Turboprop IDEAL: A motion-resistant fat-water separation technique
Suppression of the fat signal in MRI is very important for many clinical applications. Multi‐point water–fat separation methods, such as IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least‐squares estimation), can robustly separate water and fat signal, but inevitably incre...
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Published in | Magnetic resonance in medicine Vol. 61; no. 1; pp. 188 - 195 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.01.2009
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Subjects | |
Online Access | Get full text |
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Summary: | Suppression of the fat signal in MRI is very important for many clinical applications. Multi‐point water–fat separation methods, such as IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least‐squares estimation), can robustly separate water and fat signal, but inevitably increase scan time, making separated images more easily affected by patient motions. PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) and Turboprop techniques offer an effective approach to correct for motion artifacts. By combining these techniques together, we demonstrate that the new TP‐IDEAL method can provide reliable water–fat separation with robust motion correction. The Turboprop sequence was modified to acquire source images, and motion correction algorithms were adjusted to assure the registration between different echo images. Theoretical calculations were performed to predict the optimal shift and spacing of the gradient echoes. Phantom images were acquired, and results were compared with regular FSE‐IDEAL. Both T1‐ and T2‐weighted images of the human brain were used to demonstrate the effectiveness of motion correction. TP‐IDEAL images were also acquired for pelvis, knee, and foot, showing great potential of this technique for general clinical applications. Magn Reson Med 61:188–195, 2009. © 2008 Wiley‐Liss, Inc. |
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Bibliography: | ark:/67375/WNG-5V5Z0BBR-2 GE Healthcare ArticleID:MRM21825 istex:D0995801CF2058D5379C064B3EE4270CE16BED8A ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0740-3194 1522-2594 |
DOI: | 10.1002/mrm.21825 |