Iterative conic beam tomography based on Bayesian approach to radiation therapy

Abstract

The key problem in increasing efficiency of radiotherapy of malignant tumors in brain and other dangerous neoplasms is the problem of increasing the quality of 3D positioning of a patient before radiotherapy. We consider the principles of development of fast parallel iterative algorithms based on graphic accelerators and the OpenGL library. The proposed approaches provide simultaneous residual minimization for the sought solution and total variation of the reconstructed 3D image. In this case, the number of required initial data, i.e., conic X-ray projections, can be reduced several times, and therefore, the radiation load on the patient can also be accordingly reduced with preservation of the necessary contrast and spatial resolution of the 3D image of the patient. The new heuristic iterative algorithm can be used as an alternative to the known 3D Feldkamp algorithm.