Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Computed Tomography01:10

Computed Tomography

7.9K
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...
7.9K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

261
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...
261

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Exosomes-Derived MiR-302b Suppresses Lung Cancer Cell Proliferation and Migration via TGFβRII Inhibition.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology·2016
Same author

Communication: Mode specific quantum dynamics of the F + CHD3 → HF + CD3 reaction.

The Journal of chemical physics·2016
Same author

New ab initio adiabatic potential energy surfaces and bound state calculations for the singlet ground X̃(1)A1 and excited C̃(1)B2(2(1)A(')) states of SO2.

The Journal of chemical physics·2016
Same author

Rate Coefficients of the HCl + OH → Cl + H2O Reaction from Ring Polymer Molecular Dynamics.

The journal of physical chemistry. A·2016
Same author

Comparison of multislice breath-hold and 3D respiratory triggered T1 ρ imaging of liver in healthy volunteers and liver cirrhosis patients in 3.0 T MRI.

Journal of magnetic resonance imaging : JMRI·2016
Same author

Exponential Arithmetic Based Self-Healing Group Key Distribution Scheme with Backward Secrecy under the Resource-Constrained Wireless Networks.

Sensors (Basel, Switzerland)·2016
Same journal

Systematic comparison of MPRAGE and BRAVO T1-weighted MRI pulse sequences and brain morphometry in high-risk young adults.

Magnetic resonance imaging·2026
Same journal

Foot dynamic contrast-enhanced MRI for assessing microcirculatory changes after endovascular therapy in peripheral artery disease: A prospective pilot study.

Magnetic resonance imaging·2026
Same journal

Reconstruction of MRI from undersampled k-spaces of double-contrast volume acquisitions using deep neural networks.

Magnetic resonance imaging·2026
Same journal

Radiofrequency-induced heating safety of brain MRI scans at 7 T in the presence of a shoulder implant.

Magnetic resonance imaging·2026
Same journal

Incremental diagnostic value of microstructural time-dependent diffusion MRI in differentiating PCNSL from glioblastoma over conventional MRI.

Magnetic resonance imaging·2026
Same journal

Enhanced respiratory motion compensation in free-breathing dynamic contrast-enhanced MRI with GROG-facilitated bunch phase encoding and Golden angle radial sampling.

Magnetic resonance imaging·2026
See all related articles

Related Experiment Video

Updated: Jan 9, 2026

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

689

Adaptive regularization weight selection for compressed sensing MRI reconstruction.

Yuan Lian1, Yuancheng Jiang1, Hua Guo1

  • 1Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.

Magnetic Resonance Imaging
|December 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive method to automatically select regularization weights for compressed sensing MRI reconstruction. This approach improves image quality by reducing errors and artifacts without manual tuning.

Keywords:
Compressed SensingImage reconstructionThreshold selection

More Related Videos

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

20.0K
Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
11:13

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Published on: May 24, 2021

7.0K

Related Experiment Videos

Last Updated: Jan 9, 2026

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

689
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

20.0K
Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
11:13

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Published on: May 24, 2021

7.0K

Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Compressed Sensing (CS) MRI relies on accurate regularization weights for high-quality image reconstruction.
  • Current methods often require manual selection of these weights, which is time-consuming and suboptimal.

Purpose of the Study:

  • To develop an automatic and adaptive method for selecting regularization weights in CS-MRI reconstruction.
  • To enhance image reconstruction quality by optimizing regularization parameters dynamically.

Main Methods:

  • A Bayesian statistical model was developed, integrating prior knowledge of noise and wavelet coefficient distributions.
  • An adaptive regularization weight was derived using a maximum a posteriori estimator based on coefficient and noise variances.
  • The method dynamically adjusts weights across subjects, slices, iterations, and wavelet sub-bands.

Main Results:

  • The proposed adaptive method demonstrated superior performance in retrospective and prospective studies.
  • It significantly reduced reconstruction errors compared to fixed weights and SCoRe.
  • The method effectively recovered signals from noise-like artifacts and saved time on weight selection.

Conclusions:

  • An adaptive regularization weight selection method for CS-MRI reconstruction has been successfully developed.
  • This method provides optimal, subject-specific, and iteration-dependent weights automatically.
  • It eliminates the need for manual intervention in the weight selection process, improving efficiency and potentially image quality.