Using GPUs to accelerate advanced MRI reconstruction with field inhomogeneity compensation

Yue Zhuo, Xiao Long Wu, Justin P. Haldar, Thibault Marin, Wen mei W Hwu, Zhi Pei Liang, Bradley P. Sutton

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter focuses on a GPU implementation for a fast advanced non-Cartesian MRI reconstruction algorithm with field inhomogeneity compensation. Magnetic resonance imaging (MRI) is a flexible diagnostic tool, providing image contrast relating to the structure, function, and biochemistry of virtually every system in the body. However, the technique is generally slow and has low sensitivity, which limits its application in the clinical environment. Several challenges exist that limit the application of MRI in the clinical environment. Traditionally, the main limitations in MRI have been due to the manner in which data are sampled in clinical scans. The techniques of tiling have been applied with constant memory, loop invariant code motion, storing variables in registers, and using single-precision floating-point computations on the GPU kernels. The parallel structure of the reconstruction algorithms makes it suitable for parallel programming on GPUs. Accelerating this kind of algorithm can allow for more accurate image reconstruction while keeping computation times short enough for clinical use. Thus, the use of GPUs will enable improved trade-offs between data acquisition time, signal-to-noise ratio, and the severity of artifacts owing to nonideal physical effects during the MRI imaging experiment. © 2011

Original languageEnglish (US)
Title of host publicationGPU Computing Gems Emerald Edition
PublisherElsevier Inc.
Number of pages14
ISBN (Print)9780123849885
StatePublished - 2011

ASJC Scopus subject areas

  • General Computer Science


Dive into the research topics of 'Using GPUs to accelerate advanced MRI reconstruction with field inhomogeneity compensation'. Together they form a unique fingerprint.

Cite this