Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT

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 lo...

Popoln opis

Shranjeno v:
Bibliografske podrobnosti
izdano v:J Med Imaging (Bellingham)
Main Authors: Gong, Hao, Marsh, Jeffrey F., D’Souza, Karen N., Huber, Nathan R., Rajendran, Kishore, Fletcher, Joel G., McCollough, Cynthia H., Leng, Shuai
Format: Artigo
Jezik:Inglês
Izdano: Society of Photo-Optical Instrumentation Engineers 2021
Teme:
Special Section Celebrating X-Ray Computed Tomography at 50
Online dostop:https://ncbi.nlm.nih.gov/pmc/articles/PMC8054272/
https://ncbi.nlm.nih.gov/pubmed/33889658
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1117/1.JMI.8.5.052104
Oznake: Označite
Brez oznak, prvi označite!

Podobne knjige/članki