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Special Issue on Machine Learning for Image Reconstruction, June 2018

Special Issue Editors: G. Wang, J. C. Ye, K. Mueller, and J. A. Fessler
IEEE Trans Med Imaging, vol. 37, issue 6, June 2018

This is the first special issue dedicated to the theme of "Machine Learning for Image Reconstruction". Computer vision and image analysis are great examples of machine learning, especially deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic image reconstruction produces images of internal structures from measurement data, which are various features (attenuated/non-attenuated line integrals, Fourier/harmonic components, echoed/scattered/transmitted ultrasound signatures, diffused/excited/interfered light signals, and so on) of the underlying images (features to images). Recently, machine learning, especially deep learning, techniques are being actively developed worldwide for tomographic image reconstruction, which has become a new area of research as evidenced by the 20 high-quality papers included in this special issue, as well as similar publications in other journals and conferences. In addition to well-known analytic and iterative methods for tomographic image reconstruction, machine learning is an emerging approach for image reconstruction, and likewise image reconstruction is a new frontier of machine learning. There are exciting research and application opportunities ahead for smart imaging and precision medicine.