Literature DB >> 9696161

Magnetic resonance imaging and the reduction of motion artifacts: review of the principles.

R Van de Walle1, I Lemahieu, E Achten.   

Abstract

Magnetic resonance (MR) imaging is a non-invasive diagnostic tool which is widely used nowadays. In this paper, the basic principles of MR imaging are explained and it is shown how images can be reconstructed in case of standard 2D Fourier Transform (2DFT) imaging. Several aspects of MR signal encoding are described. Unfortunately, motion of the patient during a magnetic resonance experiment often causes severe artifacts in the images. For example, in 2DFT imaging blurring and ghosting are seen and the appearance of motion artifacts remains one of the major drawbacks in MR imaging. Several methods to reduce motion artifacts in MR imaging have been proposed in the past. An overview of the principles on which these methods are based is given in this paper. Both post-processing methods and techniques that rely on gating or the use of alternative acquisition schemes such as projection reconstruction are discussed.

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Mesh:

Year:  1997        PMID: 9696161

Source DB:  PubMed          Journal:  Technol Health Care        ISSN: 0928-7329            Impact factor:   1.285


  6 in total

Review 1.  Motion artifacts in MRI: A complex problem with many partial solutions.

Authors:  Maxim Zaitsev; Julian Maclaren; Michael Herbst
Journal:  J Magn Reson Imaging       Date:  2015-01-28       Impact factor: 4.813

Review 2.  Prospective motion correction in functional MRI.

Authors:  Maxim Zaitsev; Burak Akin; Pierre LeVan; Benjamin R Knowles
Journal:  Neuroimage       Date:  2016-11-11       Impact factor: 6.556

3.  WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI.

Authors:  Brent Foster; Anand A Joshi; Marissa Borgese; Yasser Abdelhafez; Robert D Boutin; Abhijit J Chaudhari
Journal:  Comput Med Imaging Graph       Date:  2017-12-28       Impact factor: 4.790

4.  Addressing Motion Blurs in Brain MRI Scans Using Conditional Adversarial Networks and Simulated Curvilinear Motions.

Authors:  Shangjin Li; Yijun Zhao
Journal:  J Imaging       Date:  2022-03-23

5.  Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans.

Authors:  Ádám Nárai; Petra Hermann; Tibor Auer; Péter Kemenczky; János Szalma; István Homolya; Eszter Somogyi; Pál Vakli; Béla Weiss; Zoltán Vidnyánszky
Journal:  Sci Data       Date:  2022-10-17       Impact factor: 8.501

6.  Effect of head motion-induced artefacts on the reliability of deep learning-based whole-brain segmentation.

Authors:  Péter Kemenczky; Pál Vakli; Eszter Somogyi; István Homolya; Petra Hermann; Viktor Gál; Zoltán Vidnyánszky
Journal:  Sci Rep       Date:  2022-01-31       Impact factor: 4.379

  6 in total

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