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Related Concept Videos

Computed Tomography01:10

Computed Tomography

4.5K
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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Related Experiment Video

Updated: Jun 24, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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[Reconstruction from CT truncated data based on dual-domain transformer coupled feature learning].

C Wang1,2, M Meng1,2, M Li2

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Nan Fang Yi Ke Da Xue Xue Bao = Journal of Southern Medical University
|June 11, 2024
PubMed
Summary
This summary is machine-generated.

The DDTrans model reduces computed tomography (CT) truncation artifacts using dual-domain Transformer learning. This method effectively reconstructs images with insufficient field of view (FOV) data.

Keywords:
CT truncation artifactsdeep learningdual-domaintransformer

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Area of Science:

  • Medical imaging
  • Artificial intelligence in radiology
  • Image reconstruction algorithms

Context:

  • Computed tomography (CT) scans often suffer from truncation artifacts due to an insufficient field of view (FOV).
  • These artifacts distort image structures and compromise diagnostic accuracy.
  • Existing reconstruction methods struggle to fully recover information outside the scanned FOV.

Purpose:

  • To introduce DDTrans, a novel CT reconstruction model.
  • DDTrans utilizes dual-domain Transformer learning in both projection and image spaces.
  • The goal is to minimize truncation artifacts and image distortion.

Summary:

  • DDTrans employs Transformer networks in projection and image domains, leveraging attention mechanisms for global feature capture.
  • A differentiable Radon back-projection operator enables end-to-end training.
  • Projection consistency loss further refines image reconstruction accuracy.

Impact:

  • DDTrans demonstrates superior performance in removing edge artifacts and restoring external FOV information compared to existing algorithms.
  • The model effectively ensures accurate reconstruction within the FOV.
  • It achieves approximate reconstruction of data outside the FOV, enhancing diagnostic utility.