More IMPATIENT: A gridding-accelerated Toeplitz-based strategy for non-Cartesian high-resolution 3D MRI on GPUs

Jiading Gai, Nady Obeid, Joseph L. Holtrop, Xiao Long Wu, Fan Lam, Maojing Fu, Justin P. Haldar, Wen Mei W Hwu, Zhi Pei Liang, Bradley P. Sutton

Research output: Contribution to journalArticle

Abstract

Several recent methods have been proposed to obtain significant speed-ups in MRI image reconstruction by leveraging the computational power of GPUs. Previously, we implemented a GPU-based image reconstruction technique called the Illinois Massively Parallel Acquisition Toolkit for Image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI) for reconstructing data collected along arbitrary 3D trajectories. In this paper, we improve IMPATIENT by removing computational bottlenecks by using a gridding approach to accelerate the computation of various data structures needed by the previous routine. Further, we enhance the routine with capabilities for off-resonance correction and multi-sensor parallel imaging reconstruction. Through implementation of optimized gridding into our iterative reconstruction scheme, speed-ups of more than a factor of 200 are provided in the improved GPU implementation compared to the previous accelerated GPU code.

Original languageEnglish (US)
Pages (from-to)686-697
Number of pages12
JournalJournal of Parallel and Distributed Computing
Volume73
Issue number5
DOIs
StatePublished - May 2013

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Keywords

  • CUDA
  • GPU
  • Gridding
  • MRI
  • Non-Cartesian
  • Toeplitz

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Theoretical Computer Science

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