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Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence.

Tianye Niu1, Xiaojing Ye, Quentin Fruhauf

  • 1Nuclear and Radiological Engineering and Medical Physics Programs, The George W Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

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We improved the Accelerated Barrier Optimization Compressed Sensing (ABOCS) algorithm for CT reconstruction using the Unknown-Parameter Nesterov (UPN) method. This new approach ensures more stable and faster convergence, significantly reducing iteration counts for improved image quality.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Iterative CT reconstruction algorithms aim to improve image quality and reduce radiation dose.
  • Previous Accelerated Barrier Optimization Compressed Sensing (ABOCS) implementations using Gradient Projection-Barzilai-Borwein (GP-BB) exhibited unstable convergence.
  • Non-monotonic behavior in GP-BB hindered reliable and efficient CT image reconstruction.

Purpose of the Study:

  • To enhance the convergence stability and speed of the ABOCS algorithm.
  • To investigate the performance of ABOCS with the Unknown-Parameter Nesterov (UPN) method on clinical patient data.
  • To compare the UPN-enhanced ABOCS against existing reconstruction methods.

Main Methods:

  • Implementation of the ABOCS algorithm with the Unknown-Parameter Nesterov (UPN) optimization method.
  • Comparative analysis using computer simulations (Shepp-Logan phantom), physical phantom (Catphan©600), and clinical head-and-neck patient data.
  • Evaluation of reconstruction performance based on image quality and convergence metrics, including Relative Reconstruction Error (RRE).

Main Results:

  • ABOCS with UPN demonstrated significantly more stable and faster convergence than GP-BB and Bregman-type methods across all tested datasets.
  • The UPN method achieved comparable image quality to existing methods while reducing iteration numbers by up to 90%.
  • High-quality reconstructions were achieved with significantly fewer projections (17-25%) and lower RRE (e.g., 7.3% for patient data vs. 21% for FBP).

Conclusions:

  • The Unknown-Parameter Nesterov (UPN) method is proposed as an effective enhancement for the ABOCS algorithm.
  • UPN significantly improves convergence stability and reduces iteration count compared to GP-BB and Bregman-type methods.
  • The UPN-enhanced ABOCS offers a superior approach for CT image reconstruction, particularly in low-dose and limited-view scenarios.