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

Vallot, Delphine; Caselles, Olivier; Chaltiel, Léonor; Fernandez, Anthony; Gabiache, Erwan; Dierickx, Lawrence; Zerdoud, Slimane; Bauriaud, M.; Courbon, F.

doi:10.1097/mnm.0000000000000729

Cited by 20 publications

(24 citation statements)

References 21 publications

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“…Using 40%SUV max as the threshold, the MTVs of lesions decreased from 21.5 ml (1.5-143.4 ml) of OSEM to 16.3 ml (0.7-110.4 ml) of BPL (p < 0.001). According to Vallot D et al [6], the MTV of 61.8% tumor lesions in BPL group was lower than that in OSEM group, but the difference was not signi cant (p = 0.069). This result is attributed to the full convergence of BPL reconstruction algorithm.…”

Section: Discussionmentioning

confidence: 89%

“…Bayesian penalized likelihood (BPL) reconstruction is a new PET/CT reconstruction algorithm based on the iterative reconstruction [6]. Compared with conventional reconstruction methods, BPL does not only improve the quantitation accuracy of lesions, but also effectively reduce the noise level of the image, and then improve the general image quality and conspicuity of small lesions [7].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Bayesian Penalized Likelihood Reconstruction Algorithm on Quantitative Accuracy of Positron Emission Tomography Volumetric Measurements

Chen

Huang

et al. 2020

Preprint

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Objectives The metabolic tumor volume (MTV) of positron emission tomography/computed tomography (PET/CT) is an important index to evaluate the prognosis and the responses of treatments. The purpose of this study is to assess the impact of Bayesian Penalized Likelihood (BPL) reconstruction algorithm and segmentation methods on the accuracy of MTV via a phantom study.Methods Using the National Electrical Manufactures Association/International Electrotechnical Commission (NEMA/IEC) image quality phantom, six hot spheres and background were filled with 21.56 KBq/ml and 5.39 KBq/ml Na 18 F (a sphere to background ratio of 4: 1). Acquired images were reconstructed using BPL (β = 400) and non-BPL (Ordered subsets expectation maximization + time of flight + point spread function, OSEM+TOF+PSF) algorithms, respectively. MTVs of six spheres were delineated using maximum standardized uptake value (SUV max ) percentage threshold method and iterative adaptive method, respectively. The actual measured volumes of spheres were used as the standard for comparative analysis.Results The MTVs measurement errors in BPL were 4.96%, -3.00%, 6.18%, 5.20%, -10.00% and 18.33%, which was significantly lower than that in non-BPL ( Z = - 2.562, p = 0.009), and the measurement errors in non-BPL were 16.70%, 10.77%, 26.00%, 30.00%, 61.82% and 113.33%. The optimal percentage SUV max threshold of spheres in BPL algorithm was raged in 40% - 45%, which was not affected by the ball size. And there was no significant difference of MTVs measurement accuracy between the 42%SUV max and iterative adaptive threshold (Z = -0.48, p = 0.699). However, using the non-BPL algorithm, the measurement errors of 42%SUV max and iterative adaptive delineation methods were 16.70%, 10.77%, 26.00%, 30.00%, 61.82%, 113.33%, and -7.70%, -9.00%, -8.73%, -5.20%, -12.91%, 38.33% respectively. The MTVs measurement accuracy of iterative adaptive was significantly better than that of the 42%SUV max threshold (Z = -2.24, p = 0.026). The iterative adaptive and 42%SUV max threshold methods had excellent interobserver reliability (ICCs=1.00 for all of six spheres) for MTVs measurement.Conclusion BPL reconstruction algorithm can improve the accuracy of MTVs measurements, especially for small lesions. In the case of using non-BPL methods, the iterative adaptive delineation method should be adopted to improve the accuracy of MTVs measurements.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

The Impact of Bayesian Penalized Likelihood Reconstruction Algorithm on Quantitative Accuracy of Positron Emission Tomography Volumetric Measurements

Chen

Huang

et al. 2020

Preprint

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“…16 Using an optimum penalized factor (beta) value of 450 as in our case yields a high contrast recovery and lower background variability in body FDG-PET images. 6,11,15,17,18 The use of BSREM may have implications notably for the detection of small lesions, such as in-transit metastases as in our case or lymph node metastases. 6,10,11 This is particularly important for malignant melanoma patients, whose prognosis worsens with the presence of in-transit metastases, and even more so if they are missed.…”

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confidence: 91%

“…F-FDG-PET/CT imaging of a 70-year-old man with acral lentiginous melanoma, using a novel digital PET/CT system (Discovery MI PET/CT; GE Healthcare, Waukesha, WI). A dose of 267 MBq of 18 F-FDG was administered. Coronal maximum intensity projection PET image reconstructed using ordered subset expectation maximization (OSEM) algorithm and point-spread function (PSF) (A) shows low uptake at the primary tumor excision site in the left foot (arrowhead).…”

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confidence: 99%

“…In retrospect, some of them are also hardly seen on the OSEM + PSF reconstruction (A). Axial fused 18 F-FDG-PET/CT image using OSEM + PSF reconstruction (C) at the level of one in-transit metastasis shows only very subtle uptake (SUVmax, 1.7) close to background activity in the subcutaneous adipose tissue medial to the sartorius muscle. The same lesion (arrow) is easily identified as distinct focal uptake (SUVmax, 6.1) on the axial fused 18 F-FDG-PET/CT image using BSREM reconstruction, whereas the activity in a vessel nearby is not promoted (D).…”

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confidence: 99%

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BSREM Reconstruction for Improved Detection of In-Transit Metastases With Digital FDG-PET/CT in Patients With Malignant Melanoma

2018

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Block sequential regularized expectation maximization (BSREM) is a Bayesian penalized-likelihood reconstruction algorithm for PET, which reaches full convergence without the detriment of deteriorating the image quality by noise. Therefore, BSREM might have implications particularly for the detection of small lesions, which may be beneficial in melanoma patients. Our case of a 70-year-old man with metastasized malignant melanoma illustrates the impact of such a novel iterative PET reconstruction algorithm. Whereas the lymph node metastases are seen with the latest generation ordered subset expectation maximization reconstruction, the in-transit metastases are identified straightforward only with BSREM reconstruction.

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New PET technologies – embracing progress and pushing the limits

Aide

Lasnon

Kesner

et al. 2021

Eur J Nucl Med Mol Imaging

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

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

Cited by 20 publications

References 21 publications

The Impact of Bayesian Penalized Likelihood Reconstruction Algorithm on Quantitative Accuracy of Positron Emission Tomography Volumetric Measurements

The Impact of Bayesian Penalized Likelihood Reconstruction Algorithm on Quantitative Accuracy of Positron Emission Tomography Volumetric Measurements

BSREM Reconstruction for Improved Detection of In-Transit Metastases With Digital FDG-PET/CT in Patients With Malignant Melanoma

New PET technologies – embracing progress and pushing the limits

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