Virtual multi-alignment theory of parallel-beam CT image reconstruction for rigid objects

Differential Cone-Beam CT Reconstruction for Planar Objects

Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis

Since X-ray tomography is now widely adopted in many different areas, it becomes more crucial to find a robust routine of handling tomographic data to get better quality of reconstructions. Though there are several existing techniques, it seems helpful to have a more automated method to remove the possible errors that hinder clearer image reconstruction. Here, we proposed an alternative method ...

Parallel ART for image reconstruction in CT using processor arrays

Algebraic Reconstruction Technique (ART) is a widely-used iterative method for solving sparse systems of linear equations. This method (originally due to Kaczmarz) is inherently sequential according to its mathematical definition since, at each step, the current iterate is projected toward one of the hyperplanes defined by the equations. The main advantages of ART are its robustness, its cyclic...

Parallel iterative cone beam CT image reconstruction on a PC cluster

The iterative reconstruction (IR) algorithms are able to generate CT images with higher quality compared with the filtered backprojection method when the projection data is noisy or the radiation dose is low. IR can also be used when the data is incomplete. The major disadvantage of the IR is its high demand on computation and slow reconstruction. To improve the time performance of the IR we pa...

Continuous Regression for Non-rigid Image Alignment

Parameterized Appearance Models (PAMs) such as Active Appearance Models (AAMs), Morphable Models and Boosted Appearance Models have been extensively used for face alignment. Broadly speaking, PAMs methods can be classified into generative and discriminative. Discriminative methods learn a mapping between appearance features and motion parameters (rigid and non-rigid). While discriminative appro...