A nonlinear algorithm for task-specific tomosynthetic image reconstruction

This investigation defines and tests a simple, nonlinear, task-specific method for rapid tomosynthetic reconstruction of radiographic images designed to allow an increase in specificity at the expense of sensitivity. Representative lumpectomy specimens containing cancer from human breasts were radiographed with a digital mammographic machine. Resulting projective data were processed to yield a series of tomosynthetic slices distributed throughout the breast. Five board-certified radiologists compared tomographic displays of these tissues processed both Linearly (control) and nonlinearly (test) and ranked them in terms of their perceived interpretability. In another task, a different set of nine observers estimated the relative depths of six holes bored in a solid Lucite block as perceived when observed in three dimensions as a tomosynthesized series of test and control slices. All participants preferred the nonlinearly generated tomosynthetic mammograms to those produced conventionally, with or without subsequent deblurring by means of iterative deconvolution. The result was similar (p<0.015) when the hole-depth experiment was performed objectively. We therefore conclude for certain tasks that are unduly compromised by tomosynthetic blurring, the nonlinear tomosynthetic reconstruction method described here may improve diagnostic performance with a negligible increase in cost or complexity.