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Compressed-sensing-based content-driven hierarchical reconstruction: Theory and application to C-arm cone-beam

Hélène Langet1, Cyril Riddell2, Aymeric Reshef2

  • 1Image Processing and Clinical Applications Laboratory, GE Healthcare, Buc F-78533, France; Laboratoire des Signaux et Systèmes, CentraleSupélec, Gif-sur-Yvette F-91192, France; Center for Visual Computing, CentraleSupélec, Châtenay-Malabry F-92295, France; and INRIA, Orsay F-91893, France.

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This study introduces a novel compressed sensing method for C-arm cone-beam CT (CBCT) reconstruction. The hierarchical approach effectively reduces subsampling artifacts, improving image quality in interventional imaging.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Interventional C-arm systems face significant subsampling artifacts in cone-beam CT (CBCT) due to slow rotation and low detector frame rates.
  • Analytical reconstruction methods often struggle with these artifacts, impacting image quality in clinical applications.

Purpose of the Study:

  • To propose a novel content-driven hierarchical reconstruction method for C-arm CBCT.
  • To address and reduce subsampling artifacts using compressed sensing principles.

Main Methods:

  • A hierarchical reconstruction strategy prioritizing salient features to mitigate artifact contamination.
  • Implementation of various sparsity-promoting schemes including orthogonal matching pursuit, L1-norm minimization, total variation (TV), and nonlinear diffusion (NLD).
  • Utilized penalized iterative filtered backprojection algorithms and the alternating direction method of multipliers for optimization.

Main Results:

  • Demonstrated fast convergence to accurate solutions for TV-constrained minimization problems on simulated data.
  • Clinical C-arm CBCT data showed significant streak artifact reduction and improved image quality using both TV and NLD constraints.

Conclusions:

  • A flexible compressed sensing algorithmic framework was developed for C-arm CBCT reconstruction.
  • The proposed method effectively handles images not well-approximated by piecewise constant functions, enhancing clinical utility.