A Physics-Informed Deep Neural Network for Harmonization of CT Images.

Journal: IEEE transactions on bio-medical engineering

Published Date: Nov 21, 2024

Abstract

OBJECTIVE: Computed Tomography (CT) quantification is affected by the variability in image acquisition and rendition. This paper aimed to reduce this variability by harmonizing the images utilizing physics-based deep neural networks (DNNs).

Authors

Mojtaba Zarei

Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran.
Saman Sotoudeh-Paima

Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Cindy McCabe
Ehsan Abadi
Ehsan Samei

Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.

Keywords

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Lung Lung Diseases Neural Networks, Computer Phantoms, Imaging Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39012733)

A Physics-Informed Deep Neural Network for Harmonization of CT Images.

Abstract

Authors

Keywords

External Resources

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