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Impact of PET reconstruction protocols on quantification of lesions that fulfil the PERCIST lesion inclusion criteria.

ABSTRACT: BACKGROUND:The aim of this study was to compare liver and oncologic lesion standardized uptake values (SUV) obtained through two different reconstruction protocols, GE's newest clinical lesion detection protocol (Q.Clear) and the EANM Research Ltd (EARL) harmonization protocol, and to assess the clinical relevance of potential differences and possible implications for daily clinical practice using the PERCIST lesional inclusion criteria. NEMA phantom recovery coefficients (RC) and SUV normalized for lean body mass (LBM), referred to as SUV normalized for LBM (SUL), of liver and lesion volumes of interest were compared between the two reconstruction protocols. Head-to-toe PET/CT examinations and raw data from 64 patients were retrospectively retrieved. PET image reconstruction was carried out twice: once optimized for quantification, complying with EARL accreditation requirements, and once optimized for lesion detection, according to GE's Q.Clear reconstruction settings. RESULTS:The two reconstruction protocols showed different NEMA phantom RC values for different sphere sizes. Q.Clear values were always highest and exceeded the EARL accreditation maximum for smaller spheres. Comparison of liver SULmean showed a statistically significant but clinically irrelevant difference between both protocols. Comparison of lesion SULpeak and SULmax showed a statistically significant, and clinically relevant, difference of 1.64 and 4.57, respectively. CONCLUSIONS:For treatment response assessment using PERCIST criteria, the harmonization reconstruction protocol should be used as the lesion detection reconstruction protocol using resolution recovery systematically overestimates true SUL values.

SUBMITTER: Devriese J

PROVIDER: S-EPMC6283809 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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Impact of PET reconstruction protocols on quantification of lesions that fulfil the PERCIST lesion inclusion criteria.

Devriese Joke J Beels Laurence L Maes Alex A Van de Wiele Christophe C Pottel Hans H

EJNMMI physics 20181207 1

<h4>Background</h4>The aim of this study was to compare liver and oncologic lesion standardized uptake values (SUV) obtained through two different reconstruction protocols, GE's newest clinical lesion detection protocol (Q.Clear) and the EANM Research Ltd (EARL) harmonization protocol, and to assess the clinical relevance of potential differences and possible implications for daily clinical practice using the PERCIST lesional inclusion criteria. NEMA phantom recovery coefficients (RC) and SUV no ...[more]

PMID: 30523429

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Project description:Point-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction may improve lesion detection in oncologic PET, but can alter quantitation resulting in variable standardized uptake values (SUVs) between different PET systems. This study aims to validate a proprietary software tool (EQ.PET) to harmonize SUVs across different PET systems independent of the reconstruction algorithm used.NEMA NU2 phantom data were used to calculate the appropriate filter for each PSF or PSF+TOF reconstruction from three different PET systems, in order to obtain EANM compliant recovery coefficients. PET data from 517 oncology patients were reconstructed with a PSF or PSF+TOF reconstruction for optimal tumour detection and an ordered subset expectation maximization (OSEM3D) reconstruction known to fulfil EANM guidelines. Post-reconstruction, the proprietary filter was applied to the PSF or PSF+TOF data (PSFEQ or PSF+TOFEQ). SUVs for PSF or PSF+TOF and PSFEQ or PSF+TOFEQ were compared to SUVs for the OSEM3D reconstruction. The impact of potential confounders on the EQ.PET methodology including lesion and patient characteristics was studied, as was the adherence to imaging guidelines.For the 1380 tumour lesions studied, Bland-Altman analysis showed a mean ratio between PSF or PSF+TOF and OSEM3D of 1.46 (95%CI: 0.86-2.06) and 1.23 (95%CI: 0.95-1.51) for SUVmax and SUVpeak, respectively. Application of the proprietary filter improved these ratios to 1.02 (95%CI: 0.88-1.16) and 1.04 (95%CI: 0.92-1.17) for SUVmax and SUVpeak, respectively. The influence of the different confounding factors studied (lesion size, location, radial offset and patient's BMI) was less than 5%. Adherence to the European Association of Nuclear Medicine (EANM) guidelines for tumour imaging was good.These data indicate that it is not necessary to sacrifice the superior lesion detection and image quality achieved by newer reconstruction techniques in the quest for harmonizing quantitative comparability between PET systems.

Project description:BACKGROUND:The clinical impact of hypoxia in solid tumours is indisputable and yet questions about the sensitivity of hypoxia-PET imaging have impeded its uptake into routine clinical practice. Notably, the binding rate of hypoxia-sensitive PET tracers is slow, comparable to the rate of diffusive equilibration in some tissue types, including mucinous and necrotic tissue. This means that tracer uptake on the scale of a PET imaging voxel-large enough to include such tissue and hypoxic cells-can be as much determined by tissue transport properties as it is by hypoxia. Dynamic PET imaging of 20 patients with pancreatic ductal adenocarcinoma was used to assess the impact of transport on surrogate metrics of hypoxia: the tumour-to-blood ratio [TBR(t)] at time t post-tracer injection and the trapping rate k 3 inferred from a two-tissue compartment model. Transport quantities obtained from this model included the vascular influx and efflux rate coefficients, k 1 and k 2, and the distribution volume v d ?k 1/(k 2+k 3). RESULTS:Correlations between voxel- and whole tumour-scale k 3 and TBR values were weak to modest: the population average of the Pearson correlation coefficients (r) between voxel-scale k 3 and TBR (1 h) [TBR(2 h)] values was 0.10 [0.01] in the 20 patients, while the correlation between tumour-scale k 3 and TBR(2 h) values was 0.58. Using Patlak's formula to correct uptake for the distribution volume, correlations became strong (r=0.80[0.52] and r=0.93, respectively). The distribution volume was substantially below unity for a large fraction of tumours studied, with v d ranging from 0.68 to 1 (population average, 0.85). Surprisingly, k 3 values were strongly correlated with v d in all patients. A model was proposed to explain this in which k 3 is a combination of the hypoxia-sensitive tracer binding rate k b and the rate k eq of equilibration in slow-equilibrating regions occupying a volume fraction 1-v d of the imaged tissue. This model was used to calculate the proposed hypoxia surrogate marker k b. CONCLUSIONS:Hypoxia-sensitive PET tracers are slow to reach diffusive equilibrium in a substantial fraction of pancreatic tumours, confounding quantification of hypoxia using both static (TBR) and dynamic (k 3) PET imaging. TBR is reduced by distribution volume effects and k 3 is enhanced by slow equilibration. We proposed a novel model to quantify tissue transport properties and hypoxia-sensitive tracer binding in order to improve the sensitivity of hypoxia-PET imaging.

Project description:BackgroundPositron-emission tomography (PET) simulators are frequently used for development and performance evaluation of segmentation methods or quantitative uptake metrics. To date, most PET simulation tools are based on Monte Carlo simulations, which are computationally demanding. Other analytical simulation tools lack the implementation of time of flight (TOF) or resolution modelling (RM). In this study, a fast and easy-to-use PET simulation-reconstruction package, SiMulAtion and ReconsTruction (SMART)-PET, is developed and validated, which includes both TOF and RM. SMART-PET, its documentation and instructions to calibrate the tool to a specific PET/CT system are available on Zenodo. SMART-PET allows the fast generation of 3D PET images. As input, it requires one image representing the activity distribution and one representing the corresponding CT image/attenuation map. It allows the user to adjust different parameters, such as reconstruction settings (TOF/RM), noise level or scan duration. Furthermore, a random spatial shift can be included, representing patient repositioning. To evaluate the tool, simulated images were compared with real scan data of the NEMA NU 2 image quality phantom. The scan was acquired as a 60-min list-mode scan and reconstructed with and without TOF and/or RM. For every reconstruction setting, ten statistically equivalent images, representing 30, 60, 120 and 300 s scan duration, were generated. Simulated and real-scan data were compared regarding coefficient of variation in the phantom background and activity recovery coefficients (RCs) of the spheres. Furthermore, standard deviation images of each of the ten statistically equivalent images were compared.ResultsSMART-PET produces images comparable to actual phantom data. The image characteristics of simulated and real PET images varied in similar ways as function of reconstruction protocols and noise levels. The change in image noise with variation of simulated TOF settings followed the theoretically expected behaviour. RC as function of sphere size agreed within 0.3-11% between simulated and actual phantom data.ConclusionsSMART-PET allows for rapid and easy simulation of PET data. The user can change various acquisition and reconstruction settings (including RM and TOF) and noise levels. The images obtained show similar image characteristics as those seen in actual phantom data.

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Impact of PET reconstruction protocols on quantification of lesions that fulfil the PERCIST lesion inclusion criteria.

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Impact of PET reconstruction protocols on quantification of lesions that fulfil the PERCIST lesion inclusion criteria.

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