Literature DB >> 12594754

Fast method for correcting image misregistration due to organ motion in time-series MRI data.

Sandeep N Gupta1, Meiyappan Solaiyappan, Garth M Beache, Andrew E Arai, Thomas K F Foo.   

Abstract

Time-series MRI data often suffers from image misalignment due to patient movement and respiratory and other physiologic motion during the acquisition process. It is necessary that this misalignment be corrected prior to any automated quantitative analysis. In this article a fast and automated technique for removing in-plane misalignment from time-series MRI data is presented. The method is computationally efficient, robust, and fine-tuned for the clinical setting. The method was implemented and tested on data from 21 human subjects, including myocardial perfusion imaging, renal perfusion imaging, and blood-oxygen level-dependent cardiac T(2*) imaging. In these applications 10-fold or better reduction in image misalignment is reported. The improvement after registration on representative time-intensity curves is shown. Although the method currently corrects translation motion using image center of mass, the mathematical framework of our approach may be extended to correct rotation and other higher-order displacements. Copyright 2003 Wiley-Liss, Inc.

Entities:  

Mesh:

Year:  2003        PMID: 12594754     DOI: 10.1002/mrm.10394

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  10 in total

1.  A quantitative pixel-wise measurement of myocardial blood flow by contrast-enhanced first-pass CMR perfusion imaging: microsphere validation in dogs and feasibility study in humans.

Authors:  Li-Yueh Hsu; Daniel W Groves; Anthony H Aletras; Peter Kellman; Andrew E Arai
Journal:  JACC Cardiovasc Imaging       Date:  2012-02

2.  Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis.

Authors:  Gert Wollny; Peter Kellman; Andrés Santos; María J Ledesma-Carbayo
Journal:  Med Image Anal       Date:  2012-02-23       Impact factor: 8.545

3.  Robust universal nonrigid motion correction framework for first-pass cardiac MR perfusion imaging.

Authors:  Mitchel Benovoy; Matthew Jacobs; Farida Cheriet; Nagib Dahdah; Andrew E Arai; Li-Yueh Hsu
Journal:  J Magn Reson Imaging       Date:  2017-02-15       Impact factor: 4.813

4.  3D diffusion tensor MRI with isotropic resolution using a steady-state radial acquisition.

Authors:  Youngkyoo Jung; Alexey A Samsonov; Walter F Block; Mariana Lazar; Aiming Lu; Jing Liu; Andrew L Alexander
Journal:  J Magn Reson Imaging       Date:  2009-05       Impact factor: 4.813

5.  Quantitative high-resolution renal perfusion imaging using 3-dimensional through-time radial generalized autocalibrating partially parallel acquisition.

Authors:  Katherine L Wright; Yong Chen; Haris Saybasili; Mark A Griswold; Nicole Seiberlich; Vikas Gulani
Journal:  Invest Radiol       Date:  2014-10       Impact factor: 6.016

6.  An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI.

Authors:  Beau Pontre; Brett R Cowan; Edward DiBella; Sancgeetha Kulaseharan; Devavrat Likhite; Nils Noorman; Lennart Tautz; Nicholas Tustison; Gert Wollny; Alistair A Young; Avan Suinesiaputra
Journal:  IEEE J Biomed Health Inform       Date:  2017-09       Impact factor: 5.772

7.  Non-rigid registration and KLT filter to improve SNR and CNR in GRE-EPI myocardial perfusion imaging.

Authors:  Georgeta Mihai; Yu Ding; Hui Xue; Yiu-Cho Chung; Sanjay Rajagopalan; Jens Guehring; Orlando P Simonetti
Journal:  J Biomed Sci Eng       Date:  2012-12

8.  Free breathing myocardial perfusion data sets for performance analysis of motion compensation algorithms.

Authors:  Gert Wollny; Peter Kellman
Journal:  Gigascience       Date:  2014-11-11       Impact factor: 6.524

Review 9.  Image registration in dynamic renal MRI-current status and prospects.

Authors:  Frank G Zöllner; Amira Šerifović-Trbalić; Gordian Kabelitz; Marek Kociński; Andrzej Materka; Peter Rogelj
Journal:  MAGMA       Date:  2019-10-09       Impact factor: 2.310

10.  Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks.

Authors:  Ahmed S Fahmy; Hossam El-Rewaidy; Maryam Nezafat; Shiro Nakamori; Reza Nezafat
Journal:  J Cardiovasc Magn Reson       Date:  2019-01-14       Impact factor: 5.364

  10 in total

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