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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

524
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
524

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Updated: May 2, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
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Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction

Koichiro Yasaka1, Rin Tsujimoto2, Rintaro Miyo2

  • 1Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan. koyasaka@gmail.com.

Japanese Journal of Radiology
|August 9, 2025
PubMed
Summary
This summary is machine-generated.

Super-resolution deep learning reconstruction with motion reduction (SR-DLR-M) significantly improves CT image quality for diagnosing aortic dissection. This advanced technique reduces motion artifacts and noise, offering superior diagnostic acceptability compared to other methods.

Keywords:
AortaComputed tomographyDeep learning reconstructionMotion artifactSuper-resolution

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Cardiovascular Imaging

Background:

  • Aortic motion artifacts degrade CT image quality, impacting diagnostic accuracy.
  • Deep learning reconstruction (DLR) techniques show promise in improving image quality.
  • Motion reduction algorithms are crucial for mitigating dynamic artifacts in CT scans.

Purpose of the Study:

  • To evaluate the effectiveness of super-resolution deep learning reconstruction with motion reduction (SR-DLR-M) in reducing aorta motion artifacts.
  • To compare SR-DLR-M against super-resolution deep learning reconstruction (SR-DLR) and deep learning reconstruction with motion reduction algorithm (DLR-M).

Main Methods:

  • Retrospective analysis of 86 patients undergoing contrast-enhanced chest CT.
  • Image reconstruction using SR-DLR-M, SR-DLR, and DLR-M.
  • Quantitative assessment of image noise and edge sharpness (edge rise slope/distance).
  • Qualitative assessment of artifacts, sharpness, noise, structure depiction, and diagnostic acceptability by two readers.

Main Results:

  • SR-DLR-M demonstrated significantly lower quantitative noise (7.4 HU) compared to SR-DLR (5.4 HU) and DLR-M (8.3 HU).
  • Improved edge rise slope and distance were observed with SR-DLR-M versus DLR-M, indicating better edge definition.
  • Reader assessments showed SR-DLR-M significantly outperformed SR-DLR and DLR-M in artifact reduction, sharpness, noise, structure depiction, and diagnostic acceptability for aortic dissection.

Conclusions:

  • SR-DLR-M provides superior CT image quality for diagnosing aortic dissection.
  • The combination of super-resolution and motion reduction in deep learning reconstruction effectively mitigates motion artifacts.
  • SR-DLR-M represents a significant advancement in cardiovascular CT imaging.