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

9.0K
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...
9.0K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

424
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...
424
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

951
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...
951
Brain Imaging01:14

Brain Imaging

763
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
763
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

652
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
652

You might also read

Related Articles

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

Sort by
Same author

Introduction to the Special Issue on Big Data and the Internet of Things in Complex Information Systems.

Big data·2025
Same author

An Exploration into Human-Computer Interaction: Hand Gesture Recognition Management in a Challenging Environment.

SN computer science·2023
Same author

Knowledge Graph and Deep Learning-based Text-to-GQL Model for Intelligent Medical Consultation Chatbot.

Information systems frontiers : a journal of research and innovation·2022
Same author

Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access.

IEEE internet of things journal·2022
Same author

A Systematic Literature Review on Distributed Machine Learning in Edge Computing.

Sensors (Basel, Switzerland)·2022
Same author

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms.

Neural computing & applications·2022
Same journal

Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.

Journal of medical systems·2026
Same journal

Predicting the Predictor: Unresolved Validity Threats in LLM-Based ASA Classification.

Journal of medical systems·2026
Same journal

Development and Internal Validation of a Vectorcardiography-Augmented Model for 12-Month Major Adverse Cardiovascular Events in Chronic Heart Failure.

Journal of medical systems·2026
Same journal

Development and Validation of an Automated Acute Kidney Injury E-Alert System Integrated with Clinical Decision Support for Hospitalized Patients.

Journal of medical systems·2026
Same journal

Calibration of Self-Reported Confidence and Accuracy of Large Language Models in Medical Question Answering.

Journal of medical systems·2026
Same journal

Throughput Benchmarking and Throughput Variance Analysis to Evaluate the Efficiency of an Outpatient Endoscopy Unit.

Journal of medical systems·2026
See all related articles

Related Experiment Video

Updated: Feb 18, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K

Computational Intelligence for Medical Imaging Simulations.

Victor Chang1

  • 1International Business School Suzhou, Xi'an Jiaotong-Liverpool University, Suzhou, China. victorchang.research@gmail.com.

Journal of Medical Systems
|November 28, 2017
PubMed
Summary
This summary is machine-generated.

Computational intelligence enables advanced medical imaging simulations for cancer and immunity research, offering efficient data processing and visualization for complex biological systems.

Keywords:
Computational intelligenceGene, protein and immunity inspection via simulationsMapReduce framework with a fusion algorithmSimulations of medical imaging

More Related Videos

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

285
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.6K

Related Experiment Videos

Last Updated: Feb 18, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K
Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

285
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.6K

Area of Science:

  • Computational intelligence
  • Medical imaging
  • Bioinformatics

Background:

  • Traditional methods face limitations in simulating complex biological processes.
  • Exploring genes and proteins involved in cancer development and immunity requires advanced simulation techniques.

Purpose of the Study:

  • To describe the simulation of medical imaging using computational intelligence.
  • To present simulations of specific genes (BIRC3, BIRC6, CCL4, KLKB1, CYP2A6) and brain imaging.
  • To introduce a novel MapReduce framework with a fusion algorithm for medical imaging simulation.

Main Methods:

  • Development of a MapReduce framework incorporating a fusion algorithm (M-Fusion and M-Update).
  • Simulation of biological unit aggregation, akin to digital surface theories.
  • Virtual inspection and analysis of selected genes and brain imaging data.

Main Results:

  • The proposed framework successfully simulates medical imaging and virtual biological units.
  • The fusion algorithm demonstrates efficient performance, processing 40 GB of data in 600 seconds.
  • Detailed outputs and explanations for simulated genes and brain segment intensity are provided.

Conclusions:

  • Computational intelligence offers effective and efficient solutions for healthcare research through simulation and visualization.
  • The developed framework enhances the capability to explore complex biological systems and medical imaging data.