Deep convolutional neural network regularized digital breast tomosynthesis reconstruction with detector blur and correlated noise modeling

Mingjie Gao; Jeffrey A. Fessler; Heang-Ping Chan

doi:10.1117/12.2611933

4 April 2022 Deep convolutional neural network regularized digital breast tomosynthesis reconstruction with detector blur and correlated noise modeling

Mingjie Gao, Jeffrey A. Fessler, Heang-Ping Chan

Author Affiliations +

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 1203108 (2022) https://doi.org/10.1117/12.2611933
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Digital breast tomosynthesis (DBT) reconstruction is an ill-posed inverse problem due to the limited-angle acquisition geometry. DBT is also a low dose imaging technique and has very noisy projection views. In this study, we investigated the feasibility of improving image quality of DBT reconstruction by combining (1) a model-based iterative reconstruction (MBIR) method that models the detector blur and correlated noise (DBCN) of the DBT system, and (2) a deep convolutional neural network based DBT denoiser, DNGAN, that we developed in our previous work. DBCN is physics-based whereas DNGAN is data-driven. We followed the regularization by denoising (RED) framework to construct a regularizer from DNGAN and used the DBCN-modeled terms in the MBIR formulation. We solved the optimization problem using the proximal gradient method. The proposed approach, named DBCN+DNGAN, was tested on a set of human subject DBT data sets. The image quality was evaluated quantitatively with figures of merit (FOMs) including the contrast-to-noise ratio, full width at half maximum, and task-based detectability index of a set of microcalcifications individually marked in the human subject data set. We found that these FOMs were improved in the DBCN+DNGAN-reconstructed DBT volumes compared to those reconstructed with DBCN alone or with the simultaneous algebraic reconstruction technique. The soft tissue appearance was visually satisfactory and the background noise level was low in the DBCN+DNGAN reconstructed images.

Conference Presentation

Citation Download Citation

Mingjie Gao, Jeffrey A. Fessler, and Heang-Ping Chan "Deep convolutional neural network regularized digital breast tomosynthesis reconstruction with detector blur and correlated noise modeling", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 1203108 (4 April 2022); https://doi.org/10.1117/12.2611933

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