Deep ADMM-Net for compressive sensing MRI

564 indexed citations

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This paper, published in 2016, received 564 indexed citations. Written by Yan Yang, Jian Sun, Huibin Li and Zongben Xu covering the research area of Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Computational Mechanics. It is primarily cited by scholars working on Radiology, Nuclear Medicine and Imaging (280 citations), Computational Mechanics (226 citations) and Computer Vision and Pattern Recognition (205 citations). Published in Neural Information Processing Systems.

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Countries where authors are citing Deep ADMM-Net for compressive sensing MRI

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Citations

This map shows the geographic impact of Deep ADMM-Net for compressive sensing MRI. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep ADMM-Net for compressive sensing MRI with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep ADMM-Net for compressive sensing MRI more than expected).

Fields of papers citing Deep ADMM-Net for compressive sensing MRI

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Deep ADMM-Net for compressive sensing MRI. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep ADMM-Net for compressive sensing MRI.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w3803030.

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