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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한자기공명의과학회 Investigative Magnetic Resonance Imaging Investigative Magnetic Resonance Imaging 제24권 제4호
- 발행연도
- 2020.1
- 수록면
- 196 - 206 (11page)
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With the development of deep-learning techniques, the application of deep learning in MR imaging processing seems to be growing. Accordingly, deep learning has also been introduced in motion correction and seemed to work as well as do conventional motion-compensation methods. In this article, we review the motion-correction methods based on deep learning, focusing especially on the motion-simulation methods adopted. We then propose a new motion-simulation tool, which we call view2Dmotion.
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참고문헌 (53)
참고문헌 신청
Zaitsev M / 2015 / Motion artifacts in MRI : a complex problem with many partial solutions / J Magn Reson Imaging 42:887~901
Heiland S / 2008 / From A as in aliasing to Z as in zipper : artifacts in MRI / Clin Neuroradiol 18:25~36
Stucht D / 2015 / Highest resolution in vivo human brain MRI using prospective motion correction / PLoS One 10:e0133921
Gallichan D / 2016 / Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators(3D FatNavs)at 7T / Magn Reson Med 75:1030~1039
Stadler A / 2007 / Artifacts in body MR imaging : their appearance and how to eliminate them / Eur Radiol 17:1242~1255
Heiland S / 2008 / From A as in aliasing to Z as in zipper : artifacts in MRI / Clin Neuroradiol 18:25~36
Stucht D / 2015 / Highest resolution in vivo human brain MRI using prospective motion correction / PLoS One 10:e0133921
Gallichan D / 2016 / Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators(3D FatNavs)at 7T / Magn Reson Med 75:1030~1039
Stadler A / 2007 / Artifacts in body MR imaging : their appearance and how to eliminate them / Eur Radiol 17:1242~1255
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