Literature DB >> 24579187

Free-breathing whole-heart coronary MRA: motion compensation integrated into 3D cartesian compressed sensing reconstruction.

Christoph Forman1, Robert Grimm1, Jana Maria Hutter1, Andreas Maier1, Joachim Hornegger1, Michael O Zenge2.   

Abstract

Respiratory motion remains a major challenge for whole-heart coronary magnetic resonance angiography (CMRA). Recently, iterative reconstruction has been augmented with non-rigid motion compensation to correct for the effects of respiratory motion. The major challenge of this approach is the estimation of dense deformation fields. In this work, the application of such a motion-compensated reconstruction is proposed for accelerated 3D Cartesian whole-heart CMRA. Without the need for extra calibration data or user interaction, the nonrigid deformations due to respiratory motion are directly estimated on the acquired image data. In-vivo experiments on 14 healthy volunteers were performed to compare the proposed method with the result of a navigator-gated reference scan. While reducing the acquisition time by one third, the reconstructed images resulted in equivalent vessel sharpness of 0.44 +/- 0.06 mm(-1) and 0.45 +/- 0.05 mm(-1), respectively.

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Year:  2013        PMID: 24579187     DOI: 10.1007/978-3-642-40763-5_71

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  4 in total

1.  Respiratory optimized data selection for more resilient self-navigated whole-heart coronary MR angiography.

Authors:  Jerome Chaptinel; Davide Piccini; Gabriele Bonanno; Simone Coppo; Pierre Monney; Matthias Stuber; Juerg Schwitter
Journal:  MAGMA       Date:  2016-11-14       Impact factor: 2.310

Review 2.  Compressed sensing for body MRI.

Authors:  Li Feng; Thomas Benkert; Kai Tobias Block; Daniel K Sodickson; Ricardo Otazo; Hersh Chandarana
Journal:  J Magn Reson Imaging       Date:  2016-12-16       Impact factor: 4.813

Review 3.  Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption.

Authors:  Alice C Yang; Madison Kretzler; Sonja Sudarski; Vikas Gulani; Nicole Seiberlich
Journal:  Invest Radiol       Date:  2016-06       Impact factor: 6.016

4.  Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests.

Authors:  Benedikt Lorch; Ghislain Vaillant; Christian Baumgartner; Wenjia Bai; Daniel Rueckert; Andreas Maier
Journal:  J Med Eng       Date:  2017-06-11
  4 in total

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