Discovery-690 shows very good PET physical performance for all the standard NEMA NU-2-2007 measurements. Furthermore, the new reconstruction algorithms available for PET data (TOF and PSF) allow further improvements of the D-690 image quality performance both qualitatively and quantitatively.

The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crystals, finite dimensions of the crystals and depth of interaction). To improve the spatial resolution of the images, a possible way consists of measuring the point spread function (PSF) of the system and then accounting for it inside the reconstruction algorithm. In this work, the system response of the GE Discovery STE operating in 3D mode has been characterized by acquiring (22)Na point sources in different positions of the scanner field of view. An image-based model of the PSF was then obtained by fitting asymmetric two-dimensional Gaussians on the (22)Na images reconstructed with small pixel sizes. The PSF was then incorporated, at the image level, in a three-dimensional ordered subset maximum likelihood expectation maximization (OS-MLEM) reconstruction algorithm. A qualitative and quantitative validation of the algorithm accounting for the PSF has been performed on phantom and clinical data, showing improved spatial resolution, higher contrast and lower noise compared with the corresponding images obtained using the standard OS-MLEM algorithm.

No abstract

By using the 4D-PET/CT acquisition technique, it is possible to compensate for the degradation effect of lesion motion on the reconstructed PET images.

No abstract

A general limitation in PET is represented by the poor spatial resolution of the system. To compensate for this limitation by using iterative reconstruction algorithms it is possible to account for the response of the PET system (Point Spread Function, PSF) in the reconstruction scheme to improve PET image quality and quantitative accuracy. Unfortunately, a common behaviour of iterative reconstruction techniques is the increase of noise as the iterations proceed due to the ill-posed nature of the reconstruction process. On the other hand a high number of iterations is usually needed to recover a significant percentage of the signal and to reach the convergence, especially when including resolution modelling. To solve this dilemma, regularization strategies could be employed to control the noise amplification as the iterations proceed. In this work a new prior for variational Maximum a Posteriori regularization is proposed to be used in a 3D ML-OSEM reconstruction algorithm which also accounts for the PSF of the PET system. The new regularization prior is characterised by a strong smoothing component for regions in the image with a magnitude of the gradient below a given threshold (set to discriminate between background and signal), while preserving edges above the threshold.The new algorithm has been validated on phantom and clinical data. The results showed that the use of the proposed regularization prior allows a better control of the noise, while maintaining high enough signal recovery thanks to the PSF modelling. To obtain the best results using the proposed prior, i.e. the best compromise between noise control and loss of recovered signal, an optimization of the regularization parameters is therefore required.

No abstract

No abstract