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

Imaging Studies IV: Magnetic Resonance Imaging

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

You might also read

Related Articles

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

Sort by

Same author

MedSegAgent: A Universal and Scalable Multi-Agent System for Instructive Medical Image Segmentation.

IEEE journal of biomedical and health informatics·2026

Same author

The application of CT-free electromagnetic navigation-guided percutaneous puncture localization of pulmonary nodules.

Surgical endoscopy·2025

Same author

Real-Time 3D Ultrasound Reconstruction Using U2-Net Segmentation for Cardiovascular Interventional Surgery<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Revisiting model scaling with a U-net benchmark for 3D medical image segmentation.

Scientific reports·2025

Same author

FIND: A Framework for Iterative to Non-Iterative Distillation for Lightweight Deformable Registration.

IEEE journal of biomedical and health informatics·2025

Same author

A-Eval: A benchmark for cross-dataset and cross-modality evaluation of abdominal multi-organ segmentation.

Medical image analysis·2025

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

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

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

Published on: March 21, 2025

E-CMCA and LSTM-Enhanced Framework for Cross-Modal MRI-TRUS Registration in Prostate Cancer.

Ciliang Shao¹, Ruijin Xue¹, Lixu Gu¹

¹Pittsburgh Institute, Sichuan University, Chengdu 610207, China.

Journal of Imaging

|September 26, 2025

Summary

This summary is machine-generated.

This study introduces a novel framework for accurate cross-modal MRI-TRUS image registration, improving prostate cancer diagnosis and biopsy guidance. The method excels in both static 3D and dynamic 4D imaging tasks.

Keywords:

MRI-TRUS registration cross-modal attention deep learning feature fusion long-short term memory prostate cancer diagnosis

More Related Videos

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Jan 16, 2026

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

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

Published on: March 21, 2025

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

Medical Imaging
Biomedical Engineering
Computer Vision

Background:

Accurate registration of Magnetic Resonance Imaging (MRI) and Transrectal Ultrasound (TRUS) images is vital for prostate cancer diagnosis and biopsy.
Modality differences and non-rigid deformations present significant challenges, particularly in dynamic imaging scenarios.

Purpose of the Study:

To develop a novel cross-modal MRI-TRUS registration framework that addresses challenges in dynamic imaging.
To enhance the accuracy and robustness of image registration for improved clinical applications.

Main Methods:

A dual-encoder architecture incorporating an Enhanced Cross-Modal Channel Attention (E-CMCA) module for feature extraction and integration.
An LSTM-Based Spatial Deformation Modeling Module to process temporal dynamics in depth-sliced 3D deformation fields.
Implementation of VecInt for smooth, diffeomorphic transformations and FuseConv for enhanced feature integration.

Main Results:

The proposed model achieved a Dice Similarity Coefficient (DSC) of 0.865 and a Robust DSC (RDSC) of 0.898.
Target Registration Error (TRE) was 2.278 mm, and Robust TRE (RTRE) was 1.293 mm.
Demonstrated superior performance over state-of-the-art methods in both static 3D and dynamic 4D registration tasks on the μ-RegPro dataset.

Conclusions:

The novel framework significantly improves cross-modal MRI-TRUS registration accuracy and robustness.
The model's ability to handle temporal dynamics makes it suitable for dynamic 4D imaging in prostate interventions.
This advancement holds promise for enhancing the precision of prostate cancer diagnosis and image-guided biopsies.