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Related Concept Videos

Computed Tomography01:10

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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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.
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Imaging Studies for Cardiovascular System V: CT01:28

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Imaging Studies I: CT and MRI01:14

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Related Experiment Video

Updated: Sep 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising.

Chaoqun Tan1, Mingming Yang2, Zhisheng You1

  • 1National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China.

Precision Clinical Medicine
|June 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces SKFCycleGAN, an advanced AI model for low-dose computed tomography (LDCT) denoising. The method effectively reduces noise and preserves image details, enhancing medical imaging quality.

Keywords:
clinical datasetcycle-consistent adversarial networkimage denoisingselective kernel networksunsupervised low dose CT

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Processing

Background:

  • Low-dose computed tomography (LDCT) is crucial for reducing patient radiation exposure.
  • Existing denoising methods, including CycleGAN, struggle with noise reduction while preserving fine details due to a lack of paired data.
  • There is a need for improved LDCT denoising techniques that maintain image quality and anatomical features.

Purpose of the Study:

  • To develop a novel unsupervised CycleGAN-based model for enhanced low-dose computed tomography (LDCT) denoising.
  • To improve image quality by reducing noise while preserving critical details in LDCT scans.
  • To address the limitations of current denoising methods in scenarios with limited paired CT images.

Main Methods:

  • Proposed a novel unsupervised model, SKFCycleGAN, integrating a two-sided network within selective kernel networks (SK-NET) for adaptive feature selection.
  • Utilized a patchGAN discriminator and perceptual loss to enhance detail maintenance in generated CT images.
  • Employed patch-based training for robust model performance.

Main Results:

  • The SKFCycleGAN model demonstrated superior performance compared to competing methods on both clinical and Mayo datasets.
  • Achieved significant noise suppression in low-dose computed tomography (LDCT) images.
  • Successfully preserved crucial edge details and fine features within the denoised images.

Conclusions:

  • The proposed SKFCycleGAN offers an effective solution for low-dose computed tomography (LDCT) denoising.
  • The model excels in noise reduction and edge preservation, outperforming existing techniques.
  • This approach holds promise for improving the diagnostic quality of LDCT imaging.