Advanced MRI reconstruction toolbox with accelerating on GPU

Xiao Long Wu, Yue Zhuo, Jiading Gai, Fan Lam, Maojing Fu, Justin P. Haldar, Wen Mei Hwu, Zhi Pei Liang, Bradley P. Sutton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a fast iterative magnetic resonance imaging (MRI) reconstruction algorithm taking advantage of the prevailing GPGPU programming paradigm. In clinical environment, MRI reconstruction is usually performed via fast Fourier transform (FFT). However, imaging artifacts (i.e. signal loss) resulting from susceptibility-induced magnetic field inhomogeneities degrade the quality of reconstructed images. These artifacts must be addressed using accurate modeling of the physics of the system coupled with iterative reconstruction. We have developed a reconstruction algorithm with improved image quality at the expense of computation time and hence an implementation on GPUs achieving significant speedup. In this work, we extend our previous work on GPU implementation by adding several new features. First, we enable Sensitivity Encoding for Fast MRI (SENSE) reconstruction (from data acquired using a multi-receiver coil array) which can reduce the acquisition time. Besides, we have implemented a GPU-based total variation regularization in our SENSE reconstruction framework. In this paper, we describe the different optimizations employed from levels of algorithm, program code structures, and specific architecture performance tuning, featuring both our MRI reconstruction algorithm and GPU hardware specifics. Results show that the current GPU implementation produces accurate image estimates while significantly accelerating the reconstruction.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications
DOIs
StatePublished - Feb 11 2011
EventParallel Processing for Imaging Applications - San Francisco, CA, United States
Duration: Jan 24 2011Jan 25 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7872
ISSN (Print)0277-786X

Other

OtherParallel Processing for Imaging Applications
CountryUnited States
CitySan Francisco, CA
Period1/24/111/25/11

Fingerprint

Magnetic Resonance Imaging
Magnetic resonance
magnetic resonance
Imaging techniques
Reconstruction Algorithm
Total Variation Regularization
GPGPU
artifacts
Fast Fourier transform
Coil
Inhomogeneity
Image Quality
Fast Fourier transforms
Susceptibility
Coupled System
Image quality
Tuning
Encoding
Speedup
Receiver

Keywords

  • field inhomogeneity
  • GPU
  • MRI
  • SENSE
  • susceptibility
  • total variation regularization

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Wu, X. L., Zhuo, Y., Gai, J., Lam, F., Fu, M., Haldar, J. P., ... Sutton, B. P. (2011). Advanced MRI reconstruction toolbox with accelerating on GPU. In Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications [78720Q] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7872). https://doi.org/10.1117/12.872204

Advanced MRI reconstruction toolbox with accelerating on GPU. / Wu, Xiao Long; Zhuo, Yue; Gai, Jiading; Lam, Fan; Fu, Maojing; Haldar, Justin P.; Hwu, Wen Mei; Liang, Zhi Pei; Sutton, Bradley P.

Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications. 2011. 78720Q (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7872).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, XL, Zhuo, Y, Gai, J, Lam, F, Fu, M, Haldar, JP, Hwu, WM, Liang, ZP & Sutton, BP 2011, Advanced MRI reconstruction toolbox with accelerating on GPU. in Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications., 78720Q, Proceedings of SPIE - The International Society for Optical Engineering, vol. 7872, Parallel Processing for Imaging Applications, San Francisco, CA, United States, 1/24/11. https://doi.org/10.1117/12.872204
Wu XL, Zhuo Y, Gai J, Lam F, Fu M, Haldar JP et al. Advanced MRI reconstruction toolbox with accelerating on GPU. In Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications. 2011. 78720Q. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.872204
Wu, Xiao Long ; Zhuo, Yue ; Gai, Jiading ; Lam, Fan ; Fu, Maojing ; Haldar, Justin P. ; Hwu, Wen Mei ; Liang, Zhi Pei ; Sutton, Bradley P. / Advanced MRI reconstruction toolbox with accelerating on GPU. Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications. 2011. (Proceedings of SPIE - The International Society for Optical Engineering).
@inproceedings{d203ef5c4a8d479b85328fef79d19576,
title = "Advanced MRI reconstruction toolbox with accelerating on GPU",
abstract = "In this paper, we present a fast iterative magnetic resonance imaging (MRI) reconstruction algorithm taking advantage of the prevailing GPGPU programming paradigm. In clinical environment, MRI reconstruction is usually performed via fast Fourier transform (FFT). However, imaging artifacts (i.e. signal loss) resulting from susceptibility-induced magnetic field inhomogeneities degrade the quality of reconstructed images. These artifacts must be addressed using accurate modeling of the physics of the system coupled with iterative reconstruction. We have developed a reconstruction algorithm with improved image quality at the expense of computation time and hence an implementation on GPUs achieving significant speedup. In this work, we extend our previous work on GPU implementation by adding several new features. First, we enable Sensitivity Encoding for Fast MRI (SENSE) reconstruction (from data acquired using a multi-receiver coil array) which can reduce the acquisition time. Besides, we have implemented a GPU-based total variation regularization in our SENSE reconstruction framework. In this paper, we describe the different optimizations employed from levels of algorithm, program code structures, and specific architecture performance tuning, featuring both our MRI reconstruction algorithm and GPU hardware specifics. Results show that the current GPU implementation produces accurate image estimates while significantly accelerating the reconstruction.",
keywords = "field inhomogeneity, GPU, MRI, SENSE, susceptibility, total variation regularization",
author = "Wu, {Xiao Long} and Yue Zhuo and Jiading Gai and Fan Lam and Maojing Fu and Haldar, {Justin P.} and Hwu, {Wen Mei} and Liang, {Zhi Pei} and Sutton, {Bradley P.}",
year = "2011",
month = "2",
day = "11",
doi = "10.1117/12.872204",
language = "English (US)",
isbn = "9780819484093",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications",

}

TY - GEN

T1 - Advanced MRI reconstruction toolbox with accelerating on GPU

AU - Wu, Xiao Long

AU - Zhuo, Yue

AU - Gai, Jiading

AU - Lam, Fan

AU - Fu, Maojing

AU - Haldar, Justin P.

AU - Hwu, Wen Mei

AU - Liang, Zhi Pei

AU - Sutton, Bradley P.

PY - 2011/2/11

Y1 - 2011/2/11

N2 - In this paper, we present a fast iterative magnetic resonance imaging (MRI) reconstruction algorithm taking advantage of the prevailing GPGPU programming paradigm. In clinical environment, MRI reconstruction is usually performed via fast Fourier transform (FFT). However, imaging artifacts (i.e. signal loss) resulting from susceptibility-induced magnetic field inhomogeneities degrade the quality of reconstructed images. These artifacts must be addressed using accurate modeling of the physics of the system coupled with iterative reconstruction. We have developed a reconstruction algorithm with improved image quality at the expense of computation time and hence an implementation on GPUs achieving significant speedup. In this work, we extend our previous work on GPU implementation by adding several new features. First, we enable Sensitivity Encoding for Fast MRI (SENSE) reconstruction (from data acquired using a multi-receiver coil array) which can reduce the acquisition time. Besides, we have implemented a GPU-based total variation regularization in our SENSE reconstruction framework. In this paper, we describe the different optimizations employed from levels of algorithm, program code structures, and specific architecture performance tuning, featuring both our MRI reconstruction algorithm and GPU hardware specifics. Results show that the current GPU implementation produces accurate image estimates while significantly accelerating the reconstruction.

AB - In this paper, we present a fast iterative magnetic resonance imaging (MRI) reconstruction algorithm taking advantage of the prevailing GPGPU programming paradigm. In clinical environment, MRI reconstruction is usually performed via fast Fourier transform (FFT). However, imaging artifacts (i.e. signal loss) resulting from susceptibility-induced magnetic field inhomogeneities degrade the quality of reconstructed images. These artifacts must be addressed using accurate modeling of the physics of the system coupled with iterative reconstruction. We have developed a reconstruction algorithm with improved image quality at the expense of computation time and hence an implementation on GPUs achieving significant speedup. In this work, we extend our previous work on GPU implementation by adding several new features. First, we enable Sensitivity Encoding for Fast MRI (SENSE) reconstruction (from data acquired using a multi-receiver coil array) which can reduce the acquisition time. Besides, we have implemented a GPU-based total variation regularization in our SENSE reconstruction framework. In this paper, we describe the different optimizations employed from levels of algorithm, program code structures, and specific architecture performance tuning, featuring both our MRI reconstruction algorithm and GPU hardware specifics. Results show that the current GPU implementation produces accurate image estimates while significantly accelerating the reconstruction.

KW - field inhomogeneity

KW - GPU

KW - MRI

KW - SENSE

KW - susceptibility

KW - total variation regularization

UR - http://www.scopus.com/inward/record.url?scp=79551702326&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79551702326&partnerID=8YFLogxK

U2 - 10.1117/12.872204

DO - 10.1117/12.872204

M3 - Conference contribution

SN - 9780819484093

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications

ER -