In many applications of computed tomography, it may not be possible to acquire projection data at all angles, as required by the most commonly used algorithm of convolution backprojection. In such a limited-data situation, we face an ill-posed problem in attempting to reconstruct an image from an incomplete set of projections. Many techniques have been proposed to tackle this situation, employing diverse theories such as signal recovery, image restoration, constrained deconvolution, and constrained optimization, as well as novel schemes such as iterative object-dependent algorithms incorporating a priori knowledge and use of multi-spectral radiation. We present an overview of such techniques and offer a challenge to all readers to reconstruct images from a set of limited-view data provided here.
© 1985 Optical Society of America
Rangaraj Rangayyan, Atam Prakash Dhawan, and Richard Gordon, "Algorithms for limited-view computed tomography: an annotated bibliography and a challenge," Appl. Opt. 24, 4000-4012 (1985)