Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow-Editorial for the 2nd Special Issue "Machine Learning for Image Reconstruction"

Ge Wang; Mathews Jacob; Xuanqin Mou; Yongyi Shi; Yonina C Eldar

doi:10.1109/TMI.2021.3115547

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Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow-Editorial for the 2nd Special Issue "Machine Learning for Image Reconstruction"

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Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow-Editorial for the 2nd Special Issue "Machine Learning for Image Reconstruction"

Ge Wang, Mathews Jacob, Xuanqin Mou, Yongyi Shi and Yonina C Eldar

IEEE transactions on medical imaging, Vol.40(11), pp.2956-2964

11/2021

DOI: 10.1109/TMI.2021.3115547

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Abstract

As a follow-up to the first IEEE Transactions on Medical Imaging (TMI) special issue on the theme of deep tomographic reconstruction, the second special issue is assembled to reflect the status and momentum of this rapidly emerging field. In this editorial, we provide a brief background illustrating the motivation for the development of network-based, data-driven, and learning-oriented reconstruction methods, summarize the included papers, and report our verification of the shared deep learning codes. Finally, we discuss several important research topics to facilitate further investigation and collaboration.

artificial intelligence

deep learning

deep reconstruction

image reconstruction

machine learning

Tomography

Details

Title: Subtitle: Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow-Editorial for the 2nd Special Issue "Machine Learning for Image Reconstruction"
Creators: Ge Wang - Rensselaer Polytechnic Institute
Mathews Jacob - University of Iowa
Xuanqin Mou - Xi'an Jiaotong University
Yongyi Shi - Xi'an Jiaotong University
Yonina C Eldar - Weizmann Institute of Science
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.40(11), pp.2956-2964
DOI: 10.1109/TMI.2021.3115547
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 11/2021
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984187846602771

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