Generalized Filtered Back-projection for Digital Breast Tomosynthesis Reconstruction

Filtered back-projection (FBP) has been commonly used as an efficient and robust reconstruction technique in tomographic X-ray imaging during the last decades. For limited angle tomography acquisitions such as digital breast tomosynthesis, however, standard FBP reconstruction algorithms provide poor results and give rise to image artifacts due to the limited angular range and the coarse angular sampling. Therefore, iterative algorithms are often used in digital breast tomosynthesis since they potentially yield a reconstructed image that is in better accordance with the measured data. In this work, a generalized FBP algorithm is presented, which uses the filtered projection data of all acquired views for back-projection along one direction in order to compute an image that is similar to an iteratively calculated one. The proposed method requires the computation of geometry-dependent filter kernels that provide an efficient reconstruction algorithm with an accuracy comparable to iterative techniques, which will be demonstrated on simulated breast tomosynthesis data.