Using EQ·PET to reduce reconstruction-dependent variations in [18F]FDG-PET brain imaging. (28th August 2019)

• Record Type: Journal Article
• Authors: Vanhoutte, Matthieu; Semah, Franck; Lopes, Renaud; Jaillard, Alice; Petyt, Grégory; Aziz, Anne-Laure; Lahousse, Hélène; Declerck, Jérôme; Pasquier, Florence; Spottiswoode, Bruce; Fahmi, Rachid
• Is Part Of: Physics in medicine & biology. Volume 64:Number 17(2019:Sep.)
• Publication Date: 2019-08-28
• Subjects: [18F]FDG-PET -- EQ·PET -- harmonization -- quantification -- brain imaging -- Alzheimer's disease; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ab35b4
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: This study aims at assessing whether EANM harmonisation strategy combined with EQ·PET methodology could be successfully applied to harmonize brain 2-deoxy-2[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) images. The NEMA NU 2 body phantom was prepared according to the EANM guidelines with an [ 18 F]FDG solution. Raw PET phantom data were reconstructed with three different reconstruction protocols frequently used in clinical PET brain imaging: ( ) Ordered subset expectation maximization (OSEM) 3D with time of flight (TOF), 2 iterations and 21 subsets; ( ) OSEM 3D with TOF, 6 iterations and 21 subsets; and ( ) OSEM 3D with TOF, point spread function (PSF), and 8 iterations and 21 subsets. EQ·PET filters were computed as the Gaussian smoothing that best independently aligned the recovery coefficients (RCs) of reconstructions and with the RCs of the reference reconstruction, . The performance of the EQ·PET filter to reduce variations in quantification due to differences in reconstruction was investigated using clinical PET brain images of 35 early-onset Alzheimer's disease (EOAD) patients. Qualitative assessments and multiple quantitative metrics on the cortical surface at different scale levels with or without partial volume effect correction were evaluated on the [ 18 F]FDG brain data before and after application of the EQ·PET filter. The EQ·PET methodology succeeded in finding the optimal smoothing that minimised root-mean-square error (RMSE) calculated using human brain [ 18 F]FDG-PET datasets of EOAD patients, providing harmonized comparisons in the neurological context. Performance was superior for TOF than for TOF + PSF reconstructions. Results showed the capability of the EQ·PET methodology to minimize reconstruction-induced variabilities between brain [ 18 F]FDG-PET images. However, moderate variabilities remained after harmonizing PSF reconstructions with standard non-PSF OSEM reconstructions, suggesting that precautions should be taken when using PSF modelling.