A benchmark of denoising digital breast tomosynthesis in projection domain: neural network-based and traditional methods-Reference-Cited by-同舟云学术

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A benchmark of denoising digital breast tomosynthesis in projection domain: neural network-based and traditional methods

Published:2022-04-04 Issue: Volume: Page:
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Container-title:Medical Imaging 2022: Image Processing
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Author:

Nakamura de Araújo Darlan M.,Salvadeo Denis H. P.,de Paula Davi D.

Publisher

SPIE

Reference28 articles.

1. Breast Tomosynthesis: Present Considerations and Future Applications

2. The alara principle in medical imaging;Miller;philosophy,1983

3. Feasibility study of dose reduction in digital breast tomosynthesis using non-local denoising algorithms;Vieira;Medical Imaging 2015: Physics of Medical Imaging,2015

4. Pipeline for effective denoising of digital mammography and digital breast tomosynthesis;Borges;Medical Imaging 2017: Physics of Medical Imaging,2017

5. Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Denoising digital breast tomosynthesis projections using deep learning with synthetic data as training set;Journal of Medical Imaging;2023-05-22

2. Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images;Medical Physics;2023-05-05

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