System Matrix Analysis for Computed Tomography Imaging.

In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data.

Iterative reconstruction of CT images on GPUs.

Although widely used in nuclear medicine (gamma-camera, single photon emission computed tomography (SPECT), positron emission tomography (PET)), iterative reconstruction has not yet penetrated in CT. The main reason for this is that data sets in CT are much larger than in nuclear medicine and iterative reconstruction then becomes computationally very intensive. Graphical Processing Units (GPUs) provide the possibility to reduce effectively the high computational cost of their implementation.

Fast parallel algorithm for CT image reconstruction.

In X-ray computed tomography (CT) the X rays are used to obtain the projection data needed to generate an image of the inside of an object. The image can be generated with different techniques. Iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections.