ENHANCED BREAST TOMOSYNTHESIS RECONSTRUCTION USING DISTANCE-DRIVEN MAXIMUM LIKELIHOOD EXPECTATION MAXIMIZATION TECHNIQUE

Early detection of breast cancer significantly improves patient outcomes through timely and accurate diagnosis. This study proposes a hybrid image reconstruction method combining the Maximum Likelihood Expectation Maximization (MLEM) algorithm with the Distance Driven Method (DDM) for stationary digital breast tomosynthesis, aimed at producing high-resolution three-dimensional (3D) breast images. The method was initially validated using simulated projection data of a digital breast phantom modeled with two spheres of varying radii and attenuation coefficients. Fifteen projection images were generated over a 15° angular range (from +7° to –7° with 1° increments) to replicate realistic tomosynthesis acquisition. The focus plane was set 45 mm above the detector with a pixel size of 0.14 mm. Reconstruction results demonstrated enhanced image quality, with spatial resolution quantitatively evaluated using the Line Spread Function (LSF) across the smaller sphere. Compared to the Ray Driven Method (RDM), the MLEM-DDM approach provided better contrast, sharper edges, and fewer artifacts. Subsequently, the method was applied to experimental data from a real breast phantom. Volumetric reconstructions revealed detailed tissue structures across multiple planes, with spatial resolution assessed by line profiling through two aligned calcifications in the focus plane. The MLEM-DDM method preserved the shape and sharpness of these calcifications more effectively than MLEM-RDM. Overall, the proposed MLEM-DDM framework enhances spatial resolution and visualization quality in stationary digital breast tomosynthesis, demonstrating strong potential for improved early breast cancer detection and diagnostic accuracy