Deep learning enabled high-speed image reconstruction for breast diffuse optical tomography

Bin Deng; Hanxue Gu; Stefan A. Carp

doi:10.1117/12.2577736

5 March 2021 Deep learning enabled high-speed image reconstruction for breast diffuse optical tomography

Bin Deng, Hanxue Gu, Stefan A. Carp

Proceedings Volume 11639, Optical Tomography and Spectroscopy of Tissue XIV; 116390B (2021) https://doi.org/10.1117/12.2577736
Event: SPIE BiOS, 2021, Online Only

Abstract

In this paper, we describe a deep convolutional neural network (DNN) model trained with simulated breast diffuse optical tomography data with realistic noise characteristics to solve the inverse problem in a fast single-pass feed forward reconstruction. In addition to an AUTOMAP-inspired network structure, our DNN model, a.k.a. FDU-Net, is also comprised of a U-Net to further improve the image quality. We demonstrate that our FDU-Net model can successfully recover nearly the full contrast of inclusions with accurate localization at millisecond-scale speed, outperforming the conventional finite element-based (FEM) methods. Trained with cases with a single spherical inclusion, the FDU-Net model can also recover multi-inclusions and irregular-shaped cases, demonstrating advantages of generalization.

Conference Presentation

Citation Download Citation

Bin Deng, Hanxue Gu, and Stefan A. Carp "Deep learning enabled high-speed image reconstruction for breast diffuse optical tomography", Proc. SPIE 11639, Optical Tomography and Spectroscopy of Tissue XIV, 116390B (5 March 2021); https://doi.org/10.1117/12.2577736

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