Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain-Reference-Cited by-同舟云学术

Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain

Published:2023-06 Issue:6 Volume:42 Page:1835-1845
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Chang Shaojie¹^ORCID,Gao Yongfeng¹^ORCID,Pomeroy Marc J.²^ORCID,Bai Ti³^ORCID,Zhang Hao⁴,Lu Siming²,Pickhardt Perry J.⁵,Gupta Amit¹^ORCID,Reiter Michael J.¹,Gould Elaine S.¹,Liang Zhengrong²^ORCID

Affiliation:

1. Department of Radiology, Stony Brook University, Stony Brook, NY, USA

2. Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA

3. Department of Radiation Oncology, University of Texas Southwestern Medical Centre, Dallas, TX, USA

4. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, York Ave, NY, USA

5. Department of Radiology, School of Medicine, University of Wisconsin, Madison, WI, USA

Funder

NIH/National Cancer Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10142147/10032836.pdf?arnumber=10032836

Reference35 articles.

1. Improved Material Decomposition With a Two-Step Regularization for Spectral CT

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3. Accuracies of the synthesized monochromatic CT numbers and effective atomic numbers obtained with a rapid kVp switching dual energy CT scanner

4. Characterization of tissue‐specific pre‐log Bayesian CT reconstruction by texture–dose relationship

5. Iterative image-domain decomposition for dual-energy CT

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