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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Related Experiment Video

Updated: Mar 14, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction.

Luzhen Deng1, Peng Feng1, Mianyi Chen1

  • 1Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China.

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This study introduces an improved Total Variation minimization method for Computed Tomography (CT) reconstruction using prior images. The novel approach enhances image quality from sparse-view data by incorporating valuable previous information.

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Imaging

Background:

  • Compressive Sensing (CS) theory offers potential for reconstructing Computed Tomography (CT) images from limited projection data.
  • Total Variation (TV) minimization is a popular CT reconstruction method but does not inherently integrate prior imaging information.
  • The absence of prior image incorporation limits the quality of reconstructions in conventional TV-based methods.

Purpose of the Study:

  • To enhance the quality of CT image reconstruction from sparse-view projection data.
  • To develop an improved TV minimization method that effectively utilizes prior images.
  • To leverage the Split-Bregman method and image registration for improved CT reconstruction.

Main Methods:

  • Proposed an improved Total Variation (TV) minimization method incorporating prior images.
  • Employed the Split-Bregman method for CT reconstruction.
  • Utilized Locally Linear Embedding (LLE) for registering asynchronously acquired images.

Main Results:

  • Numerical simulations with an abdomen phantom demonstrated accurate image reconstruction from sparse projection data.
  • The proposed method successfully incorporated prior information to promote image quality.
  • Validation with a real dataset confirmed the method's efficacy.

Conclusions:

  • The improved TV minimization method effectively reconstructs high-quality CT images from sparse-view data.
  • Incorporating prior images via the proposed method significantly enhances the reconstruction process.
  • The technique shows promise for clinical applications requiring low-dose or sparse-view CT.