A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement

Colard et al. EJNMMI Research (2018) 8:99 https://doi.org/10.1186/s13550-018-0454-9 ORIGINAL RESEARCH Open Access A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement Elyse Colard1, Sarkis Delcourt2, Laetitia Padovani3, Sébastien Thureau4, Arthur Dumouchel5, Pierrick Gouel5, Justine Lequesne6, Bardia Farman Ara2, Pierre Vera4, David Taïeb7, Isabelle Gardin8, Dominique Barbolosi9† and Sébastien Hapdey8*† Abstract Background: In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. Methods: The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on Ki index, the net influx rate and its maximum value in the lesion (Ki,max). Results: This parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in Ki,max estimates were not statistically significant (Wilcoxon test) for ParaPET (− 3.0%; [− 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (− 8.0%; [− 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (− 7.9%; [− 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [− 14.6%; 132.2%]; p < 10− 5). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the Ki magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUVmax or ΔKi,max) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the Ki images than from the SUV images (additional median variation of − 2.3% [− 52.6%; + 19.1%] for ΔKi,max compared to ΔSUVmax). (Continued on next page) * Correspondence: † Dominique Barbolosi and Sébastien Hapdey contributed equally to this work. 8 Department of Nuclear Medicine, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Colard et al. EJNMMI Research (2018) 8:99 Page 2 of 12 (Continued from previous page) Conclusion: None of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving Ki values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response. Trial registration: NCT 02821936; May 2016. Keywords: Dynamic PET, Parametric imaging, FDG kinetics, Lung cancer, Quantification Introduction Positron emission tomography using 18-fluorodeoxyglucose (FDG PET) is useful for tumour staging, radiotherapy planning, treatment response and disease progression assessment [1]. FDG uptake is characterised by the standardised uptake value (SUV), which is widely used in clinical practice, based on a late static image acquired 60 min post-injection [2]. Therefore, different factors (both technical and physiological) can affect SUV calculation because it does not accurately represent the exact glucose metabolic rate, nor differentiate metabolised from unmetabolised FDG within the tumour [3]. Moreover, SUV is sensitive to patient preparation, scanning procedure, image reconstruction and image analysis procedures, compromising its use for pre-, per- and post-treatment scans comparison and inter-patient comparison [3, 4]. An alternative consists in the determination of the total behaviour of glucose within the lesion using more quantitative measurements. The gold standard for modelling tissue time-activity concentration curves derived from dynamic FDG PET acquisitions is the full kinetic analysis with compartmental modelling using a nonlinear least-squares regression [5] or the simplified Patlak graphical analysis [6]. These methods provide access to FDG kinetic parameters such as the net influx rate constant (Ki). Several studies have demonstrated the potential added value of kinetic parameters over SUV measurement for the assessment of disease progression [7]. Despite Patlak rapid calculation and simple expression, the method has two major practical limitations: the need for continuous blood sampling and a 1-h dynamic acquisition. Several derived Patlak analyses using image-derived input function (IDIF) extracted from the images of the aorta or the left ventricular to create the shape of the FDG blood concentration function Cp(t) have been proposed in the literature [8–10]. As an alternative to Patlak approach, several simplified quantitative methods have been proposed [11, 12], such as Hunter method based on a static acquisition and a venous blood sample to scale the tri-exponential blood activity curve for each patient. Recently, Barbolosi et al. [13] proposed a methodology to calculate global FDG kinetic parameters for the whole lesion, taking into account measurement errors. However, all these methods required blood samples. In the literature, refined quantitative methods have been proposed to compute 3D kinetic parametric FDG PET images [13–15]. However, these methods required complex reconstruction algorithms and long PET acquisition that have prevented their clinical adoption. Our aim was to develop a method to generate 3D kinetic parametric FDG PET images easy to perform in cl (...truncated)