Share Email Print

Proceedings Paper

Deep learning framework for digital breast tomosynthesis reconstruction
Author(s): Nikita Moriakov; Koen Michielsen; Jonas Adler; Ritse Mann M.D.; Ioannis Sechopoulos; Jonas Teuwen
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular range gives rise to different artifacts, which manufacturers try to solve in several ways. In this study we propose an extension of the Learned Primal- Dual algorithm for digital breast tomosynthesis. The Learned Primal-Dual algorithm is a deep neural network consisting of several ‘reconstruction blocks’, which take in raw sinogram data as the initial input, perform a forward and a backward pass by taking projections and back-projections, and use a convolutional neural network to produce an intermediate reconstruction result which is then improved further by the successive reconstruction block. We extend the architecture by providing breast thickness measurements as a mask to the neural network and allow it to learn how to use this thickness mask. We have trained the algorithm on digital phantoms and the corresponding noise-free/noisy projections, and then tested the algorithm on digital phantoms for varying level of noise. Reconstruction performance of the algorithms was compared visually, using MSE loss and Structural Similarity Index. Results indicate that the proposed algorithm outperforms the baseline iterative reconstruction algorithm in terms of reconstruction quality for both breast edges and internal structures and is robust to noise.

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094804 (1 March 2019); doi: 10.1117/12.2512912
Show Author Affiliations
Nikita Moriakov, Radboud Univ. Medical Ctr. (Netherlands)
Koen Michielsen, Radboud Univ. Medical Ctr. (Netherlands)
Jonas Adler, KTH Royal Institute of Technology (Sweden)
Research and Physics (Sweden)
Ritse Mann M.D., Radboud Univ. Medical Ctr. (Netherlands)
Ioannis Sechopoulos, Radboud Univ. Medical Ctr. (Netherlands)
Dutch Expert Ctr. for Screening (Netherlands)
Jonas Teuwen, Radboud Univ. Medical Ctr. (Netherlands)
Delft Univ. of Technology (Netherlands)

Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?