Can Q.Clear reconstruction be used to improve [68 Ga]Ga-DOTANOC PET/CT image quality in overweight NEN patients?

Zanoni, Lucia; Argalia, Giulia; Fortunati, Emilia; Malizia, Claudio; Allegri, Vincenzo; Calabrò, Diletta; Civollani, Simona; Campana, Davide; Fanti, Stefano; Ambrosini, Valentina

doi:10.1007/s00259-021-05592-w

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…New image reconstruction methods could help in clinical practice by reducing acquisition times while maintaining accuracy ( 18 ), getting better image quality in obese patients ( 19 ), improving accurately identify of very small lesions ( 20 ), and underpinning the use of further new tools such as data driven gating which can help to differentiate malignant lesions from begin structures and injuries ( 21 ). And the most accurate reconstruction factor were likely depending on the count statistics and uptake pattern vary between the radionuclide and radiopharmaceutical ( 22 ).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Similar studies have also reported better overall image quality by Q.Clear compared with OSEM in [ 68 Ga]Ga-DOTANOC PET/CT scans [ 29 ], 18 F-fluciclovine PET/CT scans [ 20 ], and [ 68 Ga]Ga-PSMA PET/CT scans [ 30 ]. This indicates that the improved image quality provided by Q.Clear is not exclusively dedicated to 2-[ 18 F]FDG-PET/CT scans.…”

Section: Discussionmentioning

confidence: 70%

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

et al. 2023

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

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“…Two different approaches were identified for reducing the noise in 68 Ga PET images and enabling low-count 68 Ga PET measurements. The first approach reduces the noise during the image reconstruction process (reconstruction-based noise reduction approaches, n = 11) [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], whereas the second approach is based on neural networks (deep learning approaches for noise reduction, n = 5) [ 37 , 38 , 39 , 40 , 41 ], as seen in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

“…As a result, different ranges of recommended β values were reported, as presented in Table 3 . BMI can also affect the optimal β value as Zanoni et al suggested a rather large β value of 1600 for 68 Ga-DOTANOC PET scanning of scan patients with 25 ≤ BMI < 30 or BMI ≥ 30 [ 34 ]. However, these ranges of β values need to be interpreted with care since this is a GE proprietary and unitless tuning parameter, which can also depend on the version of the reconstruction software.…”

Section: Resultsmentioning

confidence: 99%

Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

et al. 2022

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Gallium-68 (68Ga) is characterized by relatively high positron energy compared to Fluorine-18 (18F), causing substantial image quality degradation. Furthermore, the presence of statistical noise can further degrade image quality. The aim of this literature review is to identify the recently developed positron range correction techniques for 68Ga, as well as noise reduction methods to enhance the image quality of low count 68Ga PET imaging. The search engines PubMed and Scopus were employed, and we limited our research to published results from January 2010 until 1 August 2022. Positron range correction was achieved by using either deblurring or deep learning approaches. The proposed techniques improved the image quality and, in some cases, achieved an image quality comparable to 18F PET. However, none of these techniques was validated in clinical studies. PET denoising for 68Ga-labeled radiotracers was reported using either reconstruction-based techniques or deep learning approaches. It was demonstrated that both approaches can substantially enhance the image quality by reducing the noise levels of low count 68Ga PET imaging. The combination of 68Ga-specific positron range correction techniques and image denoising approaches may enable the application of low-count, high-quality 68Ga PET imaging in a clinical setting.

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Can Q.Clear reconstruction be used to improve [68 Ga]Ga-DOTANOC PET/CT image quality in overweight NEN patients?

Cited by 12 publications

References 21 publications

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

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

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

Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

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