A preliminary investigation of reduced-view image reconstruction from low dose breast CT data

Analytic-based algorithms such as the FDK algorithm is used currently for image reconstruction from data acquired with prototypes of dedicated breast CT scanners. In general, analytic-based algorithms require data collected at a large number (∼500) of views. In current breast-CT scans, imaging dose delivered to the patient is about the same as that used in a typical two-view mammography exam. This highly limited total imaging dose, when distributed over a large number of views in breast CT, can result in low-SNR data. There exists a renewed interest in developing optimization-based (i.e., iterative) algorithms for image reconstruction from low-SNR data and/or from sparse-view data collected at a reduced number of views. Results of recent studies on optimization-based algorithms from CT data suggest that the algorithms may reconstruct images of quality higher than than analytic-based algorithms from low-SNR data and/or from sparse-view data. In this work, we investigated image reconstruction from low-SNR patient-breast-CT data collected at a large number (∼500), as well as at reduced numbers, of views. The result of the study appears to indicate that optimization-based reconstructions can yield breast-CT images from low-SNR data comparable to, or better than, the corresponding FDK reconstructions.