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Computed Tomography01:10

Computed Tomography

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...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

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Related Experiment Video

Updated: Jun 3, 2026

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
07:58

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

An efficient algorithm for denoising MR and CT images using digital curvelet transform.

S Ali Hyder1, R Sukanesh

  • 1Research Scholar, Anna University, Chennai, Tamil Nadu, India.

Advances in Experimental Medicine and Biology
|March 25, 2011
PubMed
Summary
This summary is machine-generated.

Curvelet transform effectively denoises medical images like MRI and CT scans, outperforming wavelet methods in visual quality and signal-to-noise ratio for clearer edge and feature recovery.

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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Related Experiment Videos

Last Updated: Jun 3, 2026

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
07:58

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Area of Science:

  • Medical Imaging
  • Image Processing
  • Signal Processing

Background:

  • Medical imaging techniques like Magnetic Resonance (MR) and Computed Tomography (CT) are crucial for diagnosis.
  • Image noise reduction is essential for accurate interpretation and analysis.
  • Traditional denoising methods may struggle with complex image features.

Purpose of the Study:

  • To introduce and evaluate a curvelet transform-based approach for denoising MR and CT images.
  • To compare the effectiveness of curvelet transform against wavelet transform methods for image denoising.
  • To assess the impact of curvelet denoising on image quality, particularly edge and feature recovery.

Main Methods:

  • Application of curvelet transform, a multiscale representation optimized for curved discontinuities.
  • Denoising of standard MR and CT images corrupted by white, random, and Poisson noise.
  • Simple thresholding of curvelet coefficients for noise removal.
  • Comparative analysis with state-of-the-art wavelet transform techniques.

Main Results:

  • Curvelet-based denoising demonstrated competitive performance against wavelet methods.
  • Curvelet reconstructions yielded superior perceptual quality, including sharper images.
  • Enhanced recovery of edges and faint linear/curvilinear features was observed.
  • Improved Peak Signal-to-Noise Ratio (PSNR) compared to wavelet-based methods.

Conclusions:

  • Curvelet transform is a highly effective tool for denoising MR and CT images.
  • Its ability to handle curved features makes it particularly suitable for medical image analysis.
  • The method offers significant advantages in both visual quality and quantitative metrics over traditional wavelet approaches.