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A CUDA-based reverse gridding algorithm for MR reconstruction.

Jingzhu Yang1, Chaolu Feng, Dazhe Zhao

  • 1Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, CO 110179, China. yangjinzhu@neusoft.com

Magnetic Resonance Imaging
|August 18, 2012
PubMed
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A new reverse gridding algorithm (RGA) using Compute Unified Device Architecture (CUDA) significantly speeds up Magnetic Resonance (MR) image reconstruction from non-Cartesian data. This method enhances PROPELLER imaging efficiency by overcoming data conflicts and improving speed 7.5x.

Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Non-Cartesian Magnetic Resonance (MR) data requires gridding algorithms (GA) for reconstruction.
  • Traditional GA has high runtime complexity (O(K×N(2))) and involves extensive matrix calculations.

Purpose of the Study:

  • To develop a more efficient gridding algorithm for non-Cartesian MR data reconstruction.
  • To address the write-write conflict issue in CUDA-based gridding.

Main Methods:

  • A Compute Unified Device Architecture (CUDA)-based Reverse Gridding Algorithm (RGA) was developed.
  • RGA calculates trajectory windows for each grid, accumulating k-space data contributions.
  • The algorithm was implemented for PROPELLER non-Cartesian sampling.

Main Results:

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  • The CUDA-based RGA successfully resolved write-write conflicts encountered in traditional CUDA implementations.
  • Reconstruction speed was improved by 7.5 times compared to the traditional GA.

Conclusions:

  • The proposed CUDA-based RGA offers a significant improvement in reconstruction efficiency for non-Cartesian MR imaging.
  • This method provides a faster and more consistent approach to reconstructing PROPELLER data.