Literature DB >> 27587225

On the influence of zero-padding on the nonlinear operations in Quantitative Susceptibility Mapping.

Sarah Eskreis-Winkler1, Dong Zhou1, Tian Liu2, Ajay Gupta1, Susan A Gauthier3, Yi Wang1, Pascal Spincemaille4.   

Abstract

PURPOSE: Zero padding is a well-studied interpolation technique that improves image visualization without increasing image resolution. This interpolation is often performed as a last step before images are displayed on clinical workstations. Here, we seek to demonstrate the importance of zero padding before rather than after performing non-linear post-processing algorithms, such as Quantitative Susceptibility Mapping (QSM). To do so, we evaluate apparent spatial resolution, relative error and depiction of multiple sclerosis (MS) lesions on images that were zero padded prior to, in the middle of, and after the application of the QSM algorithm.
MATERIALS AND METHODS: High resolution gradient echo (GRE) data were acquired on twenty MS patients, from which low resolution data were derived using k-space cropping. Pre-, mid-, and post-zero padded QSM images were reconstructed from these low resolution data by zero padding prior to field mapping, after field mapping, and after susceptibility mapping, respectively. Using high resolution QSM as the gold standard, apparent spatial resolution, relative error, and image quality of the pre-, mid-, and post-zero padded QSM images were measured and compared.
RESULTS: Both the accuracy and apparent spatial resolution of the pre-zero padded QSM was higher than that of mid-zero padded QSM (p<0.001; p<0.001), which was higher than that of post-zero padded QSM (p<0.001; p<0.001). The image quality of pre-zero padded reconstructions was higher than that of mid- and post-zero padded reconstructions (p=0.004; p<0.001).
CONCLUSION: Zero padding of the complex GRE data prior to nonlinear susceptibility mapping improves image accuracy and apparent resolution compared to zero padding afterwards. It also provides better delineation of MS lesion geometry, which may improve lesion subclassification and disease monitoring in MS patients. Copyright Â
© 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Microstructure; Multiple sclerosis; Nonlinearity; Quantitative susceptibility mapping (QSM); Zero padding

Mesh:

Year:  2016        PMID: 27587225      PMCID: PMC5160043          DOI: 10.1016/j.mri.2016.08.020

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  18 in total

1.  Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy.

Authors:  M A Bernstein; S B Fain; S J Riederer
Journal:  J Magn Reson Imaging       Date:  2001-09       Impact factor: 4.813

2.  Improved depiction of small anatomic structures in MR images using Gaussian-weighted spirals and zero-filled interpolation.

Authors:  Rotem A Elgavish; Donald B Twieg
Journal:  Magn Reson Imaging       Date:  2003-02       Impact factor: 2.546

3.  Effects of zero-filled interpolation on cervical magnetic resonance angiographic measurement.

Authors:  Kirsten P Forbes; James G Pipe; John P Karis; Joseph E Heiserman
Journal:  AJNR Am J Neuroradiol       Date:  2003-03       Impact factor: 3.825

4.  Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping.

Authors:  Tian Liu; Cynthia Wisnieff; Min Lou; Weiwei Chen; Pascal Spincemaille; Yi Wang
Journal:  Magn Reson Med       Date:  2012-04-09       Impact factor: 4.668

5.  Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging.

Authors:  Tian Liu; Jing Liu; Ludovic de Rochefort; Pascal Spincemaille; Ildar Khalidov; James Robert Ledoux; Yi Wang
Journal:  Magn Reson Med       Date:  2011-04-04       Impact factor: 4.668

6.  Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map.

Authors:  Jing Liu; Tian Liu; Ludovic de Rochefort; James Ledoux; Ildar Khalidov; Weiwei Chen; A John Tsiouris; Cynthia Wisnieff; Pascal Spincemaille; Martin R Prince; Yi Wang
Journal:  Neuroimage       Date:  2011-09-08       Impact factor: 6.556

7.  Reduction of partial-volume artifacts with zero-filled interpolation in three-dimensional MR angiography.

Authors:  Y P Du; D L Parker; W L Davis; G Cao
Journal:  J Magn Reson Imaging       Date:  1994 Sep-Oct       Impact factor: 4.813

8.  A novel background field removal method for MRI using projection onto dipole fields (PDF).

Authors:  Tian Liu; Ildar Khalidov; Ludovic de Rochefort; Pascal Spincemaille; Jing Liu; A John Tsiouris; Yi Wang
Journal:  NMR Biomed       Date:  2011-03-08       Impact factor: 4.044

9.  Iron and neurodegeneration in multiple sclerosis.

Authors:  Michael Khalil; Charlotte Teunissen; Christian Langkammer
Journal:  Mult Scler Int       Date:  2011-02-10

10.  Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions.

Authors:  Veela Mehta; Wei Pei; Grant Yang; Suyang Li; Eashwar Swamy; Aaron Boster; Petra Schmalbrock; David Pitt
Journal:  PLoS One       Date:  2013-03-14       Impact factor: 3.240

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  2 in total

1.  Rapid automated liver quantitative susceptibility mapping.

Authors:  Ramin Jafari; Sujit Sheth; Pascal Spincemaille; Thanh D Nguyen; Martin R Prince; Yan Wen; Yihao Guo; Kofi Deh; Zhe Liu; Daniel Margolis; Gary M Brittenham; Andrea S Kierans; Yi Wang
Journal:  J Magn Reson Imaging       Date:  2019-01-13       Impact factor: 4.813

2.  Disease correlates of rim lesions on quantitative susceptibility mapping in multiple sclerosis.

Authors:  Melanie Marcille; Sandra Hurtado Rúa; Charles Tyshkov; Abhishek Jaywant; Joseph Comunale; Ulrike W Kaunzner; Nancy Nealon; Jai S Perumal; Lily Zexter; Nicole Zinger; Olivia Bruvik; Yi Wang; Elizabeth Sweeney; Amy Kuceyeski; Thanh D Nguyen; Susan A Gauthier
Journal:  Sci Rep       Date:  2022-03-15       Impact factor: 4.379

  2 in total

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