Matrix approach for processing of iterative reconstruction on cone beam CT

Cone-beam computed tomography (CBCT) is an important technique providing new insights into the inner structure of products in industry and medicine physics. Iterative reconstruction methods have been shown to be more robust than analytical algorithm against the noise and limited angles conditions present in CT. Nevertheless, these methods are not extensively used due to their computational demands. In the iteration algorithm, the matrix of projection is massive and it is very time-consuming to calculate the forward projections and back-projections. In this work, we design a matrix approach that the coefficients of the projection matrix are pre-calculated and simultaneously stored with two compressing formats due to the different sparse structures of the matrix and its transposed matrix. And we implement the corresponding SpMV (sparse matrix-vector multiplication) based on the compressing matrices with GPU platform to realize the acceleration. Experimental results indicate that this method allows efficient implementations of reconstruction in CBCT and it can have a better performance than those with serial computing on CPU.