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Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
Radboud Univ Nijmegen, Med Ctr, Diagnost Image Anal Grp, Dept Radiol & Nucl Med, Nijmegen, Netherlands..
Radboud Univ Nijmegen, Med Ctr, Diagnost Image Anal Grp, Dept Radiol & Nucl Med, Nijmegen, Netherlands..
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.). Elekta, Res & Phys, Stockholm, Sweden..ORCID iD: 0000-0001-9928-3407
Radboud Univ Nijmegen, Med Ctr, Diagnost Image Anal Grp, Dept Radiol & Nucl Med, Nijmegen, Netherlands..
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2019 (English)In: MEDICAL IMAGING 2019: PHYSICS OF MEDICAL IMAGING / [ed] Schmidt, TG Chen, GH Bosmans, H, SPIE-INT SOC OPTICAL ENGINEERING , 2019, article id 1094804Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
SPIE-INT SOC OPTICAL ENGINEERING , 2019. article id 1094804
Series
Proceedings of SPIE, ISSN 0277-786X ; 10948
Keywords [en]
deep learning, digital breast tomosynthesis, primal-dual algorithm, breast cancer, reconstruction
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-260225DOI: 10.1117/12.2512912ISI: 000483585700003Scopus ID: 2-s2.0-85068378440ISBN: 978-1-5106-2544-0 (print)OAI: oai:DiVA.org:kth-260225DiVA, id: diva2:1355710
Conference
Conference on Medical Imaging - Physics of Medical Imaging, FEB 17-20, 2019, San Diego, CA
Note

QC 20190930

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-09-30Bibliographically approved

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Adler, Jonas

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