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

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...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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...
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

You might also read

Related Articles

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

Sort by
Same author

Disrupted brain functional network topology and potential network reorganization in early-stage Parkinson's disease with probable REM sleep behavior disorder.

Neuroscience·2026
Same author

From anxiety to innovation: the effect of perceived involution on digital creativity via the parallel mediation of relative deprivation and constructive deviance.

Frontiers in psychology·2026
Same author

Mental health in Kindergarten to Grade 12 schools during the COVID-19 crisis: Literature review and survey suggesting multi-tiered support systems.

Medical research archives·2026
Same author

Reflections on Visualizing the COVID-19 Pandemic for the Public.

IEEE computer graphics and applications·2026
Same author

Sesaminol Ameliorates Metabolic and Alcohol-Related Liver Injury by Activating the PPARα/Slc27a5 Axis-Driven Hepatic Fatty Acid β-Oxidation.

Molecular nutrition & food research·2026
Same author

Dynamic Functional Connectivity Changes in Parkinson's Disease With and Without Depression.

The European journal of neuroscience·2026
Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026
Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026
Same journal

A novel optical respiratory gating system with a hybrid phase-amplitude algorithm for spot-scanning proton therapy.

Medical physics·2026
Same journal

Gamma Knife treatment planning using knowledge-based reinforcement learning.

Medical physics·2026
Same journal

Development and characterization of a novel, small animal external beam irradiator using a clinical high dose rate brachytherapy source.

Medical physics·2026
Same journal

Deep learning-based dose prediction for MR-guided prostate SIB: Supporting rapid feasibility assessment and adaptive editing margin selection.

Medical physics·2026
See all related articles

Related Experiment Videos

Creating optimal code for GPU-accelerated CT reconstruction using ant colony optimization.

Eric Papenhausen1, Ziyi Zheng, Klaus Mueller

  • 1Center of Visual Computing, Computer Science Department, Stony Brook University, Stony Brook, New York 11794-4400, USA. epapenhausen@cs.sunysb.edu

Medical Physics
|March 8, 2013
PubMed
Summary
This summary is machine-generated.

This study presents a framework using ant colony optimization to automatically fine-tune GPU-accelerated CT reconstruction algorithms. The framework optimizes code for efficiency and readability, reducing manual effort and improving programmer productivity.

Related Experiment Videos

Area of Science:

  • Medical Imaging
  • Computer Science
  • Algorithm Optimization

Background:

  • GPU-accelerated CT reconstruction algorithms are sensitive to hardware and implementation details.
  • Manual fine-tuning is time-consuming and requires frequent updates with hardware changes.
  • Existing automatic fine-tuning techniques are limited in scope and accuracy.

Purpose of the Study:

  • To present a flexible framework for automating GPU code optimization in CT reconstruction.
  • To produce efficient and readable code with minimal user intervention.
  • To reduce the overhead associated with leveraging hardware-specific resources.

Main Methods:

  • Utilized ant colony optimization (ACO) to search for optimal implementation paths within a graph representation.
  • Input a graph where paths correspond to potential CT reconstruction implementations.
  • Optimized based on execution time and image reconstruction quality.

Main Results:

  • Successfully optimized GPU-accelerated FDK and separable footprint backprojection implementations.
  • Demonstrated that optimal implementations vary based on specific hardware.
  • Framework-generated results were comparable to manual optimization efforts.

Conclusions:

  • The proposed framework enhances programmer productivity for GPU-accelerated CT reconstruction.
  • It intelligently searches for optimal implementations, reducing development time.
  • The framework yields efficient and readable code, adaptable to different hardware.