Due to the complex background and varying scanning conditions, extracting the vessel structure from the rotational angiography (RA) image is challenging. Various methods have been proposed for binary vessel segmentation with no gray level information which is essentially for diagnosis. A new method based on background inpainting and image subtraction is introduced in this paper. Instead of providing binary segmentation result, the proposed method estimates the background caused by surrounding tissues, and the vessel structure with gray level is obtained via image subtraction. The experiments verify that the proposed method works well for clinical sequences with different scanning conditions.
Projection and back-projection are the most computational consuming parts in Computed Tomography (CT) reconstruction. Parallelization strategies using GPU computing techniques have been introduced. We in this paper present a new parallelization scheme for both projection and back-projection. The proposed method is based on CUDA technology carried out by NVIDIA Corporation. Instead of build complex model, we aimed on optimizing the existing algorithm and make it suitable for CUDA implementation so as to gain fast computation speed. Besides making use of texture fetching operation which helps gain faster interpolation speed, we fixed sampling numbers in the computation of projection, to ensure the synchronization of blocks and threads, thus prevents the latency caused by inconsistent computation complexity. Experiment results have proven the computational efficiency and imaging quality of the proposed method.
In this paper, we proposed a new MAP method more suitable for low signal to noise (SNR) measurements. We took the projection space as a Gibbs random field, under such assumption, new priori was defined which is not limited to a small neighborhood region. We choose the hyperparameter of the penalty using maximum-likelihood estimation. We applied filtering scheme in the proposed method to control reconstruction results. The proposed method was applied to reconstruct both simulated data and real clinical data, and the results are discussed. Future work is mentioned at the end of the paper.