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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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SART-Type Half-Threshold Filtering Approach for CT Reconstruction.

Hengyong Yu1, Ge Wang2

  • 1Department of Biomedical Engineering, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA ; Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Winston-Salem, NC 27157, USA.

IEEE Access : Practical Innovations, Open Solutions
|December 23, 2014
PubMed
Summary

This study introduces a new Simultaneous Algebraic Reconstruction Technique (SART)-type half-threshold filtering method for computed tomography (CT) reconstruction. This approach enhances image quality from limited and noisy projection data.

Keywords:
Compressive samplingdiscrete gradient transformhalf-threshold filteringpseudo-inverse transform

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Regularization Techniques

Background:

  • Sparsity-constrained problems are often solved using L1 regularization.
  • The L p -norm (0 < p < 1) offers enhanced sparsity constraints for improved imaging performance.
  • Iterative thresholding operations, or half-threshold filtering, provide analytic solutions for L p -regularization.

Purpose of the Study:

  • To develop a Simultaneous Algebraic Reconstruction Technique (SART)-type half-threshold filtering framework for computed tomography (CT) reconstruction.
  • To address the non-invertibility of the Discrete Gradient Transform (DGT) for CT reconstruction by constructing a pseudoinverse transform.
  • To evaluate the effectiveness of the proposed framework in improving image quality from limited and noisy projection data.

Main Methods:

  • A SART-type half-threshold filtering framework was designed for CT reconstruction.
  • A pseudoinverse transform for the Discrete Gradient Transform (DGT) was constructed to enable its use in half-threshold filtering.
  • The proposed algorithms were tested using numerical and physical phantom datasets.

Main Results:

  • The SART-type half-threshold filtering algorithms demonstrated significant potential for enhancing reconstructed image quality.
  • The framework effectively utilizes limited and noisy projection data.
  • The proposed methods showed complementary performance to existing soft-threshold and hard-threshold filtering techniques.

Conclusions:

  • The developed SART-type half-threshold filtering framework offers a promising approach for CT image reconstruction.
  • The construction of a DGT pseudoinverse transform overcomes limitations of non-invertible transforms in half-threshold filtering.
  • These algorithms have the potential to improve diagnostic accuracy in medical imaging by providing higher quality reconstructions from sparse and noisy data.