalized in hospital were randomly divided into group A (without any intervention), group B (PRP treatment), group C (VSD treatment), and group D (PRP+VSD treatment). Procalcitonin (PCT) was detected by enzyme-linked immunosorbent assay (ELISA) combined with immunofluorescence method, C-reactive protein (CRP) was detected by rate reflectance turbidimetry (RRT), and interleukin-6 (IL-6) was detected by electrochemiluminescence method. The results showed that after treatment, 44 cases (52.38%) of pressure ulcers patients recovered, 24 cases (28.57%) had no change in stage, and 16 cases (19.04%) developed pressure ulcers. The pain scores of group D at 1 week (3.35 ± 0.56 points) and 2 weeks (2.76 ± 0.55 points) after treatment were significantly lower than those in group C (7.77 ± 0.58 points and 6.34 ± 0.44 points, respectively). The time of complete wound healing in group D (24.5 ± 2.32) was obviously lower in contrast to that in groups A, B, and C (35.54 ± 3.22 days, 30.23 ± 2 days, and 29.34 ± 2.15 days, respectively). In addition, the medical satisfaction of group D (8.74 ± 0.69) was significantly higher than that of groups A, B, and C (4.69 ± 0.85, 5.22 ± 0.31, and 5.18 ± 0.59, respectively). The levels of IL-6 and PCT in group D were lower than those in groups A, B, and C, and the differences were statistically significant (P < 0.01). The average values of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the Dr-CNN network model were 37.21 ± 1.09 dB and 0.925 ± 0.01, respectively, which were higher than other algorithms. The mean values of root mean square error (MSE) and normalized mean absolute distance (NMAD) of the Dr-CNN network model were 0.022 ± 0.002 and 0.126 ± 0.012, respectively, which were significantly lower than other algorithms (P < 0.05). The experimental results showed that PrP combined with VSD could significantly reduce the inflammatory response of patients with pressure ulcers. PRP combined with VSD could significantly reduce the pain of dressing change for patients. Moreover, the performance model of image denoising algorithm based on double residual convolutional neural network was better than other algorithms.

Influences on PET Quantification and Interpretation

Various factors have been identified that influence quantitative accuracy and image interpretation in positron emission tomography (PET). Through the continuous introduction of new PET technology—both imaging hardware and reconstruction software—into clinical care, we now find ourselves in a transition period in which traditional and new technologies coexist. The effects on the clinical value of PET imaging and its interpretation in routine clinical practice require careful reevaluation. In this review, we provide a comprehensive summary of important factors influencing quantification and interpretation with a focus on recent developments in PET technology. Finally, we discuss the relationship between quantitative accuracy and subjective image interpretation.

The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR

Background

Q.Clear is a block sequential regularized expectation maximization penalized-likelihood reconstruction algorithm for Positron Emission Tomography (PET). It has shown high potential in improving image reconstruction quality and quantification accuracy in PET/CT system. However, the evaluation of Q.Clear in PET/MR system, especially for clinical applications, is still rare. This study aimed to evaluate the impact of Q.Clear on the ¹⁸F-fluorodeoxyglucose (FDG) PET/MR system and to determine the optimal penalization factor β for clinical use.

Methods

A PET National Electrical Manufacturers Association/ International Electrotechnical Commission (NEMA/IEC) phantom was scanned on GE SIGNA PET/MR, based on NEMA NU 2-2012 standard. Metrics including contrast recovery (CR), background variability (BV), signal-to-noise ratio (SNR) and spatial resolution were evaluated for phantom data. For clinical data, lesion SNR, signal to background ratio (SBR), noise level and visual scores were evaluated. PET images reconstructed from OSEM + TOF and Q.Clear were visually compared and statistically analyzed, where OSEM + TOF adopted point spread function as default procedure, and Q.Clear used different β values of 100, 200, 300, 400, 500, 800, 1100 and 1400.

Results

For phantom data, as β value increased, CR and BV of all sizes of spheres decreased in general; images reconstructed from Q.Clear reached the peak SNR with β value of 400 and generally had better resolution than those from OSEM + TOF. For clinical data, compared with OSEM + TOF, Q.Clear with β value of 400 achieved 138% increment in median SNR (from 58.8 to 166.0), 59% increment in median SBR (from 4.2 to 6.8) and 38% decrement in median noise level (from 0.14 to 0.09). Based on visual assessment from two physicians, Q.Clear with β values ranging from 200 to 400 consistently achieved higher scores than OSEM + TOF, where β value of 400 was considered optimal.

Conclusions

The present study indicated that, on ¹⁸F-FDG PET/MR, Q.Clear reconstruction improved the image quality compared to OSEM + TOF. β value of 400 was optimal for Q.Clear reconstruction.

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise

PURPOSE

To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks.

METHODS

List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series).

RESULTS

OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time.

CONCLUSION

Deep learning-based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.

Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network

BACKGROUND

The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images.

METHODS

A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN.

RESULTS

Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest.

CONCLUSIONS

AI can enhance [18F]FDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUVmax/peak stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUVmax and SUVpeak fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.

Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight 68Ga-PSMA-11 PET/CT Imaging

In prostate cancer, the early detection of distant spread has been shown to be of importance. Prostate-specific membrane antigen (PSMA)-binding radionuclides in positron emission tomography (PET) is a promising method for precise disease staging. PET diagnostics depend on image reconstruction techniques, and ordered subset expectation maximization (OSEM) is the established standard. Block sequential regularized expectation maximization (BSREM) is a more recent reconstruction algorithm and may produce fewer equivocal findings and better lesion detection. Methods: ⁶⁸Ga PSMA-11 PET/CT scans of patients with de novo or suspected recurrent prostate cancer were retrospectively reformatted using both the OSEM and BSREM algorithms. The lesions were counted and categorized by three radiologists. The intra-class correlation (ICC) and Cohen’s kappa for the inter-rater reliability were calculated. Results: Sixty-one patients were reviewed. BSREM identified slightly fewer lesions overall and fewer equivocal findings. ICC was excellent with regards to definitive lymph nodes and bone metastasis identification and poor with regards to equivocal metastasis irrespective of the reconstruction algorithm. The median Cohen’s kappa were 0.66, 0.74, 0.61 and 0.43 for OSEM and 0.61, 0.63, 0.66 and 0.53 for BSREM, with respect to the tumor, local lymph nodes, metastatic lymph nodes and bone metastasis detection, respectively. Conclusions: BSREM in the setting of ⁶⁸Ga PMSA PET staging or restaging is comparable to OSEM.

PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning

PURPOSE

This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans.

METHODS

In this retrospective study, we enrolled 22 cognitively normal subjects, 20 patients with mild cognitive impairment, and 42 patients with Alzheimer disease. Twenty minutes of list-mode PET/CT data were acquired and reconstructed as the ground-truth images. The short-time scans were made in either 1, 2, 3, 4, or 5 minutes. The CNN with a residual learning framework was implemented to predict the ground-truth images of 18F-FBB PET/CT using short-time scans with either a single-slice or a 3-slice input layer. Model performance was evaluated by quantitative and qualitative analyses. Additionally, we quantified the amyloid load in the ground-truth and predicted images using the SUV ratio.

RESULTS

On quantitative analyses, with increasing scan time, the normalized root-mean-squared error and the SUV ratio differences between predicted and ground-truth images gradually decreased, and the peak signal-to-noise ratio increased. On qualitative analysis, the predicted images from the 3-slice CNN model showed better image quality than those from the single-slice model. The 3-slice CNN model with a short-time scan of at least 2 minutes achieved comparable, quantitative prediction of full-time 18F-FBB PET/CT images, with adequate to excellent image quality.

CONCLUSIONS

The 3-slice CNN model with a residual learning framework is promising for the prediction of full-time 18F-FBB PET/CT images from short-time scans.

Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

Background

Image quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6-mm post-processing filter) for ⁶⁸Ga-DOTATATE (⁶⁸Ga-DOTA) PET studies, both visually and quantitatively.

Thirty consecutive whole-body ⁶⁸Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β = 300–1100; 1.0 min/bp: β = 600–1400 and 0.5 min/bp: β = 800–2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.

Results

Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUV_max of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM.

Conclusion

⁶⁸Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUV_max and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

Fully digital PET is unaffected by any deterioration in TOF resolution and TOF image quality in the wide range of routine PET count rates

PURPOSE

Digital PET involving silicon photomultipliers (SiPM) provides an enhanced time-of-flight (TOF) resolution as compared with photomultiplier (PMT)-based PET, but also a better prevention of the count-related rises in dead time and pile-up effects mainly due to smaller trigger domains (i.e., the detection surfaces associated with each trigger circuit). This study aimed to determine whether this latter property could help prevent against deteriorations in TOF resolution and TOF image quality in the wide range of PET count rates documented in clinical routine.

METHODS

Variations, according to count rates, in timing resolution and in TOF-related enhancement of the quality of phantom images were compared between the first fully digital PET (Vereos) and a PMT-based PET (Ingenuity). Single-count rate values were additionally extracted from the list-mode data of routine analog- and digital-PET exams at each 500-ms interval, in order to determine the ranges of routine PET count rates.

RESULTS

Routine PET count rates were lower for the Vereos than for the Ingenuity. For Ingenuity, the upper limits were estimated at approximately 21.7 and 33.2 Mcps after injection of respectively 3 and 5 MBq.kg^-1 of current ¹⁸F-labeled tracers. At 5.8 Mcps, corresponding to the lower limit of the routine count rates documented with the Ingenuity, timing resolutions provided by the scatter phantom were 326 and 621 ps for Vereos and Ingenuity, respectively. At higher count rates, timing resolution was remarkably stable for Vereos but exhibited a progressive deterioration for Ingenuity, respectively reaching 732 and 847 ps at the upper limits of 21.7 and 33.2 Mcps. The averaged TOF-related gain in signal/noise ratio was stable at approximately 2 for Vereos but decreased from 1.36 at 5.8 Mcps to 1.14 and 1.00 at respectively 21.7 and 33.2 Mcps for Ingenuity.

CONCLUSION

Contrary to the Ingenuity PMT-based PET, the Vereos fully digital PET is unaffected by any deterioration in TOF resolution and consequently, in the quality of TOF images, in the wide range of routine PET count rates. This advantage is even more striking with higher count-rates for which the preferential use of digital PET should be further recommended (i.e., dynamic PET recording, higher injected activities).

[Examination of Optimal Window Size and Acquisition Time of Respiratory-gated PET Image: Phantom Study with a SiPM-based PET/CT Scanner]

PURPOSE

This phantom study aimed to determine the optimal acquisition window size for phase-based respiratory gating in silicon photomultiplier (SiPM)-based fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and its acquisition time in respiratory-gated imaging with the optimal window size.

METHODS

Images of a moving NEMA IEC Body Phantom Set^TM with hot spheres were acquired. First, the tumor volume and the maximum standardized uptake value (SUV_max) of images reconstructed using a different window size were evaluated to define the optimal window size. Second, the quality of the images reconstructed using the optimal window size and different acquisition times was evaluated using the detectability score of the 10-mm hot sphere and physical indices.

RESULTS

The volume and the SUV_max of the 10-mm hot sphere were improved when the window size was narrow, and there were no significant differences among images reconstructed using a window size narrower than 20%. To reconstruct an image using the 20% window size, an acquisition time of 5 min was required to visualize the 10-mm hot sphere.

CONCLUSIONS

The optimal window size for phase-based respiratory gating is 20%. Further, an acquisition time of 5 min should be taken for respiratory-gated imaging with the 20% window size on SiPM-based FDG-PET/CT.

Comparison of Image Quality and Semi-quantitative Measurements with Digital PET/CT and Standard PET/CT from Different Vendors

Purpose

This study aimed to evaluate the concordance and equivalence of results between the newly acquired digital PET/CT(dPET) and the standard PET/CT (sPET) to investigate possible differences in visual and semi-quantitative analyses.

Methods

A total of 30 participants were enrolled and underwent a single ¹⁸F-FDG injection followed by dual PET/CT scans, by a dPET scan, and immediately after by the sPET scan or vice versa. Two readers reviewed overall image quality using a 5-point scale and counted the number of suggestive ¹⁸F-FDG avid lesions. The SUV values were measured in the background organs and in hypermetabolic target lesions. Additionally, we objectively evaluated image quality using the liver signal-to-noise ratio (SNR).

Results

The dPET identified 4 additional ¹⁸F-FDG avid lesions in 3 of 30 participants with improved visual image quality. The standard deviations of SUV of the background organs were significantly lower with Digital_PET than with sPET, and dPET could acquire images with better SNR (11.13 ± 2.01 vs. 8.71 ± 1.32, P < 0.001). The reliability of SUV values between scanners showed excellent agreement. Bland-Altman plot analysis of 81 lesions showed an acceptable agreement between scanners for most of the SUVmax and SUVpeak values. No relationship between the SUV values and time delays of dual PET/CT acquisition was found.

Conclusions

The dPET provides improved image quality and lesion detectability than the sPET. The semi-quantitative values of the two PET/CT systems of different vendors are comparable. This pilot study will be an important basis for possible interchangeable use of either system in clinical practice.

Impact of the Bayesian penalized likelihood algorithm (Q.Clear®) in comparison with the OSEM reconstruction on low contrast PET hypoxic images

Purpose

To determine the impact of the Bayesian penalized likelihood (BPL) reconstruction algorithm in comparison to OSEM on hypoxia PET/CT images of NSCLC using ¹⁸F-MIZO and ¹⁸F-FAZA.

Materials and methods

Images of low-contrasted (SBR = 3) micro-spheres of Jaszczak phantom were acquired. Twenty patients with lung neoplasia were included. Each patient benefitted from ¹⁸F-MISO and/or ¹⁸F-FAZA PET/CT exams, reconstructed with OSEM and BPL. Lesion was considered as hypoxic if the lesion SUV_max > 1.4. A blind evaluation of lesion detectability and image quality was performed on a set of 78 randomized BPL and OSEM images by 10 nuclear physicians. SUV_max, SUV_mean, and hypoxic volumes using 3 thresholding approaches were measured and compared for each reconstruction.

Results

The phantom and patient datasets showed a significant increase of quantitative parameters using BPL compared to OSEM but had no impact on detectability. The optimal beta parameter determined by the phantom analysis was β350. Regarding patient data, there was no clear trend of image quality improvement using BPL. There was no correlation between SUV_max increase with BPL and either SUV or hypoxic volume from the initial OSEM reconstruction. Hypoxic volume obtained by a SUV > 1.4 thresholding was not impacted by the BPL reconstruction parameter.

Conclusion

BPL allows a significant increase in quantitative parameters and contrast without significantly improving the lesion detectability or image quality. The variation in hypoxic volume by BPL depends on the method used but SUV > 1.4 thresholding seems to be the more robust method, not impacted by the reconstruction method (BPL or OSEM).

Trial registration

ClinicalTrials.gov, NCT02490696. Registered 1 June 2015

Optimization of injected 68Ga-PSMA activity based on list-mode phantom data and clinical validation

Optimization of injected gallium-68 (⁶⁸Ga) activity for ⁶⁸Ga-prostate-specific membrane antigen positron emission tomography/computed tomography (⁶⁸Ga-PSMA PET/CT) studies is relevant for image quality, radiation protection, and from an economic point of view. However, no clear guidelines are available for ⁶⁸Ga-PSMA studies. Therefore, a phantom study is performed to determine the highest coefficient of variation (COV) acceptable for reliable image interpretation and quantification.

To evaluate image interpretation, the relationship of COV and contrast-to-noise ratio (CNR) was studied. The CNR should remain larger than five, according to the Rose criterion. To evaluate image quantification, the effect of COV on the percentage difference (PD) between quantification results of two studies was analyzed. Comparison was done by calculating the PD of the SUV_max. The maximum allowable PD_SUVmax was set at 20%. The highest COV at which both criteria are still met is defined as COV_max. Of the NEMA Image Quality phantom, a 20 min/bed (2 bed positions) scan was acquired in list-mode PET (Philips Gemini TF PET/CT). The spheres to background activity ratio was approximately 9:1. To obtain images with different COV, lower activity was mimicked by reconstructions with acquisition times of 10 min/bed to 5 s/bed. Pairs of images were obtained by reconstruction of two non-overlapping parts of list-mode data.

For the 10-mm diameter sphere, a COV of 25% still meets the criteria of CNR_SUVmean ≥ 5 and PD_SUVmax ≤ 20%. This phantom scan was acquired with an acquisition time of 116 s and a background activity concentration of 0.71 MBq/kg. Translation to a clinical protocol results in a clinical activity regimen of 3.5 MBq/kg min at injection. To verify this activity regimen, 15 patients (6 MBq/kg min) with a total of 22 lesions are included. Additional reconstructions were made to mimic the proposed activity regimen. Based on the CNR_SUVmax, no lesions were missed with this proposed activity regimen.

For our institution, a clinical activity regimen of 3.5 MBq/kg min at injection is acceptable, which indicates that activity can be reduced by almost 50% compared with the current code of practice. Our proposed method could be used to obtain an objective activity regimen for other PET/CT systems and tracers.

Simultaneous emission and attenuation reconstruction in time-of-flight PET using a reference object

Background

Simultaneous reconstruction of emission and attenuation images in time-of-flight (TOF) positron emission tomography (PET) does not provide a unique solution. In this study, we propose to solve this limitation by including additional information given by a reference object with known attenuation placed outside the patient. Different configurations of the reference object were studied including geometry, material composition, and activity, and an optimal configuration was defined. In addition, this configuration was tested for different timing resolutions and noise levels.

Results

The proposed strategy was tested in 2D simulations obtained by forward projection of available PET/CT data and noise was included using Monte Carlo techniques. Obtained results suggest that the optimal configuration corresponds to a water cylinder inserted in the patient table and filled with activity. In that case, mean differences between reconstructed and true images were below 10%. However, better results can be obtained by increasing the activity of the reference object.

Conclusion

This study shows promising results that might allow to obtain an accurate attenuation map from pure TOF-PET data without prior knowledge obtained from CT, MRI, or transmission scans.