Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning-Reference-Cited by-同舟云学术

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Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning

Published:2018 Issue: Volume: Page:61-69
ISSN:0302-9743
Container-title:Understanding and Interpreting Machine Learning in Medical Image Computing Applications
language:
Short-container-title:

Author:

Clough James R.,Balfour Daniel R.,Prieto Claudia,Reader Andrew J.,Marsden Paul K.,King Andrew P.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-02628-8_7

Reference17 articles.

1. Balfour, D.R., et al.: Respiratory motion correction of PET using MR-constrained PET-PET registration. Biomed. Eng. Online 14(1), 85 (2015)

2. Baumgartner, C.F., et al.: High-resolution dynamic MR imaging of the thorax for respiratory motion correction of PET using groupwise manifold alignment. Med. Image Anal. 18(7), 939–952 (2014)

3. Lecture Notes in Computer Science;A Ben-Cohen,2017

4. Chen, C., et al.: Learning to see in the dark. arXiv preprint arXiv:1805.01934 (2018)

5. Freeman, M.F., Tukey, J.W.: Transformations related to the angular and the square root. Ann. Math. Stat. 21(4), 607–611 (1950)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Motion in nuclear cardiology imaging: types, artifacts, detection and correction techniques;Physics in Medicine & Biology;2022-01-17

2. Motion estimation and correction in SPECT, PET and CT;Physics in Medicine & Biology;2021-09-15

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