Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT
Abstract
Purpose: We developed a deep learning method to reduce noise and beam-hardening artifact in virtual monoenergetic image (VMI) at low x-ray energy levels. Approach: An encoder-decoder type convolutional neural network was implemented with customized inception modules and in-house-designed training loss (denoted as Incept-net), to directly estimate VMI from multi-energy CT images. Images of an abdomen-sized water phantom with varying insert...
Paper Details
Title
Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT
Published Date
Apr 19, 2021
Journal
Volume
8
Issue
05
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