Abstract:
PURPOSE:Gradient imperfections remain a challenge in MRI, especially for sequences relying on long imaging readouts. This work aims to explore image reconstruction based on k-space trajectories predicted by an impulse response model of the gradient system. THEORY AND METHODS:Gradient characterization was performed twice with 3 years interval on a commercial 3 Tesla (T) system. The measured gradient impulse response functions were used to predict actual k-space trajectories for single-shot echo-planar imaging (EPI), spiral and variable-speed EPI sequences. Image reconstruction based on the predicted trajectories was performed for phantom and in vivo data. Resulting images were compared with reconstructions based on concurrent field monitoring, separate trajectory measurements, and nominal trajectories. RESULTS:Image reconstruction using model-based trajectories yielded high-quality images, comparable to using separate trajectory measurements. Compared with using nominal trajectories, it strongly reduced ghosting, blurring, and geometric distortion. Equivalent image quality was obtained with the recent characterization and that performed 3 years prior. CONCLUSION:Model-based trajectory prediction enables high-quality image reconstruction for technically challenging sequences such as single-shot EPI and spiral imaging. It thus holds great promise for fast structural imaging and advanced neuroimaging techniques, including functional MRI, diffusion tensor imaging, and arterial spin labeling. The method can be based on a one-time system characterization as demonstrated by successful use of 3-year-old calibration data. Magn Reson Med 76:45-58, 2016. © 2015 Wiley Periodicals, Inc.
journal_name
Magn Reson Medjournal_title
Magnetic resonance in medicineauthors
Vannesjo SJ,Graedel NN,Kasper L,Gross S,Busch J,Haeberlin M,Barmet C,Pruessmann KPdoi
10.1002/mrm.25841subject
Has Abstractpub_date
2016-07-01 00:00:00pages
45-58issue
1eissn
0740-3194issn
1522-2594journal_volume
76pub_type
杂志文章abstract:PURPOSE:To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. METHODS:A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models we...
journal_title:Magnetic resonance in medicine
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journal_title:Magnetic resonance in medicine
pub_type: 杂志文章
doi:10.1002/mrm.1910030221
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journal_title:Magnetic resonance in medicine
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doi:10.1002/mrm.1910360607
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pub_type: 临床试验,杂志文章
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journal_title:Magnetic resonance in medicine
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doi:10.1002/mrm.21648
更新日期:2008-07-01 00:00:00
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更新日期:2020-01-01 00:00:00
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journal_title:Magnetic resonance in medicine
pub_type: 杂志文章
doi:10.1002/mrm.1910400204
更新日期:1998-08-01 00:00:00
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journal_title:Magnetic resonance in medicine
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更新日期:2004-04-01 00:00:00
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更新日期:2017-08-01 00:00:00
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更新日期:1996-10-01 00:00:00
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