Digital breast tomosynthesis denoising using deep convolutional neural network: effects of dose level of training target images

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

doi:10.1117/12.2580900

15 February 2021 Digital breast tomosynthesis denoising using deep convolutional neural network: effects of dose level of training target images

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

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115951K (2021) https://doi.org/10.1117/12.2580900
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

This paper investigates the training of deep convolutional neural networks (DCNNs) to denoise digital breast tomosynthesis (DBT) images. In our approach, the DCNN was trained with high dose (HD)/low dose (LD) image pairs, in which the HD image was used as reference to guide the DCNN to learn to reduce noise of a corresponding input LD image. In the current work, we studied the effect of the dose level of the HD target images on the effectiveness of the trained denoiser. We generated a set of simulated DBT data with different dose levels using CatSim, a dedicated x-ray imaging simulator, in combination with virtual anthropomorphic breast phantoms simulated by VICTRE. We found that a DCNN trained with higher dose level of HD target images led to less noisy denoised images. We also acquired DBT images of real physical phantoms containing simulated microcalcifications (MCs) for training and validation. The denoisers trained with either simulated or physical phantom data improved significantly (𝑝𝑝 < 0.0001) the contrast-tonoise ratio of MCs in the validation phantom images. In an independent test set of human subject DBTs, the MCs became more conspicuous, and the mass margins and spiculations were well preserved. The study showed that the denoising DCNN was robust in that the denoiser could be trained with either simulation or physical phantom data. Moreover, the denoiser trained with CatSim simulation data was directly applicable to human DBTs, allowing flexibility in terms of the training data preparation, especially the HD images.

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Mingjie Gao, Jeffrey A. Fessler, and Heang-Ping Chan "Digital breast tomosynthesis denoising using deep convolutional neural network: effects of dose level of training target images", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115951K (15 February 2021); https://doi.org/10.1117/12.2580900

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