Assessing detectability improvement of a self-supervised noise reduction algorithm for phase-sensitive breast tomosynthesis phantom images
Jared Nelson,
Xuxin Chen,
Yuhua Li
et al.
Abstract:This study aims to investigate the effectiveness of a self-supervised deep learning based noise reduction algorithm at improving the detectability of phantom images acquired from the phase-sensitive breast tomosynthesis (PBT) system.An ACR mammography phantom and three different Contrast Detail (CD) phantoms were used in experiments. Each phantom is 5cm in thickness and fabricated with materials simulating 50% glandular tissue and 50% adipose tissue. The phantoms were imaged by 59kV and 89kV with varying level… Show more
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