Abstract:
Positron Emission Tomography (PET)/Computed
Tomography (CT) is the main medical imaging technique
which used for diagnosing cancer. PET image is showing the
functional activities in the patient while CT imaging presents
the anatomical information. The PET raw-projection data
(sinogram) contains a very high level of Poisson noise, while The
reconstructed image through filtered back-projection algorithm
(FBP) is contaminated with unknown noise that is very similar
to speckle noise distribution. This noise may lead to increase
the doze of radioactive material that given to the patient for
imaging PET and to errors in the diagnosis results. Applying a
suitable filtering approach can increase the effectiveness of the
diagnosing process. Using the high resolution information in the
CT, we propose in this work an adaptive post-reconstruction
curvature motion filtering technique for PET image. The
proposed filter consider computing the diffusivity function (edge
stopping function) based on the fused image (PET/CT) to
guide the smoothing and the sharpening process in the image.
Experiments demonstrate through simulated images that the
performance of the proposed method significantly enhance the
reconstructed PET using FBP algorithm. Further, it compared
with recently published methods, both visually and in terms of
statistical measures.
