Postinjection single photon transmission tomography with ordered-subset algorithms for whole-body PET imaging

International audienc

Fast Fully 3-D Image Reconstruction in PET Using Planograms

International audienc

Fast fully 3D image reconstruction using planograms

International audienc

Exact and approximate rebinning algorithms for 3-D PET data

This paper presents two new rebinning algorithms for the reconstruction of three-dimensional (3-D) positron emission tomography (PET) data, A rebinning algorithm is one that first sorts the 3-D data into an ordinary two-dimensional (2-D) data set containing one sinogram for each transaxial slice to be reconstructed; the 3-D image is then recovered by applying to each slice a 2-D reconstruction method such as filtered-backprojection, This approach allows a significant speedup of 3-D reconstruction, which is particularly useful for applications involving dynamic acquisitions or whole-body imaging, The first new algorithm is obtained by discretizing an exact analytical inversion formula, The second algorithm, called the Fourier rebinning algorithm (FORE), is approximate but allows an efficient implementation based on taking 2-D Fourier transforms of the data, This second algorithm was implemented and applied to data acquired with the new generation of PET systems and also to simulated data for a scanner with an 18 degrees axial aperture, The reconstructed images were compared to those obtained with the 3-D reprojection algorithm (3DRP) which is the standard ''exact'' 3-D filtered-backprojection method, Results demonstrate that FORE provides a reliable alternative to 3DRP, while at the same time achieving an order of magnitude reduction in processing time

Correction Methods for Random Coincidences in Fully 3D Whole-Body PET: Impact on Data and Image Quality

With the advantages of the increased sensitivity of fully 3-dimensional (3D) PET for whole-body imaging come the challenges of more complicated quantitative corrections and, in particular, an increase in the number of random coincidences. The most common method of correcting for random coincidences is the real-time subtraction of a delayed coincidence channel, which does not add bias but increases noise. An alternative approach is the postacquisition subtraction of a low-noise random coincidence estimate, which can be obtained either from a smoothed delayed coincidence sinogram or from a calibration scan or directly estimated. Each method makes different trade-offs between noise amplification, bias, and data-processing requirements. These trade-offs are dependent on activity injected, the local imaging environment (e.g., near the bladder), and the reconstruction algorithm. Methods: Using fully 3D whole-body simulations and phantom studies, we investigate how the gains in noise equivalent count (NEC) rates from using a noiseless random coincidence estimation method are translated to improvements in image signal-to-noise ratio (SNR). The image SNR, however, depends on the image reconstruction method and the local imaging environment. Results: We show that for fully 3D whole-body imaging using a particular set of scanners and clinical protocols, a low-noise estimate of random coincidences improves sinogram and image SNRs by approximately 15% compared with online subtraction of delayed coincidences. Conclusion: A 15% improvement in image SNR arises from a 32% increase in the NEC rate. Thus, scan duration can be reduced by 25% while still maintaining a constant total acquired NE