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 Experiment Video

Updated: Jun 1, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Modelling prostate motion for data fusion during image-guided interventions.

Yipeng Hu1, Timothy J Carter, Hashim Uddin Ahmed

  • 1UCL Centre for Medical Image Computing, the Departmentof Medical Physics and Bioengineering, and the Department of ComputerScience, University College London, UK. yipeng.hu@ucl.ac.uk

IEEE Transactions on Medical Imaging
|June 3, 2011
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

PromptReg: Interactive Registration by "Corresponding Prompts" for Segment Anything Model (SAM).

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Prostate MRI quality improvement: a Roadmap from the ESUR Prostate MRI Working Group.

European radiology·2026
Same author

MRI for Active Surveillance in Prostate Cancer: How I Do It.

Radiology·2026
Same author

Biomechanics-Informed Non-Rigid Medical Image Registration With Elasticity Theories.

IEEE transactions on medical imaging·2026
Same author

Biparametric vs Multiparametric MRI for Prostate Cancer Diagnosis: The PRIME Diagnostic Clinical Trial.

JAMA·2025
Same author

Prostate Pathway Embedded Comparative Trial: Outcomes from the Pilot Phase of the Imperial Prostate 3-PROState Pathway Embedded Comparative Trial.

European urology focus·2025
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
See all related articles

A new statistical motion model accurately predicts and compensates for prostate deformation during transrectal ultrasound (TRUS) procedures. This improves image registration for cancer treatments, requiring fewer data points for successful results.

Area of Science:

  • Medical imaging and image analysis
  • Computational anatomy and biomechanics
  • Prostate cancer treatment and diagnostics

Background:

  • Clinical need for precise image registration in prostate cancer interventions (biopsy, ablation).
  • Transrectal ultrasound (TRUS) guidance causes significant prostate gland deformation.
  • Existing methods struggle to accurately compensate for TRUS-induced motion.

Purpose of the Study:

  • To investigate the efficacy of a statistical shape/motion model for predicting and compensating prostate deformation.
  • To compare the accuracy of this model against alternative elastic deformation methods.
  • To assess the model's performance in multimodal data fusion for interventional applications.

Main Methods:

  • Developed a statistical shape/motion model trained using finite element simulations.

More Related Videos

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance
05:19

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance

Published on: November 7, 2025

Related Experiment Videos

Last Updated: Jun 1, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance
05:19

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance

Published on: November 7, 2025

  • Acquired 3D ultrasound images from five patient prostates before and after TRUS-induced deformation.
  • Employed a nonrigid, surface-based registration method to compare deformation models.

    • Main Results:

    • The statistical motion model significantly outperformed alternative elastic deformation methods in accuracy and robustness.
    • Successful registration was achieved with substantially fewer target surface points compared to other methods.
    • Achieved a mean final target registration error of 1.8 mm based on anatomical landmarks.

    Conclusions:

    • A statistical model of prostate deformation offers an accurate, rapid, and robust solution for interventional applications.
    • The model effectively predicts prostate deformation from sparse surface data, enabling precise image registration.
    • This approach is well-suited for deformation compensation in image-guided prostate cancer treatments.