Machine learning-based dual-energy CT parametric mapping-Reference-Cited by-同舟云学术

Machine learning-based dual-energy CT parametric mapping

Published:2018-06-08 Issue:12 Volume:63 Page:125001
ISSN:1361-6560
Container-title:Physics in Medicine & Biology
language:
Short-container-title:Phys. Med. Biol.

Author:

Su Kuan-Hao^ORCID,Kuo Jung-Wen,Jordan David W^ORCID,Van Hedent Steven,Klahr Paul,Wei Zhouping,Al Helo Rose,Liang Fan,Qian Pengjiang,Pereira Gisele C,Rassouli Negin,Gilkeson Robert C,Traughber Bryan J,Cheng Chee-Wai,Muzic Raymond F

Funder

Ohio Third Frontier research grant

Publisher

IOP Publishing

Subject

Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://iopscience.iop.org/article/10.1088/1361-6560/aac711/pdf

Reference59 articles.

1. Dual-energy CT quantitative imaging: a comparison study between twin-beam and dual-source CT scanners

2. Energy-selective reconstructions in X-ray computerised tomography

3. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

4. The potential of dual-energy CT to reduce proton beam range uncertainties

5. Dual-energy CT-based material extraction for tissue segmentation in Monte Carlo dose calculations

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