Noise model for microcalcification detection in reconstructed tomosynthesis slices

For the detection of microcalcifications, accurate noise estimation has shown to be an important step. In tomosynthesis, noise models have been proposed for projection data. However, it is expected that manufacturers of tomosynthesis systems will not store the raw projection images, but only the reconstructed volumes. We therefore investigated if and how signal dependent image noise can be modelled in the reconstructed volumes. For this research we used a dataset of 41 tomosynthesis volumes, of which 12 volumes contained a total of 20 microcalcification clusters. All volumes were acquired with a prototype of Sectra's photon-counting tomosynthesis system. Preliminary results show that image noise is signal dependent in a reconstructed volume, and that a model of this noise can be estimated from a volume at hand. Evaluation of the noise model was performed by using a basic microcalcification cluster detection algorithm that classifies voxels by using a threshold on a local contrast filter. Image noise was normalized by dividing local contrast in a voxel by the standard deviation of the estimated image noise in that voxel. FROC analysis shows that performance increases strongly, when we use our model to correct for signal dependent image noise in reconstructed volumes.