Abstract
The method presented in this paper addresses the problem of regularization parameter selection in maximum a posteriori iterative reconstruction for digital breast tomosynthesis. The method allows analytically deriving the combination of prior function parameters for noise level expected in the reconstruction without priors and estimated breast density such that it effectively controls the level of noise while preserving the edges of breast structures. Results show reduced noise level and improved contrast to noise ratio compared to filtered back projection and maximum–likelihood iterative reconstruction without penalizing term.
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Jerebko, A.K., Kowarschik, M., Mertelmeier, T. (2010). Regularization Parameter Selection in Maximum a Posteriori Iterative Reconstruction for Digital Breast Tomosynthesis. In: MartÃ, J., Oliver, A., Freixenet, J., MartÃ, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol 6136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_74
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DOI: https://doi.org/10.1007/978-3-642-13666-5_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13665-8
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