scholarly journals A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction

2003 ◽  
Vol 19 (2) ◽  
pp. 96-111 ◽  
Author(s):  
Fa-Hsuan Lin ◽  
Ying-Jui Chen ◽  
John W. Belliveau ◽  
Lawrence L. Wald
Keyword(s):  
Parallel Imaging ◽  
Surface Coil ◽  
Intensity Inhomogeneity ◽  
Coil Sensitivity ◽  
Image Intensity ◽  
Imaging Reconstruction

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing

2013 ◽  
Vol 71 (2) ◽  
pp. 645-660 ◽  
Author(s):  
Huajun She ◽  
Rong-Rong Chen ◽  
Dong Liang ◽  
Edward V. R. DiBella ◽  
Leslie Ying
Keyword(s):  
Compressed Sensing ◽  
Parallel Imaging ◽  
Imaging Reconstruction

Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

2021 ◽  
pp. 1-12
Author(s):  
Lin Wu ◽  
Tian He ◽  
Jie Yu ◽  
Hang Liu ◽  
Shuang Zhang ◽  
...  
Keyword(s):  
Bias Field ◽  
Correction Method ◽  
Surface Coil ◽  
Intensity Inhomogeneity ◽  
Acquisition Time ◽  
Surface Coils ◽  
In Vivo Experiments ◽  
Inhomogeneity Correction ◽  
Volume Coil

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.

scholarly journals Parallel imaging reconstruction for arbitrary trajectories usingk-space sparse matrices (kSPA)

2007 ◽  
Vol 58 (6) ◽  
pp. 1171-1181 ◽  
Author(s):  
Chunlei Liu ◽  
Roland Bammer ◽  
Michael E. Moseley
Keyword(s):  
Parallel Imaging ◽  
Sparse Matrices ◽  
Imaging Reconstruction

scholarly journals Non-Cartesian parallel imaging reconstruction

2014 ◽  
Vol 40 (5) ◽  
pp. 1022-1040 ◽  
Author(s):  
Katherine L. Wright ◽  
Jesse I. Hamilton ◽  
Mark A. Griswold ◽  
Vikas Gulani ◽  
Nicole Seiberlich
Keyword(s):  
Parallel Imaging ◽  
Imaging Reconstruction

scholarly journals Autocalibrated coil sensitivity estimation for parallel imaging

2006 ◽  
Vol 19 (3) ◽  
pp. 316-324 ◽  
Author(s):  
Mark A. Griswold ◽  
Felix Breuer ◽  
Martin Blaimer ◽  
Stephan Kannengiesser ◽  
Robin M. Heidemann ◽  
...  
Keyword(s):  
Parallel Imaging ◽  
Coil Sensitivity ◽  
Sensitivity Estimation
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