A Penalized-Likelihood Image Reconstruction Algorithm for Positron Emission Tomography Exploiting Root Image Size

Iterative image reconstruction methods are considered better as compared to the analytical reconstruction methods in terms of their noise characteristics and quantification ability. Penalized-Likelihood Expectation Maximization (PLEM) image reconstruction methods are able to incorporate prior information about the object being imaged and have flexibility to include various prior functions which are based on different image descriptions. Median Root Priors intrinsically take into account the salient image features, such as edges, which becomes smooth owing to quadratic priors. Generally, a 3*3 pixels neighborhood support or root image size is used to evaluate the median. We evaluate different root image sizes to observe their effect on the final reconstructed image. Our results show that at higher parameter values, root image size has pronounced effect on different image quality parameters evaluated such as reconstructed image bias as compared to the phantom image, contrast and resolution in the reconstructed object. Our results show that for the small-sized objects, small root image is better whereas for objects of diameter more than two to three times of the resolution of the reconstructed object, larger root image size is preferable in terms of reconstruction speed and image quality.

@article{Ahmad2018,
  title     = {A Penalized-Likelihood Image Reconstruction Algorithm for Positron Emission Tomography Exploiting Root Image Size},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {4},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Munir Ahmad and H. M. Tanveer and Z.A. Shaikh and Furkh Zeshan and Usman Sharif Bajwa},
  doi       = {10.14569/IJACSA.2018.090459},
  url       = {https://doi.org/10.14569/IJACSA.2018.090459}
}