An anthropomorphic phantom for advanced image processing of realistic 18 F-FDG PET-CT oncological studies

In recent years, great efforts have been devoted to develop advanced image processing methods for oncological Positron Emission Tomography/Computed Tomography (PET-CT) images (e.g. segmentation, quantification, texture analysis) able to extract from images tumor characteristics hidden at naked eye, e.g. intra-tumor phenotypic heterogeneity. In order to accurately estimate such imaging descriptors, image processing methods need to be validated on datasets closer to the real clinical conditions, e.g. including lesions described by functional signal of irregular spatial distribution and heterogeneous intensity. The aim of this work was to realize an experimental dataset of a torso anthropomorphic phantom suitable for the assessment of advanced image processing in 18F-fluorodeoxyglucose (18F-FDG) PET-CT oncological studies. The dataset consists into several 18F-FDG PET-CT measurements of the RSD Alderson Thorax phantom including 42 lesions of irregular shape, different volumes (0.8–11.3 cc), and heterogeneous 18F-FDG uptake (actual lesion-to-background ratio (L/B GS ) 3–40). Simulation of real oncological lesions was obtained deriving the shape of the lesions from 18F-FDG PET-CT images of real patient lesions and printing 3D shells by a 3D printer. Radioactive gels consisting in a fast-setting alginate powder at different concentration allowed the simulation of heterogeneous uptake within the shells. The lesions were inserted in the RSD Alderson Thorax phantom, in the thorax and breast compartments, and measured by a current generation PET-CT system. Proofs of concept of the usefulness of the image dataset were provided. Such phantom image set could be downloaded from our and used by researchers for validation purposes.

[1]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[2]  J C Mazziotta,et al.  Assessment of Accuracy of PET Utilizing a 3-D Phantom to Simulate the Activity Distribution of [18F]Fluorodeoxyglucose Uptake in the Human Brain , 1991, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[3]  C. Gini Measurement of Inequality of Incomes , 1921 .

[4]  Giuseppe Baselli,et al.  The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology. , 2012, Medical physics.

[5]  I. El Naqa,et al.  A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015, Physics in medicine and biology.

[6]  M. Brambilla,et al.  Small lesions detectability with the Biograph 16 Hi-Rez PET/CT scanner and fast imaging protocols: performance evaluation using an anthropomorphic thoracic phantom and ROC analyses , 2011, Annals of nuclear medicine.

[7]  I Sassi,et al.  Partial volume corrected 18F-FDG PET mean standardized uptake value correlates with prognostic factors in breast cancer. , 2014, The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of....

[8]  Robert J. Gillies,et al.  The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis , 2015, Scientific Reports.

[9]  Irène Buvat,et al.  Unified description and validation of Monte Carlo simulators in PET , 2005 .

[10]  Giovanna Rizzo,et al.  Scatter correction techniques in 3D PET: a Monte Carlo evaluation , 1998 .

[11]  A. Rahmim,et al.  Is metal artefact reduction mandatory in cardiac PET/CT imaging in the presence of pacemaker and implantable cardioverter defibrillator leads? , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[12]  Luigi Gianolli,et al.  Response to chemotherapy in gastric adenocarcinoma with diffusion‐weighted MRI and 18F‐FDG‐PET/CT: Correlation of apparent diffusion coefficient and partial volume corrected standardized uptake value with histological tumor regression grade , 2014, Journal of magnetic resonance imaging : JMRI.

[13]  Irène Buvat,et al.  Understanding Changes in Tumor Texture Indices in PET: A Comparison Between Visual Assessment and Index Values in Simulated and Patient Data , 2017, The Journal of Nuclear Medicine.

[14]  F. Fazio,et al.  Lesion detectability and quantification in PET/CT oncological studies by Monte Carlo simulations , 2003, IEEE Transactions on Nuclear Science.

[15]  I Buvat,et al.  Monte Carlo simulations in SPET and PET. , 2002, The quarterly journal of nuclear medicine : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology.

[16]  I. Castiglioni,et al.  Predictive value of pre-therapy 18F-FDG PET/CT for the outcome of 18F-FDG PET-guided radiotherapy in patients with head and neck cancer , 2013, European Journal of Nuclear Medicine and Molecular Imaging.

[17]  V. Bettinardi,et al.  Physical performance of the new hybrid PET∕CT Discovery-690. , 2011, Medical physics.

[18]  I. Castiglioni,et al.  A Partial Volume Effect Correction Tailored for 18F-FDG-PET Oncological Studies , 2013, BioMed research international.

[19]  M C Gilardi,et al.  PVE Correction in PET-CT Whole-Body Oncological Studies From PVE-Affected Images , 2011, IEEE Transactions on Nuclear Science.

[20]  Isabella Castiglioni,et al.  A fully automatic, threshold-based segmentation method for the estimation of the Metabolic Tumor Volume from PET images: validation on 3D printed anthropomorphic oncological lesions , 2016 .

[21]  F. Fazio,et al.  A publicly accessible Monte Carlo database for validation purposes in emission tomography , 2005, European Journal of Nuclear Medicine and Molecular Imaging.