Literature DB >> 18068927

Automatic correction of in-plane bulk motion artifacts in self-navigated radial MRI.

Sungheon Kim1, Lawrence Dougherty, Mark A Rosen, Hee Kwon Song, Harish Poptani.   

Abstract

Radial MRI is typically used for scans that are sensitive to unavoidable motion. While the translational motion artifact can be easily removed from the radial trajectory data by phase correction, correction of rotational motion still remains a challenge in radial MRI. We present a novel method to refocus the image corrupted by view-to-view motion in the view-interleaved radial MRI data. In this method, the error in rotational view angles was modeled as a polynomial function of the view order and the model parameters were estimated by minimizing the self-navigator image metrics such as image entropy, gradient entropy, normalized gradient squared and mean square difference. Translational motion correction was conducted by aligning the projection profiles. Simulation studies were conducted to demonstrate the robustness of both translational and rotational motion correction methods in different noise levels. The proposed method was successfully applied to correct for motion of healthy subjects. Substantial motion correction with relative error of less than 5% was achieved by using either first- or second-order model with the image metrics. This study demonstrates the potential of the method for motion-sensitive applications.

Mesh:

Year:  2008        PMID: 18068927     DOI: 10.1016/j.mri.2007.08.001

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


  6 in total

1.  Prediction of response to chemoradiation therapy in squamous cell carcinomas of the head and neck using dynamic contrast-enhanced MR imaging.

Authors:  S Kim; L A Loevner; H Quon; A Kilger; E Sherman; G Weinstein; A Chalian; H Poptani
Journal:  AJNR Am J Neuroradiol       Date:  2009-10-01       Impact factor: 3.825

2.  Prediction of disease-free survival in patients with squamous cell carcinomas of the head and neck using dynamic contrast-enhanced MR imaging.

Authors:  S Chawla; S Kim; L A Loevner; W-T Hwang; G Weinstein; A Chalian; H Quon; H Poptani
Journal:  AJNR Am J Neuroradiol       Date:  2011-02-24       Impact factor: 3.825

3.  Adaptive retrospective correction of motion artifacts in cranial MRI with multicoil three-dimensional radial acquisitions.

Authors:  Ashley G Anderson; Julia Velikina; Walter Block; Oliver Wieben; Alexey Samsonov
Journal:  Magn Reson Med       Date:  2012-07-03       Impact factor: 4.668

4.  Pretreatment diffusion-weighted and dynamic contrast-enhanced MRI for prediction of local treatment response in squamous cell carcinomas of the head and neck.

Authors:  Sanjeev Chawla; Sungheon Kim; Lawrence Dougherty; Sumei Wang; Laurie A Loevner; Harry Quon; Harish Poptani
Journal:  AJR Am J Roentgenol       Date:  2013-01       Impact factor: 3.959

5.  Assessment of tumor treatment response using active contrast encoding (ACE)-MRI: Comparison with conventional DCE-MRI.

Authors:  Jin Zhang; Kerryanne Winters; Karl Kiser; Mehran Baboli; Sungheon Gene Kim
Journal:  PLoS One       Date:  2020-06-10       Impact factor: 3.240

6.  Motion corrected silent ZTE neuroimaging.

Authors:  Emil Ljungberg; Tobias C Wood; Ana Beatriz Solana; Steven C R Williams; Gareth J Barker; Florian Wiesinger
Journal:  Magn Reson Med       Date:  2022-04-05       Impact factor: 3.737

  6 in total

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