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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
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BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences.

Ryoichi Kose1, Katsumi Kose2

  • 1MRTechnology Inc, 2-1-6 B5 Sengen, Tsukuba 3050047, Japan.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|May 28, 2017
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A new magnetic resonance imaging (MRI) simulator utilizes graphic-processor-unit (GPU) boards for rapid computation. This advanced MRI simulator accurately reproduces experimental data, accelerating MRI research and development.

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Graphic processor unitMRI simulatorMagnetic resonance imagingSimulation

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Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool.
  • Accurate simulation of MRI experiments is essential for research and development.
  • Existing computational methods can be time-consuming.

Purpose of the Study:

  • To develop a high-speed MRI simulator using graphic-processor-unit (GPU) technology.
  • To validate the simulator's accuracy against experimental MRI data.
  • To assess the simulator's utility for various pulse sequences and imaging parameters.

Main Methods:

  • Developed an MRI simulator leveraging two high-performance GPU boards (GTX 1080).
  • Implemented pulse sequences for three-dimensional (3D) gradient-echo, 3D radio-frequency spoiled gradient-echo, and gradient-echo multislice imaging.
  • Compared simulation speeds against central processing unit (CPU) based calculations.

Main Results:

  • Achieved a typical calculation speed of approximately 7 TFLOPS using the GPU-based simulator.
  • Demonstrated a speed increase of approximately 14 times compared to CPU-based simulations.
  • Successfully reproduced experimental MR images by optimizing subvoxel counts and simulated practical imaging matrix sizes.

Conclusions:

  • The developed MRI simulator offers significant computational speed advantages.
  • The simulator accurately reproduces experimental MR images, validating its performance.
  • This powerful MRI simulation tool is poised to become essential for advancing MRI research and development.