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EJNMMI Res. 2018 Nov 15;8(1):99. doi: 10.1186/s13550-018-0454-9.

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

Author information

1
LITIS-QuantIF-EA4108, University of Rouen, Rouen, France.
2
Department of Nuclear Medicine, La Timone University Hospital, Marseille, France.
3
Department of Radiotherapy, La Timone University Hospital, Marseille and SMARTc-INSERM-UMR 911 CR02, Aix-Marseille University, Marseille, France.
4
Department of Radiotherapy, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France.
5
Department of Nuclear Medicine, Centre Henri Becquerel, Rouen, France.
6
Department of Clinical Research, Centre Henri Becquerel, Rouen, France.
7
Department of Nuclear Medicine, La Timone University Hospital, Marseille and CERIMED, Aix-Marseille University, Marseille, France.
8
Department of Nuclear Medicine, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France.
9
SMARTc-CRCM, INSERM UMR1068, CNRS UMR7258, Aix Marseille Université U105, Institut Paoli Calmette et APHM, Marseille, France.
10
Department of Nuclear Medicine, Centre Henri Becquerel, Rouen and LITIS-QuantIF-EA4108, University of Rouen, Rouen, France. sebastien.hapdey@chb.unicancer.fr.

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).

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; FDG kinetics; Lung cancer; Parametric imaging; Quantification

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