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Related Concept Videos

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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Related Experiment Video

Updated: Jun 1, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI.

Changwei Hu1, Xiaobo Qu, Di Guo

  • 1Department of Electronic Science, Fujian Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.

Magnetic Resonance Imaging
|June 7, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel wavelet-based algorithm for faster Magnetic Resonance Imaging (MRI) reconstruction. The method effectively reduces aliasing artifacts and noise while preserving crucial edge details for improved diagnostic accuracy.

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Last Updated: Jun 1, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Published on: September 3, 2021

Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging
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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Undersampling k-space in Magnetic Resonance Imaging (MRI) accelerates data acquisition but introduces aliasing artifacts.
  • These artifacts can obscure critical edge information in images, hindering clinical diagnosis.
  • MRI data is also susceptible to noise, further degrading image quality.

Purpose of the Study:

  • To develop a novel wavelet-based algorithm for reconstructing undersampled MRI data.
  • To enhance edge preservation while simultaneously suppressing aliasing artifacts and noise.
  • To introduce an automatic thresholding method adaptive to noise intensity.

Main Methods:

  • The algorithm formulates image reconstruction as an optimization problem with L2 data fidelity and L1 sparsity regularization.
  • A novel automatic threshold estimation, adaptive to noise intensity, is incorporated.
  • A prior matrix leveraging edge correlation in the wavelet domain is integrated into the regularization.

Main Results:

  • The proposed algorithm demonstrates superior performance compared to several existing reconstruction methods.
  • It achieves better edge recovery in reconstructed MRI images.
  • Effective suppression of aliasing artifacts and noise is observed.

Conclusions:

  • The developed wavelet-based algorithm offers improved MRI reconstruction from undersampled data.
  • It provides a robust solution for preserving diagnostic image features while reducing acquisition time.
  • The automatic noise-adaptive thresholding and edge-prior incorporation enhance reconstruction quality.