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...

You might also read

Related Articles

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

Sort by

Same author

Intravascular large B-cell lymphoma mimicking an orbital mass lesion and bilateral cavernous sinus syndrome: A case report.

Oncology letters·2026

Same author

Aorto-azygous Fistula Complicated by Arteriovenous Aneurysm.

Radiology. Cardiothoracic imaging·2026

Same author

A Large-Scale Cryo-EM RNA Motif Dataset and Benchmark for Machine Learning-Based Structure Modeling.

bioRxiv : the preprint server for biology·2026

Same author

Association of Longitudinal High-Sensitivity Cardiac Troponin T With Clinical Outcomes in Adult Heart-Transplant Recipients.

Journal of the American Heart Association·2026

Same author

Bioinspired Spatiotemporal-Responsive Nanoparticles Synergistically Regulate Mitochondrial Repair and Inhibit Fibrosis in Acute Myocardial Infarction Treatment.

Advanced healthcare materials·2026

Same author

Ovarian cancer drives TLR5-dependent expansion of myeloid progenitors through systemic ligand dissemination.

Journal of leukocyte biology·2025

Search research articles

Related Experiment Video

Updated: Aug 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Superiorization-inspired unrolled SART algorithm with U-Net generated perturbations for sparse-view and limited-angle

Yiran Jia¹, Noah McMichael¹, Pedro Mokarzel¹

¹School of STEM, University of Washington Bothell, Bothell, WA 98011, United States of America.

Physics in Medicine and Biology

|December 21, 2022

Summary

This study introduces a novel unrolled algorithm for computed tomography (CT) image reconstruction, inspired by superiorization methodology and deep learning. The new method shows improved performance in limited-angle CT and comparable results in sparse-view CT.

Keywords:

computed tomography deep learning iterative reconstruction limited angle sparse view

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: Aug 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Unrolled algorithms are effective for challenging computed tomography (CT) image reconstruction tasks like low-dose, sparse-view, and limited-angle imaging.
These algorithms integrate iterative reconstruction methods into neural networks, learning regularizers from data.
Existing methods often focus on sparse-view or low-dose CT, with less exploration in limited-angle scenarios.

Purpose of the Study:

Propose a novel unrolled algorithm for CT image reconstruction.
Compare its performance against existing methods in sparse-view and limited-angle CT.
Investigate the integration of deep learning with superiorization methodology for image reconstruction.

Main Methods:

Developed a novel unrolled algorithm inspired by superiorization methodology.
Utilized a modified U-net architecture to introduce learned perturbations during reconstruction.
Trained the network end-to-end in a supervised manner.
Evaluated performance on numerical experiments simulating sparse-view and limited-angle CT.

Main Results:

The proposed algorithm achieved excellent results in both sparse-view and limited-angle CT scenarios.
It outperformed several competing unrolled methods in limited-angle CT reconstruction.
Performance was comparable or superior to existing methods in sparse-view CT.

Conclusions:

The novel unrolled algorithm demonstrates significant potential for CT image reconstruction, particularly in challenging limited-angle cases.
This work is a foundational step in applying deep learning within the superiorization methodology.
The study highlights the impact of network architecture and the effectiveness of unrolled methods in limited-angle CT.