GPU Accelerated MRI Reconstruction

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GPU Accelerated MRI Reconstruction
Sean M. Arietta
Professor Kevin Skadron
Computer Science, School of Engineering and
Applied Science
University of Virginia, Charlottesville, VA 22904
Supported by a grant from the NSF
Algorithm and Approach
The current speed of MRI equipment hinders the
fast acquisition of data. In order to compensate
for this bottleneck, a multi-channel spiral
acquisition process must be implemented. This
process requires an algorithm that can recover
planar data across multiple channels which can
become costly as image resolution increases.

• Overview
• Data for each channel is sent to the processing
  unit as frequencies derived from the response of
  human tissue to varied EM fields.
• The data is acquired in a spiral pattern and
  must be transformed into Cartesian coordinates
  before processing can begin.
• Each sampled point from this spirally acquired
  dataset is accumulated to a point in the normal
  2D Cartesian grid.
• Now the data is in k-space or the space within
  which frequencies are encoded.
• A system of linear equations is built from each
  channel and solved to return the fully stitched
  image, still in k-space.
• Finally, the stitched image is transformed from
  k-space to the spatial domain to recover the
  imaged subject.

• Drawbacks
• Reconstruction of this acquisition style is
  extremely computationally intense.
• In order to even reconstruct the data in an
  acceptable timeframe requires multi- thousand
  dollar clusters.
• Advantages
• Spiral acquisition trajectories decrease
  acquisition times to realtime rates ( 50fps in
  some cases).
• If the reconstruction time can be reduced,
  doctors will be able to diagnose patients using
  MRI scanners while the patient is still under
  examination.

• New Research Aim
• Exploit the parallelizability of the
  reconstruction technique using modern Graphics
  Processing Units (GPUs), allowing
• Cheaper reconstruction solutions
• Faster reconstruction
• Easier maintenance
• By collaborating the University of Virginia
  Medical College, we are able to implement fast
  reconstruction algorithms that could one day run
  close to realtime.
• Although up until just recently a less intuitive
  programming practice was needed to exploit GPU
  architecture, the release of NVIDIAs CUDA
  platform has allowed for easier implementation.