Literature DB >> 30489652

Simple auto-calibrated gradient delay estimation from few spokes using Radial Intersections (RING).

Sebastian Rosenzweig1, H Christian M Holme1,2, Martin Uecker1,2.   

Abstract

PURPOSE: To develop a simple and robust tool for the estimation of gradient delays from highly undersampled radial k-space data. THEORY: In radial imaging gradient delays induce parallel and orthogonal trajectory shifts, which can be described using an ellipse model. The intersection points of the radial spokes, which can be estimated by spoke-by-spoke comparison of k-space samples, distinctly determine the parameters of the ellipse. Using the proposed method (RING), these parameters can be obtained using a least-squares fit and utilized for the correction of gradient delays.
METHODS: The functionality and accuracy of the proposed RING method is validated and compared to correlation-based gradient-delay estimation from opposing spokes using numerical simulations, phantom and in vivo heart measurements.
RESULTS: In all experiments, RING robustly provides accurate gradient delay estimations even for as few as three radial spokes.
CONCLUSIONS: The simple and straightforward to implement RING method provides accurate gradient delay estimation for highly undersampled radial imaging.
© 2018 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  RING; artifacts; gradient delay; radial imaging; system imperfections; trajectory correction

Mesh:

Year:  2018        PMID: 30489652     DOI: 10.1002/mrm.27506

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


  4 in total

1.  Whole-heart, ungated, free-breathing, cardiac-phase-resolved myocardial perfusion MRI by using Continuous Radial Interleaved simultaneous Multi-slice acquisitions at sPoiled steady-state (CRIMP).

Authors:  Ye Tian; Jason Mendes; Brent Wilson; Alexander Ross; Ravi Ranjan; Edward DiBella; Ganesh Adluru
Journal:  Magn Reson Med       Date:  2020-06-03       Impact factor: 4.668

2.  Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation.

Authors:  KyungPyo Hong; Florian Schiffers; Amanda L DiCarlo; Cynthia K Rigsby; Hassan Haji-Valizadeh; Daniel C Lee; Michael Markl; Aggelos K Katsaggelos; Daniel Kim
Journal:  Phys Med Biol       Date:  2021-10-21       Impact factor: 4.174

3.  Respiratory Motion Mitigation and Repeatability of Two Diffusion-Weighted MRI Methods Applied to a Murine Model of Spontaneous Pancreatic Cancer.

Authors:  Jianbo Cao; Hee Kwon Song; Hanwen Yang; Victor Castillo; Jinbo Chen; Cynthia Clendenin; Mark Rosen; Rong Zhou; Stephen Pickup
Journal:  Tomography       Date:  2021-02-20

4.  Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN).

Authors:  Daming Shen; Sushobhan Ghosh; Hassan Haji-Valizadeh; Ashitha Pathrose; Florian Schiffers; Daniel C Lee; Benjamin H Freed; Michael Markl; Oliver S Cossairt; Aggelos K Katsaggelos; Daniel Kim
Journal:  NMR Biomed       Date:  2020-09-01       Impact factor: 4.044

  4 in total

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