论文信息 - 3D Digital Breast Tomosynthesis Using Total Variation Regularization

3D Digital Breast Tomosynthesis Using Total Variation Regularization

3D digital breast imaging promises to significantly reduce both false negatives and false positives, allowing the earlier detection of cancer while reducing the occurrence of callbacks. In this paper we have developed an iterative algorithm that improves on the quality of the reconstructed images over current algorithms like filtered back projection. Our algorithm uses Total Variation to restrain the noise while enhancing edge features. We have applied this algorithm to data obtained using the XCounter mammography system.

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