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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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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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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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Wavelet subband-specific learning for low-dose computed tomography denoising.

Wonjin Kim1, Jaayeon Lee1, Mihyun Kang2

  • 1Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea.

Plos One
|September 9, 2022
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Summary
This summary is machine-generated.

This study introduces a novel deep learning network using stationary wavelet transform for low-dose computed tomography (CT) denoising. The method enhances both image accuracy and realism, outperforming existing techniques.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Signal Processing

Background:

  • Deep neural networks (DNNs) have advanced low-dose computed tomography (CT) denoising.
  • Traditional methods often sacrifice image realism for accuracy or vice versa.
  • A better trade-off between perceptual quality and distortion is needed.

Purpose of the Study:

  • To develop a single network for accurate and realistic low-dose CT images.
  • To improve the perception-distortion trade-off in CT image denoising.
  • To achieve high objective and perceptual quality simultaneously.

Main Methods:

  • Proposed a stationary wavelet transform-assisted network.
  • Utilized frequency subband-specific losses in the wavelet domain.
  • Employed a two-stage training process with objective and perceptual loss functions.

Main Results:

  • Achieved denoised CT images with high perceptual quality.
  • Minimized the loss in objective image quality.
  • Demonstrated superior performance over state-of-the-art algorithms on phantom and clinical images.

Conclusions:

  • The proposed network effectively balances objective and perceptual quality in low-dose CT denoising.
  • Stationary wavelet transform and frequency subband-specific losses are crucial for improved results.
  • This approach offers a significant advancement in medical image processing.