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Multiple limited-angles computed tomography reconstruction based on multi-direction total variation minimization.

Changcheng Gong1, Li Zeng2, Yumeng Guo2

  • 1Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing 400044, China.

The Review of Scientific Instruments
|January 3, 2019
PubMed
Summary
This summary is machine-generated.

Multiple limited-angles (MLA) sampling in computed tomography (CT) reduces radiation dose. A new multi-direction total variation minimization (MDTVM) method effectively suppresses shading artifacts in MLA CT reconstruction, outperforming traditional total variation minimization (TVM).

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Accurate computed tomography (CT) reconstruction from incomplete data is crucial for reducing radiation dose and scan time.
  • Sparse and limited-angle sampling present challenges in CT reconstruction, with limited-angle sampling often yielding inaccurate images.
  • The multiple limited-angles (MLA) sampling scheme offers a balance between technical feasibility and reconstruction complexity, but can introduce shading artifacts.

Purpose of the Study:

  • To address shading artifacts in CT images reconstructed from MLA sampling data.
  • To develop and evaluate an artifact-suppression model specifically for MLA CT reconstruction.
  • To compare the performance of the proposed model against existing methods.

Main Methods:

  • A novel artifact-suppression reconstruction model based on multi-direction total variation minimization (MDTVM) was designed.
  • The MDTVM model was utilized to solve the optimization problem for CT image reconstruction from MLA data.
  • Experiments were conducted using digital phantoms and real projection data.

Main Results:

  • CT images reconstructed using the traditional total variation minimization (TVM) method exhibited shading artifacts.
  • The proposed MDTVM method demonstrated superior performance in suppressing these directional shading artifacts compared to TVM.
  • Experimental results confirmed the effectiveness of MDTVM on both simulated and real-world data.

Conclusions:

  • MDTVM is an effective technique for suppressing shading artifacts in CT images reconstructed from MLA sampling.
  • The developed method offers improved image quality for CT scans utilizing MLA sampling strategies.
  • This work provides a valuable solution for enhancing the accuracy of CT reconstruction in dose-reduced scenarios.