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Related Concept Videos

Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Types Of Transformers01:16

Types Of Transformers

1.0K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

945
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
945
Deconvolution01:20

Deconvolution

239
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.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
239
Source Transformation01:15

Source Transformation

8.0K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
8.0K
The Ideal Transformer01:26

The Ideal Transformer

572
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
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Related Experiment Video

Updated: Aug 30, 2025

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

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Improved GAN: Using a transformer module generator approach for material decomposition.

Guoshuai Wang1, Zhou Liu2, Zhengyong Huang1

  • 1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 100049, China.

Computers in Biology and Medicine
|August 27, 2022
PubMed
Summary

This study introduces a new deep learning model using generative adversarial networks (GANs) with CNNs and transformers to directly create material decomposition maps from single-energy CT scans. The advanced model shows improved accuracy and stability compared to existing methods for medical imaging analysis.

Keywords:
Deep learningDual-energy CTGenerative adversarial networkMaterial decompositionTransformer module

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Dual-energy computed tomography (CT) enables precise material decomposition and quantitative mapping of body substances, crucial for disease diagnosis and treatment evaluation.
  • Limited accessibility of dual-energy CT hinders its widespread clinical adoption.
  • Current deep learning approaches to synthesize dual-energy CT data from single-energy CT involve multiple steps, potentially introducing inaccuracies.

Purpose of the Study:

  • To develop a more accurate and efficient method for generating material decomposition maps from conventional single-energy CT images.
  • To overcome the limitations of multi-step deep learning processes by directly generating maps.

Main Methods:

  • A generative adversarial network (GAN) framework was employed, featuring an improved generator that integrates convolutional neural networks (CNNs) and a transformer module.
  • The model was designed to process both local and global information within the CT images.
  • Performance was evaluated by comparing the proposed method against six other deep learning techniques on water (calcium) and calcium (water) substrate density image datasets.

Main Results:

  • The proposed model demonstrated superior performance and stability compared to competing methods.
  • Quantitative metrics for water (calcium) substrate density images showed average PSNR of 32.7207, SSIM of 0.9685, MAE of 0.0323, and RMSE of 0.0555.
  • For calcium (water) substrate density images, average PSNR was 30.2823, SSIM 0.9449, MAE 0.0652, and RMSE 0.0715.

Conclusions:

  • The developed GAN-based framework with CNNs and transformers effectively generates accurate material decomposition maps directly from single-energy CT images.
  • This approach offers a promising alternative for material quantification in medical imaging, potentially increasing the accessibility and utility of dual-energy CT applications.
  • The model's ability to capture both local and global features contributes to its enhanced performance and stability.