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

Abstract

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, $h_{\mathit{pts}}$; (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), $h_{\mathit{AFC}}$; iii) smoothing the GT image to match the reconstruction within the VOI, $h_{\mathit{PVC}}$. The methods were evaluated both using VOI-specific kernels, and a single global kernel optimised for the six VOIs combined. Furthermore, $h_{\mathit{PVC}}$ 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 $h_{\mathit{pts}}$ 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%) at $\ge$200i. The insensitivity of AF-corrected quantification to kernel width suggests that a single global kernel could be used. For all three methodologies, the computed global kernel resulted in an AF-corrected lung RMSE <10%  at $\ge$200i, while larger lung RMSEs were observed for the VOI–specific kernels. The global kernel approach was then employed with the $h_{\mathit{PVC}}$ method on measured data. The optimally smoothed GT emission matched the reconstructed image well, both within the VOI and the lung background. VOI RMSE was <10%, pre-AFC, for all reconstructions investigated.

Conclusions

Simulations for non-TOF PET indicated that around 200i were needed to approach image resolution stability in the lung. In addition, at this iteration number, a single global kernel, determined from several VOIs, for AFC, performed well over the whole lung. The $h_{\mathit{PVC}}$ method has the potential to be used to determine the kernel for AFC from scans of phantoms on clinical scanners.