Image Reconstruction is a New Frontier of Machine Learning

By Ge Wang, Jong Chul Ye, Klaus Mueller, Jeffrey A. Fessler. IEEE Transactions on Medical Imaging, 2018

433 citations recorded by the source corpus.

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of “Machine learning for image reconstruction.” This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme “Deep learning in medical imaging” [item 2) in the Appendix]. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4e75a145f59400a8d646db770cc396de87d6b9d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Mannudeep K. Kalra
2. Yi Zhang
3. Uwe Kruger
4. Hongming Shan
5. Wenxiang Cong
6. Ge Wang
7. Yunjie Yang
8. A. Gholipour

In Radiology, Nuclear Medicine and Imaging

1. Yi Zhang
2. Mannudeep K. Kalra
3. Uwe Kruger
4. Hongming Shan
5. A. Gholipour
6. Wenxiang Cong
7. Ge Wang
8. Yunjie Yang

Citations in the corpus

• Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, background): “level image processing tasks such as denoising, enhancement, and reconstruction [6], to more high-level image analysis tasks such as segmentation, detection, classification, and registration [7], [8], and even more challenging tasks such as discovering links between the content of medical images and”
• FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging (2020, background): “In addition to these model-based approaches, machine learning, particularly deep learning, has recently become a new frontier of inverse problems in imaging [12].”
• CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018, background): “In the medical imaging field, DL is an emerging approach which has exhibited a great potential [35]–[37].”
• Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction (2018, background): “Recently, deep learning techniques have attracted a major attention for tomographic image reconstruction, which is a new area of research [1, 2, 3].”