Deep learning framework for digital breast tomosynthesis reconstruction

Nikita Moriakov*, Koen Michielsen, Jonas Adler, Ritse Mann, Ioannis Sechopoulos, Jonas Teuwen

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

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 a€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.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationPhysics of Medical Imaging
EditorsTaly Gilat Schmidt, Guang-Hong Chen, Hilde Bosmans
PublisherSPIE
Number of pages7
Volume10948
ISBN (Electronic)978-151062543-3
DOIs
Publication statusPublished - 2019
EventMedical Imaging 2019: Physics of Medical Imaging - San Diego, United States
Duration: 17 Feb 201920 Feb 2019

Publication series

NameMEDICAL IMAGING 2019: PHYSICS OF MEDICAL IMAGING
ISSN (Print)0277-786X

Conference

ConferenceMedical Imaging 2019: Physics of Medical Imaging
Country/TerritoryUnited States
CitySan Diego
Period17/02/1920/02/19

Keywords

  • Breast cancer
  • Deep learning
  • Digital breast tomosynthesis
  • Primal-dual algorithm
  • Reconstruction

Fingerprint

Dive into the research topics of 'Deep learning framework for digital breast tomosynthesis reconstruction'. Together they form a unique fingerprint.

Cite this