Evaluation of Image Quality and Quantitation in a Clinical PET Scanner with a Uniformly Sparse Detector Rings Configuration

Abstract

Positron Emission Tomography (PET) cameras encompass a set of compact crystal detector rings. In this proof of principle study, we introduce a sparse ring configuration involving half the original number of rings uniformly spaced across the same axial Field-of-view (FOV) and assess its effect on image quality and quantitation in PET imaging. The Siemens PET/MR (mMR) system matrix was adopted in our proposed configuration. mMR consists of 64 detector rings made of 4×4×20 mm3 LSO crystals, and extending over 25.6cm axial Field-of-view (FOV). To emulate our sparse rings configuration, counts in sinograms associated with at least one even ring number were zeroed (Sparse-Sinograms). To account for the loss in spatial information, the zeroed sinograms were estimated by linear interpolation in sinogram space (Inter-Sinogram). The PET images for the compact, the sparse and the interpolated sparse sinogram data were reconstructed using the OSEM algorithm (21 subsets, 5 iterations) provided by the Software for Tomographic Image Reconstruction using the original mMR system matrix to maintain the same number of slices in all images. We validated our approach for one brain FDG PET/MR dataset in terms of image quality, target-to-background ratio (TBR) and contrast-to-noise ratio (CNR) scores for different number of OSEM iterations. Sparse rings configuration yielded comparable image quality to that of the clinical dataset. TBR and CNR showed increased error with the number of OSEM iterations (8.3% and 23.6% respectively at 5 iterations), decreasing to 1.7% and 5.4% respectively in the Inter-Sinogram images. PET imaging with half the number of original detector rings uniformly spaced over the same axial FOV, yielded comparable image quality, yet reduced TBR and CNR, which may be recovered via linear inter-sinogram interpolation. Uniformly spacing a given set of PET detector rings to double their axial FOV is possible without significant losses in image quality and quantitation.