Study of Computed Tomographic Image Reconstruction employing different Norms for Regularization

Mahipal Singh Parmar, Vinith Rejathalal, V. K. Govindan

Abstract


Computed Tomography (CT) is the most popular medical imaging technique and it is used to generate
images from projections of internal structure of the body. For reconstruction of images, we have a set of projections
captured by the CT scan machine, from different angles, as input. Sufficient number of projections is required to
compute a high quality image. However, heavy dose of x-rays required for the task which is harmful for the patients.
Hence, it is necessary to look for alternate methods that provide images of acceptable qualities even with a few number
of projections, thereby eliminating the harmful effect of high dose x-rays. Among the many algorithms available in the
literature for reconstruction, p-norm based minimization is a popular approach. The quality of reconstructed image is
highly dependent on the index of p-norm. In this paper, we study the performances of reconstruction for different
values of p, that is, we are reconstructing images for L0-norm, L1/2-norm, L1-norm and L2-norm Regularization, and we are comparing
these reconstructed images for same set of projection data. It is observed that L0-norm based reconstruction provides
the sparsest image and L1/2-norm based reconstruction provides the most accurate image.

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ISSN : 2251-1563