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Related Experiment Videos

Range Condition and ML-EM Checkerboard Artifacts.

Jiangsheng You1, Jing Wang, Zhengrong Liang

  • 1Cubic Imaging LLC, 264 Grove St., Auburndale, MA 02466, USA and the Department of Radiology, State University of New York, Stony Brook, NY 11794, USA (e-mail: jyou@cubic-imaging.com ).

IEEE Transactions on Nuclear Science
|May 2, 2008
PubMed
Summary
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Removing null-space noise from projection data can reduce checkerboard artifacts in maximum likelihood expectation maximization (ML-EM) image reconstruction. This method improves signal-to-noise ratio without uncertain a priori penalties.

Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Signal Processing

Background:

  • Expectation maximization (EM) algorithm for maximum likelihood (ML) image reconstruction often produces checkerboard artifacts due to noise.
  • Conventional solutions involve penalized ML or maximum a posteriori (MAP) methods, which introduce uncertainty from unknown a priori information.
  • Noise in image reconstruction can be categorized into null-space and range-space components.

Purpose of the Study:

  • Investigate the relationship between null-space noise and checkerboard artifacts in ML-EM reconstruction.
  • Evaluate the effectiveness of removing null-space noise to mitigate these artifacts.
  • Provide a noise reduction strategy for iterative image reconstruction.

Main Methods:

  • Utilized the filtered backprojection (FBP) algorithm to estimate and remove null-space noise from projection data.

Related Experiment Videos

  • Analyzed the impact of null-space noise removal on the signal-to-noise ratio (SNR) of projection data.
  • Compared ML-EM reconstructions with and without null-space noise removal.

    • Main Results:

    • Null-space noise removal effectively reduces checkerboard artifacts in ML-EM reconstructed images.
    • Removing null-space noise improves the SNR of the projection data.
    • This approach avoids the uncertainties associated with a priori penalties.

    Conclusions:

    • Removing null-space noise is a viable method to reduce checkerboard artifacts in ML-EM image reconstruction.
    • Understanding noise propagation in different reconstruction algorithms is crucial for image quality improvement.
    • This technique offers a promising alternative for enhancing image quality in applications like single photon emission computed tomography (SPECT).