Weighted image reconstruction in planar PET

Dual-head planar positron emission tomography (PET) without rotating the camera found its application in breast cancer detection recently due to the properties of high efficiency, high resolution and low cost. However, the image quality for planar PET along depth axis is degraded due to limited angle coverage. One way to alleviate this limitation is applying weighting factor during the reconstruction. The purpose of this work is to explore different weighting approaches in reconstructing the planar PET images. The two weighting methods considered in this work were geometric weighting and data weighting. Geometric weighting factor was computed from geometric LOR ratio within FOV, and data weighting factor was calculated from backprojection of the measured data. Projection data was simulated and then reconstructed by weighted MLEM with certain iterations with non-weighting, geometric weighting, and data weighting. The preliminary results showed that data weighted reconstruction can improve diffused distortion in the depth direction and lead to faster activity recovery in the early iteration. However, weighted reconstructions generated overestimated activity. Future work should include more physical effects and Monte-Carlo simulation study.