Abstract: The AX-PET collaboration has developed a novel concept for high resolution PET imaging to overcome some of the performance limitations of classical PET cameras, in particular the compromise between spatial resolution and sensitivity introduced by the parallax error. The detector consists of an arrangement of long LYSO scintillating crystals axially oriented around the field of view together with arrays of wave length shifter strips orthogonal to the crystals. This matrix allows a precise 3D measurement of the photon interaction point. This is valid both for photoelectric absorption at 511 key and for Compton scattering down to deposited energies of about 100 keV. Crystals and WLS strips are individually read out using Geiger-mode Avalanche Photo Diodes (G-APDs). The sensitivity of such a detector can be adjusted by changing the number of layers and the resolution is defined by the crystal and strip dimensions. Two AX-PET modules were built and fully characterized in dedicated test set-ups at CERN, with point-like Na-22 sources. Their performance in terms of energy (Renew approximate to 11.8% (FWMH) at 511 key) and spatial resolution was assessed (sigma(axial) approximate to 0.65 mm), both individually and for the two modules in coincidence. Test campaigns at ETH Zurich and at the company AAA allowed the tomographic reconstructions of more complex phantoms validating the 3D reconstruction algorithms. The concept of the AX-PET modules will be presented together with some characterization results. We describe a count rate model which allows to optimize the planing of the tomographic scans.

Abstract: In emission tomography, iterative statistical methods are accepted as the reconstruction algorithms that achieve the best image quality. The accuracy of these methods relies partly on the quality of the system response matrix (SRM) that characterizes the scanner. The more physical phenomena included in the SRM, the higher the SRM quality, and therefore higher image quality is obtained from the reconstruction process. High-resolution small animal scanners contain as many as 10(3)-10(4) small crystal pairs, while the field of view (FOV) is divided into hundreds of thousands of small voxels. These two characteristics have a significant impact on the number of elements to be calculated in the SRM. Monte Carlo (MC) methods have gained popularity as a way of calculating the SRM, due to the increased accuracy achievable, at the cost of introducing some statistical noise and long simulation times. In the work presented here the SRM is calculated using MC methods exploiting the cylindrical symmetries of the scanner, significantly reducing the simulation time necessary to calculate a high statistical quality SRM and the storage space necessary. The use of cylindrical symmetries makes polar voxels a convenient basis function. Alternatively, spherically symmetric basis functions result in improved noise properties compared to cubic and polar basis functions. The quality of reconstructed images using polar voxels, spherically symmetric basis functions on a polar grid, cubic voxels and post-reconstruction filtered polar and cubic voxels is compared from a noise and spatial resolution perspective. This study demonstrates that polar voxels perform as well as cubic voxels, reducing the simulation time necessary to calculate the SRM and the disk space necessary to store it. Results showed that spherically symmetric functions outperform polar and cubic basis functions in terms of noise properties, at the cost of slightly degraded spatial resolution, larger SRM file size and longer reconstruction times. However, we demonstrate that post-reconstruction smoothing, usually applied in emission imaging to reduce the level of noise, can produce a spatial resolution degradation of similar to 50%, while spherically symmetric basis functions produce a degradation of only similar to 6%, compared to polar and cubic voxels, at the same noise level. Therefore, the image quality trade-off obtained with blobs is higher than that obtained with cubic or polar voxels.

Abstract: We present a novel intrinsic method for joint reconstruction of both image and motion in positron emission tomography (PET). Intrinsic motion compensation methods exclusively work on the measured data, without any external motion measurements. Most of these methods separate image from motion estimation: They use deformable image registration/optical flow techniques in order to estimate the motion from individually reconstructed gates. Then, the image is estimated based on this motion information. With these methods, a main problem lies in the motion estimation step, which is based on the noisy gated frames. The more noise is present, the more inaccurate the image registration becomes. As we show both visually and quantitatively, joint reconstruction using a simple deformation field motion model can compete with state-of-the-art image registration methods which use robust multilevel B-spline motion models.