A Novel Geometric Parameter Self-Calibration Method for Portable CBCT Systems

Abstract

In outdoor environments or environments with space restrictions, it is difficult to transport and use conventional computed tomography (CT) systems. Therefore, there is an urgent need for rapid reconstruction of portable cone-beam CT (CBCT) systems. However, owing to its portability and the characteristics of temporary construction environments, high precision spatial location is difficult to achieve with portable CBCT systems. To overcome these limitations, we propose an iterative self-calibration improvement method with a self-calculated initial value based on the projection relationship and image features. The CT value of an open field image was used as the weight value of the projection data in the subsequent experiments to reduce the nonlinear attenuation of the projection intensity. Subsequently, an initial value was obtained based on the invariance of the rotation axis. Finally, self-calibration was realized iteratively using the reconstructed image. This method overcomes the main problem of the rotation axis invariance calibration algorithm—high similarity between the adjacent positions of symmetrical homogeneous materials. The proposed method not only improves the precision of self-calibration based on the projection relationship, but also reduces the performance cost and solution time of the self-calibration algorithm based on the image features. Thus, it satisfies the precision requirements for self-calibration of portable CBCT systems.

A Novel Reconstruction of the Sparse-View CBCT Algorithm for Correcting Artifacts and Reducing Noise

“…By traversing the three slices corresponding to voxel F i,j,k , their similarity is calculated and normalized to obtain the final weight value. Similar to the approach in the literature [22], the similarity coefficient d is defined as follows, and the final voxel value F i,j,k is computed by:…”

“…1: Standardization of the projection data by literature [22]; 2: If iter = 0, the initial reconstruction x 0 by using the FDK algorithm; divide x into three groups of slices X axis s ;…”

Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST

“…Online calibration uses the acquired projections and then uses prior information about the imaged object [8][9][10][11], or it minimizes a cost function that is defined on the reconstructed image [12][13][14][15]. A review of current approaches was published by Hatamikia et al [16].…”