Real-time imaging with radial GRAPPA: Implementation on a heterogeneous architecture for low-latency reconstructions

Haris Saybasili, Daniel A. Herzka, Nicole Seiberlich, Mark A. Griswold

Research output: Contribution to journalArticle

Abstract

Combination of non-Cartesian trajectories with parallel MRI permits to attain unmatched acceleration rates when compared to traditional Cartesian MRI during real-time imaging. However, computationally demanding reconstructions of such imaging techniques, such as k-space domain radial generalized auto-calibrating partially parallel acquisitions (radial GRAPPA) and image domain conjugate gradient sensitivity encoding (CG-SENSE), lead to longer reconstruction times and unacceptable latency for online real-time MRI on conventional computational hardware. Though CG-SENSE has been shown to work with low-latency using a general purpose graphics processing unit (GPU), to the best of our knowledge, no such effort has been made for radial GRAPPA. Radial GRAPPA reconstruction, which is robust even with highly undersampled acquisitions, is not iterative, requiring only significant computation during initial calibration while achieving good image quality for low-latency imaging applications. In this work, we present a very fast, low-latency, reconstruction framework based on a heterogeneous system using multi-core CPUs and GPUs. We demonstrate an implementation of radial GRAPPA that permits reconstruction times on par with or faster than acquisition of highly accelerated datasets in both cardiac and dynamic musculoskeletal imaging scenarios. Acquisition and reconstruction times are reported.

Original languageEnglish (US)
Pages (from-to)747-758
Number of pages12
JournalMagnetic Resonance Imaging
Volume32
Issue number6
DOIs
StatePublished - Jul 2014

Keywords

  • High-performance computing
  • Parallel MRI
  • Radial GRAPPA
  • Real-time low-latency image reconstruction

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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