Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain

• URL: http://arxiv.org/abs/2203.11722v1
• Date: Tue, 22 Mar 2022 13:31:47 GMT
• Title: Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain
• Authors: Rodrigo de Barros Vimieiro and Chuang Niu and Hongming Shan and Lucas Rodrigues Borges and Ge Wang and Marcelo Andrade da Costa Vieira
• Abstract summary: convolution neural network (CNN) proposed to restore low-dose (LD) projections to image quality equivalent to a standard full-dose (FD) acquisition. Network achieved superior results in terms of the mean squared error (MNSE), normalized training time and noise spatial correlation compared with networks trained with traditional data-driven methods.
• Score: 15.149874383250236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital breast tomosynthesis (DBT) exams should utilize the lowest possible radiation dose while maintaining sufficiently good image quality for accurate medical diagnosis. In this work, we propose a convolution neural network (CNN) to restore low-dose (LD) DBT projections to achieve an image quality equivalent to a standard full-dose (FD) acquisition. The proposed network architecture benefits from priors in terms of layers that were inspired by traditional model-based (MB) restoration methods, considering a model-based deep learning approach, where the network is trained to operate in the variance stabilization transformation (VST) domain. To accurately control the network operation point, in terms of noise and blur of the restored image, we propose a loss function that minimizes the bias and matches residual noise between the input and the output. The training dataset was composed of clinical data acquired at the standard FD and low-dose pairs obtained by the injection of quantum noise. The network was tested using real DBT projections acquired with a physical anthropomorphic breast phantom. The proposed network achieved superior results in terms of the mean normalized squared error (MNSE), training time and noise spatial correlation compared with networks trained with traditional data-driven methods. The proposed approach can be extended for other medical imaging application that requires LD acquisitions.

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