Out-of-plane artifact reduction in tomosynthesis based on regression modeling and outlier detection

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Abstract

We propose a method for out-of-plane artifact reduction in digital breast tomosynthesis reconstruction. Because of the limited angular range acquisition in DBT, the reconstructed slices have reduced resolution in z-direction and are affected by artifacts. The out-of-plane blur caused by dense tissue and large masses complicates reconstruction of thick slices volumes. The streak-like out-of-plane artifacts caused by calcifications and metal clips distort the shape of calcifications which is regarded by many radiologists as an important malignancy predictor. Small clinical features such as micro-calcifications could be obscured by bright artifacts. The proposed technique involves reconstructing a set of super-resolution slices and predicting the artifact-free voxel intensity based on the corresponding set of projection pixels using a statistical model learned from a set of training data. Our experiments show that the resulting reconstructed images are de-blurred and streak-like artifacts are reduced, visibility of clinical features, contrast and sharpness are improved and thick-slice reconstruction is possible without the loss of contrast and sharpness. © 2012 Springer-Verlag Berlin Heidelberg.

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Abdurahman, S., Jerebko, A., Mertelmeier, T., Lasser, T., & Navab, N. (2012). Out-of-plane artifact reduction in tomosynthesis based on regression modeling and outlier detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7361 LNCS, pp. 729–736). https://doi.org/10.1007/978-3-642-31271-7_94

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