Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

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...

Curvilinear Motion: Rectangular Components

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the time...

Imaging Studies III: Computed Tomography

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...

You might also read

Related Articles

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

Sort by

Same author

Image optimization for low-dose <sup>18</sup>F-FDG breast PET/MRI using deep learning: a pilot study.

EJNMMI physics·2026

Same author

AI-augmented thyroid scintigraphy for robust classification of disease.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026

Same author

Cycle-dependent variation of tumor absorbed dose rates in<sup>177</sup>Lu-DOTATATE therapies.

Biomedical physics & engineering express·2026

Same author

Beyond the tumor: recurrence-prone radiomics for prognostication in negative PSMA PET/CT scans of prostate cancer.

Biomedical physics & engineering express·2026

Same author

Kernel-based maximum likelihood reconstruction of attenuation and activity (MLAA) in SPECT imaging for improved attenuation correction and activity quantification: simulation, phantom and patient validation studies.

Physics in medicine and biology·2026

Same author

A clinically anchored radiomics dictionary for explainable TI-RADS-based thyroid nodule classification in ultrasound; dictionary version TU1.0.

European journal of radiology·2026

Search research articles

Related Experiment Video

Updated: May 9, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian

Abolfazl Mehranian¹, Hamidreza Saligheh Rad, Arman Rahmim

¹Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.

Magnetic Resonance Imaging

|July 30, 2013

Summary

A new regularization technique using the smoothly clipped absolute deviation (SCAD) norm enhances compressed sensing (CS) for faster Magnetic Resonance Imaging (MRI). This method reduces aliasing artifacts more effectively than the standard l1 norm, especially at lower sampling rates.

Keywords:

Augmented Lagrangian Compressed sensing Smoothly Clipped Absolute Deviation (SCAD)Total variation

More Related Videos

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

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

Published on: May 24, 2021

Related Experiment Videos

Last Updated: May 9, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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

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

Published on: May 24, 2021

Area of Science:

Medical Imaging
Signal Processing
Optimization

Background:

Compressed sensing (CS) accelerates Magnetic Resonance Imaging (MRI) by reconstructing images from undersampled data.
Sparsity-promoting regularization is crucial for CS-MRI, leveraging image compressibility in transform domains.
Conventional methods often use the l1 norm, which has limitations at very low sampling rates.

Purpose of the Study:

Introduce a novel regularization technique based on iterative linearization of the non-convex smoothly clipped absolute deviation (SCAD) norm for CS-MRI.
Aim to achieve lower sampling rates than achievable with the conventional l1 norm while approximating l0 norm properties.
Enhance the sparsity-promoting capabilities for improved MR image reconstruction.

Main Methods:

Formulate CS-MRI reconstruction as an equality-constrained optimization problem.
Employ a variable splitting technique and an augmented Lagrangian (AL) method for efficient optimization.
Decompose the problem into sub-problems, linearizing the SCAD norm to yield an adaptively weighted soft thresholding rule.

Main Results:

The SCAD-based algorithm adaptively assigns lower weights to gradient fields and wavelet coefficients.
This adaptive weighting proves more effective in reducing aliasing artifacts caused by k-space undersampling compared to the l1-based method.
Phantom and clinical studies demonstrate the superior performance of the SCAD approach.

Conclusions:

The proposed SCAD regularization significantly improves upon the performance of l1-based techniques in CS-MRI.
It is particularly beneficial at reduced sampling rates, offering better image quality.
SCAD regularization presents a promising alternative for specific CS-MRI applications requiring high acceleration factors.