Optimisation of the air fraction correction for lung PET/CT: addressing resolution mismatch

Background Increased pulmonary ^18 F-FDG metabolism in patients with idiopathic pulmonary fibrosis, and other forms of diffuse parenchymal lung disease, can predict measurements of health and lung physiology. To improve PET quantification, voxel-wise air fractions (AF) determined from CT can be used to correct for variable air content in lung PET/CT. However, resolution mismatches between PET and CT can cause artefacts in the AF-corrected image. Methods Three methodologies for determining the optimal kernel to smooth the CT are compared with noiseless simulations and non-TOF MLEM reconstructions of a patient-realistic digital phantom: (i) the point source insertion-and-subtraction method, h_pts ; (ii) AF-correcting with varyingly smoothed CT to achieve the lowest RMSE with respect to the ground truth (GT) AF-corrected volume of interest (VOI), h_AFC ; iii) smoothing the GT image to match the reconstruction within the VOI, h_PVC . The methods were evaluated both using VOI-specific kernels, and a single global kernel optimised for the six VOIs combined. Furthermore, h_PVC was implemented on thorax phantom data measured on two clinical PET/CT scanners with various reconstruction protocols. Results The simulations demonstrated that at <200 iterations (200 i), the kernel width was dependent on iteration number and VOI position in the lung. The h_pts method estimated a lower, more uniform, kernel width in all parts of the lung investigated. However, all three methods resulted in approximately equivalent AF-corrected VOI RMSEs (<10