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CT image reconstruction with half precision floating-point values.

Clemens Maaß1, Matthias Baer, Marc Kachelrieß

  • 1Institute of Medical Physics, University of Erlangen-Nürnberg, Henkestraße 91, D-91052 Erlangen, Germany. clemens.maass@imp.uni-erlangen.de

Medical Physics
|October 8, 2011
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Summary
This summary is machine-generated.

Using 16-bit half precision floating-point values significantly accelerates computed tomography (CT) image reconstruction. This method enhances computational efficiency without negatively impacting image quality in both analytical and iterative reconstruction algorithms.

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Computational Science

Background:

  • Computed tomography (CT) image reconstruction is computationally intensive.
  • Iterative reconstruction algorithms offer superior image quality over analytical methods but are more demanding.
  • Hardware advancements, including CPUs and GPUs, are crucial for efficient reconstruction.

Purpose of the Study:

  • To analyze the impact of 16-bit half precision floating-point values on CT image reconstruction efficiency.
  • To evaluate image quality and computational performance using half precision compared to standard 32-bit float precision.
  • To investigate the applicability of half precision in both analytical (filtered backprojection) and iterative (ordered subset SART) reconstruction.

Main Methods:

  • Adopted 16-bit (half) floating-point values for data representation in image and rawdata domains, reducing data amount by 50%.
  • Conducted CT simulations and measurements comparing half precision reconstructions with standard 32-bit float reconstructions (gold standard).
  • Evaluated image quality through visual comparison of difference images and quantitative metrics for both filtered backprojection and ordered subset SART.

Main Results:

  • Quantization noise introduced by reduced data precision was found to be negligible for both reconstruction methods.
  • Filtered backprojection (FBP) requires optimization for backprojection implementation with half precision data.
  • Ordered subset SART (OS-SART) iterative reconstruction showed no necessary adaptations and maintained convergence speed with half precision.

Conclusions:

  • 16-bit half precision floating-point values can accelerate CT image reconstruction.
  • This acceleration is achieved without compromising the image quality of CT scans.
  • Half precision is a viable and efficient data representation for modern CT reconstruction hardware.