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Conventional reconstruction in Positron Emission Tomography (PET) imaging involves a line-of-response (LOR) preprocessing step where the raw LOR data are interpolated to evenly spaced sinogram data. The LOR-based reconstruction eliminates this interpolation step and thus gives rise to better spatial resolution and image quality. In the Philips PET/CT product, Gemini GXL, this approach is combined with a blob basis function that leads not only to substantial suppression of the image noise but also to preservation of the resolution. When projecting along the raw LORs, however, the computational advantage associated with projecting an evenly spaced sinogram is lost. In addition, using blobs to represent an object results in more image elements to trace in the projection because an LOR intersects more blobs than voxels for equivalent image quality. Therefore, the combined use of LOR-based reconstruction and a blob basis function requires significantly more computation time and represents a reconstruction performance challenge. In the Gemini GXL software we have used a system-matrix lookup table. Both multiplicative and additive corrections are modeled in the system matrix but not included in the lookup table. By making use of the scanner symmetry, and, more importantly, by aligning the blob matrix with the axial crystal rings, the lookup table is reduced in size by a factor of more than 100. The reconstruction performance is optimized by continuous memory access and block looping techniques in a hybrid-projection method. Compared to a calculate-on-the-fly approach, it is ~3 times faster on a Xeon 3.06 GHz dual-processor computer, which allows GXL to achieve excellent clinical performance.
Hu et al. (Mon,) studied this question.