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THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
Rivista di Medicina Nucleare e Imaging Molecolare
A Journal on Nuclear Medicine and Molecular Imaging
Affiliated to the and to the International Research Group of Immunoscintigraphy
Indexed/Abstracted in: Current Contents/Clinical Medicine, EMBASE, PubMed/MEDLINE, Science Citation Index (SciSearch), Scopus
Impact Factor 2,413
The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2015 Sep 25
18F-FDG-PET Partial volume effect correction using a modified recovery coefficient approach based on functional volume and local contrast: physical validation and clinical feasibility in oncology
Anouan K. J. 1, 2, Lelandais B. 1, Edet-Sanson A. 1, 3, Ruan S. 1, Vera P. 1, 3, Gardin I. 1, 3, Hapdey S. 1, 3 ✉
1 Laboratoire QuantIF-LITIS EA 4108, University of Rouen, Rouen, France;
2 Siemens Healthcare France, Seine-Saint-Denis, France;
3 Department of Nuclear Medicine, Centre Henri Becquerel, Rouen, France
AIM: 2-Deoxy-2-[18F]fluoro-D-glucose 18F-FDG uptake within tumours reflects the glucose consumption of malignant tumours, i.e., the tumour activity. Thus, 18F-FDG uptake measurements enable improved therapeutic monitoring of patients in chemo- or radiotherapy treatment through the detection of changes in tumour uptake via quantitative measurements of the lesion Standard Uptake Values (SUVs) or activity concentrations. A major bias that affects positron emission tomography (PET) image quantitation is the partial volume effect (PVE), which most strongly affects the smallest structures due to the poor spatial resolution of PET. Thus, PVE corrections are important when 18F-FDG-PET images are used as a quantitative tool for monitoring responses to therapy. The aim of this paper was to propose a PVE correction based on a modified recovery coefficient method (termed FARCAS) that considers the functional volumes and local contrasts of lesions that are automatically determined using a semi- automatic iterative segmentation algorithm.
METHODS: The FARCAS method consists of establishing a set of calibration curves based on the mathematical fitting of the RC values as a function of the automatically determined functional lesion volume and local lesion contrast. We set up our method using data from a cylindrical phantom that included spheres of different volumes (range: 0.43 to 97.8 mL) and contrasts (range: 1.7 to 22.9), and we assessed the method using both cylindrical and anthropomorphic phantom data that also included spheres of different volumes and contrasts. FARCAS was also compared with conventional RC methods that only considered the lesion functional volume, either automatically determined (RCVa) or using the ground truth volume (RCVgt). Finally, the clinical feasibility of FARCAS and its evaluation on tumour classification were also assessed on 24 NSCLC lesions.
RESULTS: Whatever the phantom considered, for the spheres with contrast <5, FARCAS obtained comparable results to RCVgt and better than RCVa. For the spheres with contrast >5, FARCAS and RCVa were not statistically different, neither for the cylindrical and nor the anthropomorphic phantom. For the cylindrical phantom FARCAS yielded corrections that were not statistically different to those of RCVa for the smallest spheres (V<2 mL), but statistically superior for the larger spheres (V≥2 mL). RCVgt maintained a non-statistically superior accuracy. Regarding the anthropomorphic data, FARCAS was statistically more accurate than RCVa but not RCVgt. As main findings regarding the clinical data, FARCAS modified the classifications of five of 24 NSCLC lesions based on quantitative PERCIST criteria.
CONCLUSION: The PVE correction proposed in this paper allows the accurate quantification of the PVE- corrected SUV, allowing also an automatic definition of the Metabolic Target Volume (MTV). Our results revealed that the PVE correction based on FARCAS is a better approach than conventional RC to significantly reduce the impact of PVE on lesion quantification, thus improving the evaluation of tumour response to treatment based on PET-CT images.