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

Fast Fourier Transform01:10

Fast Fourier Transform

The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
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...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

You might also read

Related Articles

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

Sort by
Same author

Evaluation of tumor localization accuracy on fast ring-gantry cone-beam computed tomography using patient-specific breathing curves and a dynamic anthropomorphic thorax phantom.

Physics and imaging in radiation oncology·2026
Same author

<i>A posteriori</i>and predictive interplay evaluation methodology for lung and esophageal cancer patients treated in free breathing with IMPT.

Physics in medicine and biology·2026
Same author

A standardized workflow for the development and manufacturing of tissue-equivalent anthropomorphic phantoms for radiotherapy using 3D-printing.

Physics in medicine and biology·2026
Same author

Assessing dosimetric uncertainties in Papillon+ contact x-ray brachytherapy for rectal cancer: impact of beam quality and tumour geometry.

Physics in medicine and biology·2026
Same author

Evaluation of proton range differences in photon-counting and dual-energy computed tomography across imaging doses and anthropomorphic phantom sizes.

Physics in medicine and biology·2026
Same author

Evaluation of radiological properties and anisotropy with air channels analysis in 3D-printed flexible lung-mimicking materials for radiotherapy.

Physics in medicine and biology·2026

Related Experiment Video

Updated: May 30, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations.

Lucas C G G Persoon1, Mark Podesta, Wouter J C van Elmpt

  • 1Department of Radiation Oncology (MAASTRO), GROW-Schoolfor Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.

Medical Physics
|August 24, 2011
PubMed
Summary
This summary is machine-generated.

Graphics processing unit (GPU) acceleration significantly speeds up 3D gamma evaluations for radiation therapy dose comparisons. This GPU implementation achieves substantial calculation time reductions without compromising accuracy, enhancing clinical workflow efficiency.

More Related Videos

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

Related Experiment Videos

Last Updated: May 30, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

Area of Science:

  • Medical Physics
  • Radiotherapy
  • Computational Science

Background:

  • The gamma evaluation is a standard method for quantifying dose distribution differences in radiotherapy.
  • Current gamma implementations predominantly rely on central processing units (CPUs), which can be computationally intensive.
  • Graphics processing units (GPUs) offer parallel processing capabilities suitable for accelerating complex calculations.

Purpose of the Study:

  • To implement a 3D gamma evaluation algorithm on a GPU to reduce calculation time.
  • To compare the computational performance of a GPU-based gamma evaluation against a CPU-based implementation.
  • To assess the accuracy of GPU-accelerated gamma evaluation compared to traditional CPU methods.

Main Methods:

  • The 3D gamma evaluation algorithm was developed using NVIDIA's Compute Unified Device Architecture (CUDA) on an NVIDIA Tesla C2050 GPU.
  • Simulations involved virtual phantoms with varying dose distributions and clinical patient cases comparing treatment planning system (TPS) data with delivered doses.
  • Calculation times were compared between CPU and GPU implementations, exploring different thread-block sizes and memory access methods (texture vs. global memory).

Main Results:

  • A significant speed-up was observed, with the GPU achieving a 66 +/- 12 times faster calculation for virtual phantoms and 57 +/- 15 times faster for patient cases compared to the CPU.
  • Optimal performance was achieved with a thread-block size of 16x16.
  • Utilizing texture memory further enhanced calculation speed, particularly when interpolation was involved.
  • Differences in gamma values between CPU and GPU calculations were negligible, indicating high accuracy.

Conclusions:

  • GPU implementation considerably decreases calculation time for 3D gamma evaluations.
  • The use of GPU features, such as texture memory, optimizes performance without sacrificing accuracy.
  • This GPU-accelerated approach offers a practical solution for faster and efficient dose distribution analysis in clinical radiotherapy.