An adaptive multiscale anisotropic diffusion regularized image reconstruction method for digital breast tomosynthesis

As a special case of tomography, digital breast tomosynthesis (DBT) can realize quasi-3D image reconstruction for breast lesion detection from few-view and limited-angle projection data. For DBT image reconstruction, iterative algorithms are needed to suppress artifacts due to undersampling, and adaptive regularizations are necessary for preserving the edges of masses and calcifications. This paper presents a novel reconstruction method by regularizing projection onto convex sets (POCS) with multiscale Tikhonov-total variation (MTTV). The regularization, known as adaptive multiscale anisotropic diffusion, is able to preserve edges to a considerable extent and selectively suppress noise without introducing artifacts. The proposed method is referred to as MTTV-POCS and is evaluated quantitatively using 3D numerical breast and Shepp-Logan phantoms as well as two clinical volume images acquired from an advanced DBT machine. Experimental results show that the proposed method has better performance in metrics of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) over two existing methods: adaptive-steepest-descent-POCS (ASD-POCS) and selective-diffusion regularized simultaneous algebraic reconstruction technique (SD-SART). As indicated by the results, the proposed method is applicable to DBT for high-quality image reconstruction.