Impact of total variation regularized expectation maximization reconstruction on the image quality of 68Ga-PSMA PET: a phantom and patient study

Yang, Fengjiao; Ai, Shuyue; Wu, Runze; Liu, Yang; Xie, Huifang; Dong, Yun; Meng, Qingle; Wang, Feng

doi:10.1259/bjr.20201356

Cited by 15 publications

(25 citation statements)

References 17 publications

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“…In our study, the SUV of lesions in HYPER Iterative reconstructions was higher than that in OSEM reconstruction. The results were consistent with the findings of a previous study using 68 Ga-PSMA PET in which a significant increase in lesion SUV max was reconstructed with HYPER Iterative [ 18 ]. ΔSUV peak was correlated with lesion size which suggests that small lesions have larger SUV peak elevations than large lesions.…”

Section: Discussionsupporting

confidence: 91%

“…The image quality reconstructed using OSEM may not be able to determine the clinical diagnosis, and there is a necessary to find an effective reconstruction algorithm to improve the image quality to obtain qualified images. A new Bayesian penalized likelihood reconstruction algorithm (HYPER Iterative) developed by United Imaging compensates for this shortcoming [ 18 ]. The HYPER Iterative algorithm incorporates noise control into each iteration, and finds the maximum likelihood solution through repeated iterations.…”

Section: Introductionmentioning

confidence: 99%

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

Sui

Tan

et al. 2022

EJNMMI Phys

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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 (SUVmax), peak standard uptake value (SUVpeak), tumour background ratio (T/N) and the largest diameter of lesions were quantitatively analysed by a third reader who did not participate in the subjective image assessment. 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 SUVmax, SUVpeak and T/N in HYPER0.3, HYPER0.4 and HYPER0.5 were higher than those in OSEM3. In different reconstructions, there was a correlation between lesion size (median, 1.55 cm; range, 0.7–11.0 cm) and SUVpeak variation rate compared to OSEM3 (r = 0.388, − 0.515, − 0.495, − 0.464, and − 0.423, respectively, and all P < 0.001). 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.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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

Sui

Tan

et al. 2022

EJNMMI Phys

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“…Total variation regularized expectation maximization (TVREM), a new BPL algorithm, was introduced recently (HYPER Iterative, United Imaging Healthcare). Related studies have shown that TVREM algorithm in 68 Ga-PSMA imaging can ensure image quality while shortening the acquisition time ( 11 ). Therefore, as a BPL algorithm, TVREM has the potential to further increase the detection rate of lesions, shorten the acquisition time or reduce the dose (reduce administrated dose) for 68 Ga-DOTA-TATE PET imaging.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Total Variation Regularized Expectation Maximization Reconstruction on 68Ga-DOTA-TATE PET/CT Images in Patients With Neuroendocrine Tumor

Liu

et al. 2022

Front. Med.

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ObjectiveThe aim of this study was to investigate the effects of the total variation regularized expectation maximization (TVREM) reconstruction on improving 68Ga-DOTA-TATE PET/CT images compared to the ordered subset expectation maximization (OSEM) reconstruction.MethodA total of 17 patients with neuroendocrine tumors who underwent clinical 68Ga-DOTA-TATE PET/CT were involved in this study retrospectively. The PET images were acquired with either 3 min-per-bed (min/bed) acquisition time and reconstructed with OSEM (2 iterations, 20 subsets, and a 3.2-mm Gaussian filter) and TVREM (seven penalization factors = 0.01, 0.07, 0.14, 0.21, 0.28, 0.35, and 0.42) for 2 and 3 min-per-bed (min/bed) acquisition time using list-mode. The SUVmean of the liver, background variability (BV), signal-to-noise ratios (SNR), SUVmax of the lesions and tumor-to-background ratios (TBR) were measured. The mean percentage difference in the SNR and TBR between TVREM with difference penalization factors and OSEM was calculated. Qualitative image quality was evaluated by two experienced radiologists using a 5-point score scale (5-excellent, 1-poor).ResultsIn total, 63 lesions were analyzed in this study. The SUVmean of the liver did not differ significantly between TVREM and OSEM. The BV of all TVREM groups was lower than OSEM groups (all p < 0.05), and the BV of TVREM 2 min/bed group with penalization factor of 0.21 was considered comparable to OSEM 3 min/bed group (p = 0.010 and 0.006). The SNR, SUVmax and TBR were higher for all TVREM groups compared to OSEM groups (all p < 0.05). The mean percentage difference in the SNR and TBR was larger for small lesions (<10 mm) than that for medium (≥10 mm but < 20 mm) and large lesions (≥20 mm). The highest image quality score was given to TVREM 2 min/bed group with penalization factor of 0.21 (3.77 ± 0.26) and TVREM 3 min/bed group with penalization factor of 0.35 (3.77 ± 0.26).ConclusionTVREM could reduce image noise, improve the SNR, SUVmax and TBR of the lesions, and has the potential to preserves the image quality with shorter acquisition time.

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“…On the other hand, a longer acquisition time may adversely degrade the image quality due to the patient's motion during the acquisition. An alternative solution is to adapt the regularised images with less noise and better contrast, such as block sequential regularised expectation maximization (BSREM) [17,[29][30][31][32]. Moreover, the employment of the TB-PET images can make an unprecedented breakthrough on the image quality of the training dataset.…”

Section: Discussionmentioning

confidence: 99%

Can a Deep Progressive Learning Method Help to Achieve High Image Quality as Total-body PET Imaging?

Wang

Qiao

Wang

et al. 2021

Preprint

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Purpose To propose and validate a total-body PET (TB-PET) guided deep progressive learning method (DPR) for low-dose clinical imaging of standard axial field-of-view PET/CT scanner (SAFOV-PET).Methods List-mode raw data from a total of 182 scans were collected, including 100 patient scans from a TB-PET, and 15 phantom and 67 patient scans from a SAFOV-PET. Neural networks employed in DPR were trained with the high-quality images obtained from the TB-PET using a progressive learning strategy and evaluated on a SAFOV-PET through three stages of studies. The CTN phantom was firstly used to verify the effectiveness of protocols in DPR and OSEM algorithms. Subsequently, list-mode rawdata from retrospective and prospective PET oncological patients (n=26 and 41, respectively) were rebinned into short duration scans (referred as to DPR_full, DPR_1/2, DPR_1/3, and DPR_1/4), and reconstructed with DPR. Full-duration data were reconstructed with OSEM to generate images as a reference. In the retrospective study, the image quality was evaluated using the metrics of standard uptake value (SUV) and target-to-liver ratio (TLR) in lesions, and coefficient of variation (COV) in the liver, which provided evidence for the subsequent study with real-world low-dose injection. In the prospective study, the quantification accuracy was evaluated with the agreement of SUVs in the liver, the blood pool, and the muscle between the DPR and the OSEM images. Quantitative analysis was also performed with the SUV and the TLR in lesions, furthermore on small lesions with a diameter no more than 10mm. In addition, qualitative analysis was performed using a 5-point Likert scale on the following perspectives: contrast, noise, and diagnostic confidence. Results The protocols used in the study were verified to meet the EANM EARL2 requirements. In the retrospective study, the DPR group with one-third acquisition time can yield a comparable image quality to the reference. In the prospective study, good agreement of the SUVs between DPR and OSEM was found in all the selected background tissues even if the injected dose was reduced to 1/3. Both quantitative and qualitative results demonstrated that the DPR_1/3 group showed no significant difference with the reference regarding the liver COV and subjective scores. The lesion SUVs and TLRs in the DPR_1/3 group were significantly enhanced compared with the reference, even for small lesions. Conclusions The proposed DPR method can reduce the injected dose of SAFOV-PET scan by up to 2/3 in a real-world deployment while maintaining image quality.

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Impact of total variation regularized expectation maximization reconstruction on the image quality of 68Ga-PSMA PET: a phantom and patient study

Cited by 15 publications

References 17 publications

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

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

The Impact of Total Variation Regularized Expectation Maximization Reconstruction on 68Ga-DOTA-TATE PET/CT Images in Patients With Neuroendocrine Tumor

Can a Deep Progressive Learning Method Help to Achieve High Image Quality as Total-body PET Imaging?

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