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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.
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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Imaging Studies III: Computed Tomography01:27

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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning based correction of low performing pixel in computed tomography.

Bhushan D Patil1, Vanika Singhal1, Utkarsh Agrawal1

  • 1Advanced Technology Group, GE Healthcare, Bangalore, India.

Biomedical Physics & Engineering Express
|August 8, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning method effectively removes artifacts caused by low performing pixels (LPPs) in CT detector data. This sinogram-domain approach significantly improves image quality and diagnostic usability.

Keywords:
LPP: low performing pixelcomputed tomographydeep learningring artifact reduction

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Deep Learning

Background:

  • Low performing pixels (LPPs) in CT detectors create artifacts like rings and streaks, degrading image quality.
  • These artifacts obscure diagnostic information, rendering CT images unusable.
  • Current artifact removal methods often operate in the reconstructed image domain, limiting their effectiveness.

Purpose of the Study:

  • To develop a supervised deep learning algorithm for removing CT image artifacts caused by LPPs.
  • To perform artifact correction in the sinogram domain for enhanced efficacy.
  • To address the impact of LPPs, particularly those near the detector iso-center.

Main Methods:

  • A supervised deep learning algorithm was developed to operate in the sinogram domain.
  • The method utilizes CT scan geometry and conjugate ray information for sinogram interpolation.
  • Experiments were conducted on data from a GE RevACT multi-slice CT system with a flat-panel detector.

Main Results:

  • The deep learning method demonstrated significant reduction in ring artifacts across various LPP locations.
  • Simulated LPPs (5% and 10% of channels) showed substantial artifact reduction.
  • An approximate 5dB improvement in signal-to-noise ratio (SNR) was observed in both sinogram and reconstruction domains compared to traditional interpolation methods.

Conclusions:

  • The proposed sinogram-domain deep learning algorithm effectively mitigates artifacts from LPPs in CT images.
  • This method offers improved perceptual image quality and diagnostic utility.
  • The approach shows promise for enhancing CT image analysis, especially for high-frequency regions.