Dataset Information

Harmonization of standard uptake values across different positron emission tomography/computed tomography systems and different reconstruction algorithms: validation in oncology patients.

ABSTRACT:

Background

EQ.PET is a software package that overcomes the reconstruction-dependent variation of standard uptake values (SUV). In this study, we validated the use of EQ.PET for harmonizing SUVs between different positron emission tomography/computed tomography (PET/CT) systems and reconstruction algorithms.

Methods

In this retrospective study, 49 patients with various cancers were scanned on a Biograph mCT (mCT) or Gemini TF 16 (Gemini) after [¹⁸F]FDG injections. Three groups of patient data were collected: Group 1, patients scanned on mCT or Gemini with data reconstructed using two parameters; Group 2, patients scanned twice on different PET scanners (interval between two scans, 68.9 ± 41.4 days); and Group 3, patients scanned twice using mCT with data reconstructed using different algorithms (interval between two scans, 109.5 ± 60.6 days). The SUVs of the lesions and background were measured, and the tumor-to-background ratios (TBRs) were calculated. In addition, the consistency between the two reconstruction algorithms and confounding factors were evaluated.

Results

In Group 1, the consistency of SUV and TBR between different reconstruction algorithms improved when the EQ.PET filter was applied. In Group 2, by comparing ΔSUV, ΔSUV%, ΔTBR, and ΔTBR% with and without the EQ.PET, the results showed significant differences (P < 0.05). In Group 3, Bland-Altman analysis of ΔSUV with EQ.PET showed an improved consistency relative to that without EQ.PET.

Conclusions

EQ.PET is an efficient tool to harmonize SUVs and TBRs across different reconstruction algorithms. Patients could benefit from the harmonized SUV, ΔSUV, and ΔSUV% for therapy responses and follow-up evaluations.

SUBMITTER: Song Y

PROVIDER: S-EPMC10017904 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Harmonization of standard uptake values across different positron emission tomography/computed tomography systems and different reconstruction algorithms: validation in oncology patients.

Song Yufei Y Meng Xiangxi X Cao Zhen Z Zhao Wei W Zhang Yan Y Guo Rui R Zhou Xin X Yang Zhi Z Li Nan N

EJNMMI physics 20230315 1

<h4>Background</h4>EQ.PET is a software package that overcomes the reconstruction-dependent variation of standard uptake values (SUV). In this study, we validated the use of EQ.PET for harmonizing SUVs between different positron emission tomography/computed tomography (PET/CT) systems and reconstruction algorithms.<h4>Methods</h4>In this retrospective study, 49 patients with various cancers were scanned on a Biograph mCT (mCT) or Gemini TF 16 (Gemini) after [<sup>18</sup>F]FDG injections. Three ...[more]

PMID: 36920590

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Project description:BackgroundPositron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images.MethodsWe trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias.ResultsDifferent levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches.ConclusionsWe found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs. FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.

Project description:ObjectivesDespite the prognostic impacts of preoperative fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography examination, fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography-based prognosis prediction has not been used clinically because of the disparity in data between institutions. By applying an image-based harmonized approach, we evaluated the prognostic roles of fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography parameters in clinical stage I non-small cell lung cancer.MethodsWe retrospectively examined 495 patients with clinical stage I non-small cell lung cancer who underwent fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography examinations before pulmonary resection between 2013 and 2014 at 4 institutions. Three different harmonization techniques were applied, and an image-based harmonization, which showed the best-fit results, was used in the further analyses to evaluate the prognostic roles of fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography parameters.ResultsCutoff values of image-based harmonized fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography parameters, maximum standardized uptake, metabolic tumor volume, and total lesion glycolysis were determined using receiver operating characteristic curves that distinguish pathologic high invasiveness of tumors. Among these parameters, only the maximum standardized uptake was an independent prognostic factor in recurrence-free and overall survivals in univariate and multivariate analyses. High image-based maximum standardized uptake value was associated with squamous histology or lung adenocarcinomas with higher pathologic grades. In subgroup analyses defined by ground-glass opacity status and histology or by clinical stages, the prognostic impact of image-based maximum standardized uptake value was always the highest compared with other fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography parameters.ConclusionsThe image-based fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography harmonization was the best fit, and the image-based maximum standardized uptake was the most important prognostic marker in all patients and in subgroups defined by ground-glass opacity status and histology in surgically resected clinical stage I non-small cell lung cancers.

Dataset Information

Harmonization of standard uptake values across different positron emission tomography/computed tomography systems and different reconstruction algorithms: validation in oncology patients.

Background

Methods

Results

Conclusions

Publications

Harmonization of standard uptake values across different positron emission tomography/computed tomography systems and different reconstruction algorithms: validation in oncology patients.

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