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Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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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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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Updated: Mar 27, 2026

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A Two-Module Parallel Dual-Domain Network for interior tomography reconstruction.

Haihang Zhao1, Pengxiang Ji1, Yongzhou Wu1

  • 1The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.

Journal of X-Ray Science and Technology
|March 26, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning network, the Two-Module Parallel Dual-Domain Network (TPDDN), enhances interior tomography reconstruction. It effectively reduces artifacts and improves image quality, paving the way for safer medical imaging.

Keywords:
computed tomographydata truncationdeep learningimage reconstructioninterior tomographyregion of interest

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Interior tomography in computed tomography (CT) aims to reduce radiation exposure by focusing on the region of interest.
  • Traditional methods struggle with data truncation, causing cupping artifacts and degrading image quality.

Purpose of the Study:

  • To develop a parallel deep learning network for improved interior tomography reconstruction.
  • To effectively integrate projection and image domain information to enhance diagnostic accuracy.

Main Methods:

  • Proposed an end-to-end deep learning framework: the Two-Module Parallel Dual-Domain Network (TPDDN).
  • TPDDN features an Initial Restoration Module for high-quality priors and an Interactive Fusion Module with parallel, dual-domain branches.
  • The network enables bidirectional feature interaction and information fusion between projection and image domains.

Main Results:

  • TPDDN demonstrated superior qualitative and quantitative performance in extensive experiments.
  • The network achieved excellent results under both normal-dose and high-dose noise conditions.
  • Compared to existing methods, TPDDN significantly enhanced reconstructed image quality.

Conclusions:

  • The TPDDN provides a robust method for interior tomography reconstruction by fusing projection and image domain information.
  • It effectively suppresses cupping artifacts and improves image quality in noisy conditions.
  • The approach shows potential for safer and more accurate diagnostic imaging.