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Proceedings Paper

Evaluation and optimization of the maximum-likelihood approach for image reconstruction in digital breast tomosynthesis
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Paper Abstract

Digital Breast Tomosynthesis (DBT) suffers from incomplete data and poor quantum statistics limited by the total dose absorbed in the breast. Hence, statistical reconstruction assuming the photon statistics to follow a Poisson distribution may have some advantages. This study investigates state-of-art iterative maximum likelihood (ML) statistical reconstruction algorithms for DBT and compares the results with simple backprojection (BP), filtered backprojection (FBP), and iFBP (FBP with filter derived from iterative reconstruction). The gradient-ascent and convex optimization variants of the transmission ML algorithm are evaluated with phantom and clinical data. Convergence speed is very similar for both iterative statistical algorithms and after approximately 5 iterations all significant details are well displayed, although we notice increasing noise. We found empirically that a relaxation factor between 0.25 and 0.5 provides the optimal trade-off between noise and contrast. The ML-convex algorithm gives smoother results than the ML-gradient algorithm. The low-contrast CNR of the ML algorithms is between CNR for simple backprojection (highest) and FBP (lowest). Spatial resolution of iterative statistical and iFBP algorithms is similar to that of FBP but the quantitative density representation better resembles conventional mammograms. The iFBP algorithm provides the benefits of statistical iterative reconstruction techniques and requires much shorter computation time.

Paper Details

Date Published: 18 March 2010
PDF: 9 pages
Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 76220E (18 March 2010); doi: 10.1117/12.844177
Show Author Affiliations
Anna K. Jerebko, Siemens AG (Germany)
Thomas Mertelmeier, Siemens AG (Germany)

Published in SPIE Proceedings Vol. 7622:
Medical Imaging 2010: Physics of Medical Imaging
Ehsan Samei; Norbert J. Pelc, Editor(s)

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