A half-covered helical cone-beam reconstruction algorithm based on the Radon inversion transformation

Abstract

Compared with the traditional helical cone-beam computed tomography (CT), the field-of-view (FOV) half-covered cone-beam CT can almost double the FOV and thus image the large object by using a smaller panel detector. However, the projections are transversely truncated, resulting in truncation errors in reconstructed images if no correction measures are taken. In this paper, a half-covered cone-beam reconstruction algorithm based on the Radon inversion transformation is developed, in which the data filtering is performed in two steps. The first step is a local operation and can be carried out correctly even when the data is truncated. This performance of local operation makes the original data closer to zero, so the continuity of data is improved. And this also can restrain the truncation errors caused by the following global operation. Numerical simulations and experimental results are presented to demonstrate the algorithm and to compare it with existing algorithms. Preliminary results indicate that the proposed algorithm can well restrain the truncation errors and improves reconstruction quality.