Reduction of [68Ga]Ga-DOTA-TATE injected activity for digital PET/MR in comparison with analogue PET/CT

BACKGROUND

New digital detectors and block-sequential regularized expectation maximization (BSREM) reconstruction algorithm improve positron emission tomography (PET)/magnetic resonance (MR) image quality. The impact on image quality may differ from analogue PET/computed tomography (CT) protocol. The aim of this study is to determine the potential reduction of injected [68Ga]Ga-DOTA-TATE activity for digital PET/MR with BSREM reconstruction while maintaining at least equal image quality compared to the current analogue PET/CT protocol.

METHODS

NEMA IQ phantom data and 25 patients scheduled for a diagnostic PET/MR were included. According to our current protocol, 1.5 MBq [68Ga]Ga-DOTA-TATE per kilogram (kg) was injected. After 60 min, scans were acquired with 3 (≤ 70 kg) or 4 (> 70 kg) minutes per bedposition. PET/MR scans were reconstructed using BSREM and factors β 150, 300, 450 and 600. List mode data with reduced counts were reconstructed to simulate scans with 17%, 33%, 50% and 67% activity reduction. Image quality was measured quantitatively for PET/CT and PET/MR phantom and patient data. Experienced nuclear medicine physicians performed visual image quality scoring and lesion counting in the PET/MR patient data.

RESULTS

Phantom analysis resulted in a possible injected activity reduction of 50% with factor β = 600. Quantitative analysis of patient images revealed a possible injected activity reduction of 67% with factor β = 600. Both with equal or improved image quality as compared to PET/CT. However, based on visual scoring a maximum activity reduction of 33% with factor β = 450 was acceptable, which was further limited by lesion detectability analysis to an injected activity reduction of 17% with factor β = 450.

CONCLUSION

A digital [68Ga]Ga-DOTA-TATE PET/MR together with BSREM using factor β = 450 result in 17% injected activity reduction with quantitative values at least similar to analogue PET/CT, without compromising on PET/MR visual image quality and lesion detectability.

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.

Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images

BACKGROUND

The Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function. The β value that controls the behavior of RDP determines the global strength of noise suppression, whereas the γ factor in RDP controls the degree of edge preservation. The present study aimed to assess the effects of various γ factors in RDP on the ability to detect sub-centimeter lesions.

METHODS

All PET data were acquired for 10 min using a Discovery MI PET/CT system (GE Healthcare). We used a NEMA IEC body phantom containing spheres with inner diameters of 10, 13, 17, 22, 28 and 37 mm and 4.0, 5.0, 6.2, 7.9, 10 and 13 mm. The target-to-background ratio of the phantom was 4:1, and the background activity concentration was 5.3 kBq/mL. We also evaluated cold spheres containing only non-radioactive water with the same background activity concentration. All images were reconstructed using BPL + time of flight (TOF). The ranges of β values and γ factors in BPL were 50-600 and 2-20, respectively. We reconstructed PET images using the Duetto toolbox for MATLAB software. We calculated the % hot contrast recovery coefficient (CRC_hot) of each hot sphere, the cold CRC (CRC_cold) of each cold sphere, the background variability (BV) and residual lung error (LE). We measured the full width at half maximum (FWHM) of the micro hollow hot spheres ≤ 13 mm to assess spatial resolution on the reconstructed PET images.

RESULTS

The CRC_hot and CRC_cold for different β values and γ factors depended on the size of the small spheres. The CRC_hot, CRC_cold and BV increased along with the γ factor. A 6.2-mm hot sphere was obvious in BPL as lower β values and higher γ factors, whereas γ factors ≥ 10 resulted in images with increased background noise. The FWHM became smaller when the γ factor increased.

CONCLUSION

High and low γ factors, respectively, preserved the edges of reconstructed PET images and promoted image smoothing. The BPL with a γ factor above the default value in Q.Clear (γ factor = 2) generated high-resolution PET images, although image noise slightly diverged. Optimizing the β value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm.

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.

Quantitative and Qualitative Improvement of Low-Count [68Ga]Citrate and [90Y]Microspheres PET Image Reconstructions Using Block Sequential Regularized Expectation Maximization Algorithm

PURPOSE

There are several important positron emission tomography (PET) imaging scenarios that require imaging with very low photon statistics, for which both quantitative accuracy and visual quality should not be neglected. For example, PET imaging with the low photon statistics is closely related to active efforts to significantly reduce radiation exposure from radiopharmaceuticals. We investigated two examples of low-count PET imaging: (a) imaging [⁹⁰Y]microsphere radioembolization that suffers the very small positron emission fraction of Y-90's decay processes, and (b) cancer imaging with [⁶⁸Ga]citrate with uptake time of 3-4 half-lives, necessary for visualizing tumors. In particular, we investigated a type of penalized likelihood reconstruction algorithm, block sequential regularized expectation maximization (BSREM), for improving both image quality and quantitative accuracy of these low-count PET imaging cases.

PROCEDURES

The NEMA/IEC Body phantom filled with aqueous solution of Y-90 or Ga-68 was scanned to mimic the low-count scenarios of corresponding patient data acquisitions on a time-of-flight (TOF) PET/magnetic resonance imaging system. Contrast recovery, background variation, and signal-to-noise ratio were evaluated in different sets of count densities using both conventional TOF ordered subset expectation (TOF-OSEM) and TOF-BSREM algorithms. The regularization parameter, beta, in BSREM that controls the tradeoff between image noise and resolution was evaluated to find a value for improved confidence in image interpretation. Visual quality assessment of the images obtained from patients administered with [⁶⁸Ga]citrate (n = 6) was performed. We also made preliminary visual image quality assessment for one patient with [⁹⁰Y]microspheres. In Y-90 imaging, the effect of 511-keV energy window selection for minimizing the number of random events was also evaluated.

RESULTS

Quantitatively, phantom images reconstructed with TOF-BSREM showed improved contrast recovery, background variation, and signal-to-noise ratio values over images reconstructed with TOF-OSEM. Both phantom and patient studies of delayed imaging of [⁶⁸Ga]citrate show that TOF-BSREM with beta = 500 gives the best tradeoff between image noise and image resolution based on visual assessment by the readers. The NEMA-IQ phantom study with [⁹⁰Y]microspheres shows that the narrow energy window (460-562 keV) recovers activity concentrations in small spheres better than the regular energy window (425-650 keV) with the beta value of 2000 using the TOF-BSREM algorithm. For the images obtained from patients with [⁶⁸Ga]citrate using TOF-BSREM with beta = 500, the visual analogue scale (VAS) was improved by 17 % and the Likert score was increased by 1 point on average, both in comparison to corresponding scores for images reconstructed using TOF-OSEM.

CONCLUSION

Our investigation shows that the TOF-BSREM algorithm improves the image quality and quantitative accuracy in low-count PET imaging scenarios. However, the beta value in this algorithm needed to be adjusted for each radiopharmaceutical and counting statistics at the time of scans.

Improved image reconstruction of 89Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm

Purpose

The aim of this study was to evaluate the use of a Bayesian penalized likelihood reconstruction algorithm (Q.Clear) for ⁸⁹Zr-immunoPET image reconstruction and its potential to improve image quality and reduce the administered activity of ⁸⁹Zr-immunoPET tracers.

Methods

Eight ⁸⁹Zr-immunoPET whole-body PET/CT scans from three ⁸⁹Zr-immunoPET clinical trials were selected for analysis. On average, patients were imaged 6.3 days (range 5.0–8.0 days) after administration of 69 MBq (range 65–76 MBq) of [⁸⁹Zr]Zr-DFO-daratumumab, [⁸⁹Zr]Zr-DFO-pertuzumab, or [⁸⁹Zr]Zr-DFO-trastuzumab. List-mode PET data was retrospectively reconstructed using Q.Clear with incremental β-values from 150 to 7200, as well as standard ordered-subset expectation maximization (OSEM) reconstruction (2-iterations, 16-subsets, a 6.4-mm Gaussian transaxial filter, “heavy” z-axis filtering and all manufacturers’ corrections active). Reduced activities were simulated by discarding 50% and 75% of original counts in each list mode stream. All reconstructed PET images were scored for image quality and lesion detectability using a 5-point scale. SUV_max for normal liver and sites of disease and liver signal-to-noise ratio were measured.

Results

Q.Clear reconstructions with β = 3600 provided the highest scores for image quality. Images reconstructed with β-values of 3600 or 5200 using only 50% or 25% of the original counts provided comparable or better image quality scores than standard OSEM reconstruction images using 100% of counts.

Conclusion

The Bayesian penalized likelihood reconstruction algorithm Q.Clear improved the quality of ⁸⁹Zr-immunoPET images. This could be used in future studies to improve image quality and/or decrease the administered activity of ⁸⁹Zr-immunoPET tracers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00352-z.

Evaluation of block-sequential regularized expectation maximization reconstruction of 68Ga-DOTATOC, 18F-fluoride, and 11C-acetate whole-body examinations acquired on a digital time-of-flight PET/CT scanner

Background

Block-sequential regularized expectation maximization (BSREM) is a fully convergent iterative image reconstruction algorithm. We hypothesize that tracers with different distribution patterns will result in different optimal settings for the BSREM algorithm. The aim of this study was to evaluate the image quality with variations in the applied β-value and acquisition time for three positron emission tomography (PET) tracers. NEMA image quality phantom measurements and clinical whole-body digital time-of-flight (TOF) PET/computed tomography (CT) examinations with ⁶⁸Ga-DOTATOC (n = 13), ¹⁸F-fluoride (n = 10), and ¹¹C-acetate (n = 13) were included. Each scan was reconstructed using BSREM with β-values of 133, 267, 400, and 533, and ordered subsets expectation maximization (OSEM; 3 iterations, 16 subsets, and 5-mm Gaussian post-processing filter). Both reconstruction methods included TOF and point spread function (PSF) recovery. Quantitative measures of noise, signal-to-noise ratio (SNR), and signal-to-background ratio (SBR) were analysed for various acquisition times per bed position (bp).

Results

The highest β-value resulted in the lowest level of noise, which in turn resulted in the highest SNR and lowest SBR. Noise levels equal to or lower than those of OSEM were found with β-values equal to or higher than 400, 533, and 267 for ⁶⁸Ga-DOTATOC, ¹⁸F-fluoride, and ¹¹C-acetate, respectively. The specified β-ranges resulted in increased SNR at a minimum of 25% (P < 0.0001) and SBR at a maximum of 23% (P < 0.0001) as compared to OSEM. At a reduced acquisition time by 25% for ⁶⁸Ga-DOTATOC and ¹⁸F-fluoride, and 67% for ¹¹C-acetate, BSREM with β-values equal to or higher than 533 resulted in noise equal to or lower than that of OSEM at full acquisition duration (2 min/bp for ⁶⁸Ga-DOTATOC and ¹⁸F-fluoride, 3 min/bp for ¹¹C-acetate). The reduced acquisition time with β 533 resulted in increased SNR (16–26%, P < 0.003) and SBR (6–18%, P < 0.0001 (P = 0.07 for ¹¹C-acetate)) compared to the full acquisition OSEM.

Conclusions

Within tracer-specific ranges of β-values, BSREM reconstruction resulted in increased SNR and SBR with respect to conventional OSEM reconstruction. Similar SNR, SBR, and noise levels could be attained with BSREM at relatively shorter acquisition times or, alternatively, lower administered dosages, compared to those attained with OSEM.

Malignant Cutaneous Melanoma: Updates in PET Imaging

BACKGROUND

Cutaneous malignant melanoma is a neoplasm whose incidence and mortality are dramatically increasing. 18F-FDG PET/CT gained clinical acceptance over the past 2 decades in the evaluation of several glucose-avid neoplasms, including malignant melanoma, particularly for the assessment for distant metastases, recurrence and response to therapy.

OBJECTIVE

To describe the advancements of nuclear medicine for imaging melanoma with particular attention to 18F-FDG-PET and its current state-of-the-art technical innovations.

METHODS

A comprehensive search strategy was used based on SCOPUS and PubMed databases. From all studies published in English, we selected the articles that evaluated the technological insights of 18FFDG- PET in the assessment of melanoma.

RESULTS

State-of-the-art silicon photomultipliers based detectors ("digital") PET/CT scanners are nowadays more common, showing technical innovations that may have beneficial implications for patients with melanoma. Steady improvements in detectors design and architecture, as well as the implementation of both software and hardware technology (i.e., TOF, point spread function, etc.), resulted in significant improvements in PET image quality while reducing radiotracer dose and scanning time.

CONCLUSION

Recently introduced digital PET detector technology in PET/CT and PET/MRI yields higher intrinsic system sensitivity compared with the latest generation analog technology, enabling the detection of very small lesions with potential impact on disease outcome.

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

PURPOSE

Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT.

METHODS

The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300-3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region.

RESULTS

Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2-4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast.

CONCLUSION

The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2-4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.

Comparison between silicon photomultiplier-based and conventional PET/CT in patients with suspected lung cancer-a pilot study

Background

Lung cancer is one of the most common cancers in the world. Early detection and correct staging are fundamental for treatment and prognosis. Positron emission tomography with computed tomography (PET/CT) is recommended clinically. Silicon (Si) photomultiplier (PM)-based PET technology and new reconstruction algorithms are hoped to increase the detection of small lesions and enable earlier detection of pathologies including metastatic spread. The aim of this study was to compare the diagnostic performance of a SiPM-based PET/CT (including a new block-sequential regularization expectation maximization (BSREM) reconstruction algorithm) with a conventional PM-based PET/CT including a conventional ordered subset expectation maximization (OSEM) reconstruction algorithm. The focus was patients admitted for ¹⁸F-fluorodeoxyglucose (FDG) PET/CT for initial diagnosis and staging of suspected lung cancer. Patients were scanned on both a SiPM-based PET/CT (Discovery MI; GE Healthcare, Milwaukee, MI, USA) and a PM-based PET/CT (Discovery 690; GE Healthcare, Milwaukee, MI, USA). Standardized uptake values (SUV) and image interpretation were compared between the two systems. Image interpretations were further compared with histopathology when available.

Results

Seventeen patients referred for suspected lung cancer were included in our single injection, dual imaging study. No statically significant differences in SUV_max of suspected malignant primary tumours were found between the two PET/CT systems. SUV_max in suspected malignant intrathoracic lymph nodes was 10% higher on the SiPM-based system (p = 0.026). Good consistency (14/17 cases) between the PET/CT systems were found when comparing simplified TNM staging. The available histology results did not find any obvious differences between the systems.

Conclusion

In a clinical setting, the new SiPM-based PET/CT system with a new BSREM reconstruction algorithm provided a higher SUV_max for suspected lymph node metastases compared to the PM-based system. However, no improvement in lung cancer detection was seen.

Regularized reconstruction of digital time-of-flight 68Ga-PSMA-11 PET/CT for the detection of recurrent disease in prostate cancer patients

Accurate localization of recurrent prostate cancer (PCa) is critical, especially if curative therapy is intended. With the aim to optimize target-to-background uptake ratio in ⁶⁸Ga-PSMA-11 PET, we investigated the image quality and quantitative measures of regularized reconstruction by block-sequential regularized expectation maximization (BSREM).

Methods: The study encompassed retrospective reconstruction and analysis of 20 digital time-of-flight (TOF) PET/CT examinations acquired 60 min post injection of 2 MBq/kg of ⁶⁸Ga-PSMA-11 in PCa patients with biochemical relapse after primary treatment. Reconstruction by ordered-subsets expectation maximization (OSEM; 3 iterations, 16 subsets, 5 mm gaussian postprocessing filter) and BSREM (β-values of 100-1600) were used, both including TOF and point spread function (PSF) recovery. Background variability (BV) was measured by placing a spherical volume of interest in the right liver lobe and defined as the standard deviation divided by the mean standardized uptake value (SUV). The image quality was evaluated in terms of signal-to-noise ratio (SNR) and signal-to-background ratio (SBR), using SUV_max of the lesions. A visual assessment was performed by four observers.

Results: OSEM reconstruction produced images with a BV of 15%, whereas BSREM with a β-value above 300 resulted in lower BVs than OSEM (36% with β 100, 8% with β 1300). Decreasing the acquisition duration from 2 to 1 and 0.5 min per bed position increased BV for both reconstruction methods, although BSREM with β-values equal to or higher than 800 and 1200, respectively, kept the BV below 15%. In comparison of BSREM with OSEM, the mean SNR improved by 25 to 66% with an increasing β-value in the range of 200-1300, whereas the mean SBR decreased with an increasing β-value, ranging from 0 to 125% with a β-value of 100 and 900, respectively. Decreased acquisition duration resulted in β-values of 800 to 1000 and 1200 to 1400 for 1 and 0.5 min per bed position, respectively, producing improved image quality measures compared with OSEM at a full acquisition duration of 2 min per bed position. The observer study showed a slight overall preference for BSREM β 900 although the interobserver variability was high.

Conclusion: BSREM image reconstruction with β-values in the range of 400-900 resulted in lower BV and similar or improved SNR and SBR in comparison with OSEM.

Quantitative performance and optimal regularization parameter in block sequential regularized expectation maximization reconstructions in clinical 68Ga-PSMA PET/MR

BACKGROUND

In contrast to ordered subset expectation maximization (OSEM), block sequential regularized expectation maximization (BSREM) positron emission tomography (PET) reconstruction algorithms can run until full convergence while controlling image quality and noise. Recent studies with BSREM and 18F-FDG PET reported higher signal-to-noise ratios and higher standardized uptake values (SUV). In this study, we investigate the optimal regularization parameter (β) for clinical 68Ga-PSMA PET/MR reconstructions in the pelvic region applying time-of-flight (TOF) BSREM in comparison to TOF OSEM. Two-minute emission data from the pelvic region of 25 patients who underwent 68Ga-PSMA PET/MR were retrospectively reconstructed. Reference OSEM reconstructions had 28 subsets and 2 iterations. BSREM reconstructions were performed with 15 β values between 150 and 1200. Regions of interest (ROIs) were drawn around lesions and in uniform background. Background SUVmean (average) and SUVstd (standard deviation), and lesion SUVmax (average of 5 hottest voxels) were calculated. Differences were analyzed using the Wilcoxon matched pairs signed-rank test.

RESULTS

A total of 40 lesions were identified in the pelvic region. Background noise (SUVstd) and lesions SUVmax decreased with increasing β. Image reconstructions with β values lower than 400 have higher (p < 0.01) background noise, compared to the reference OSEM reconstructions, and are therefore less useful. Lesions with low activity on images reconstructed with β values higher than 600 have a lower (p < 0.05) SUVmax compared to the reference. These reconstructions are likely visually appealing due to the lower background noise, but the lower SUVmax could possibly render small low-uptake lesions invisible.

CONCLUSIONS

In our study, we showed that PET images reconstructed with TOF BSREM in combination with the 68Ga-PSMA tracer result in lower background noise and higher SUVmax values in lesions compared to TOF OSEM. Our study indicates that a β value between 400 and 550 might be the optimal compromise between high SUVmax and low background noise.

Standard OSEM vs. regularized PET image reconstruction: qualitative and quantitative comparison using phantom data and various clinical radiopharmaceuticals

We investigated the block sequential regularized expectation maximization (BSREM) algorithm. ACR phantom measurements with different count statistics and 60 PET/CT research scans from the GE Discovery 600 and 690 scanners were reconstructed using BSREM and the standard-of-care OSEM algorithm. Hot concentration recovery and cold contrast recovery were measured from the phantom data. Two experienced nuclear medicine physicians reviewed the clinical images blindly. Liver SNR liver and SUV_max of the smallest lesion detected in each patient were also measured. The relationship between the maximum and mean hot concentration recovery remained monotonic below 1.5 maximum concentration recovery. The mean cold contrast recovery remained stable even for decreasing statistics with a highest absolute difference of 4% in air and 2% in bone for each reconstruction method. The D600 images resulted in an average 30% higher SNR than the D690 data for BSREM; there was no difference in SNR results between the two scanners with OSEM. The small lesion SUV_max values on the BSREM images with β of 250, 350 and 450, respectively were on average 80%, 60% and 43% (D690) and 42%, 29%, and 21% (D600) higher than in the case of OSEM. In conclusion, BSREM can outperform OSEM in terms of contrast recovery and organ uniformity over a range of PET tracers, but a task dependent regularization strength parameter (beta) selection may be necessary. To avoid image noise and artifacts, our results suggest that using higher beta values (at least 350) may be appropriate, especially if the data has low count statistics.