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 I: CT and MRI

Imaging Studies I: CT and MRI

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...

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

New Growth, New Opportunities.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

White matter hyperintensities and relapse risk in late-life depression.

Journal of affective disorders·2025

Same author

Unsupervised discovery of clinical disease signatures using probabilistic independence.

Journal of biomedical informatics·2025

Same author

Multi-contrast computed tomography atlas of healthy pancreas with dense displacement sampling registration.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

The effect of Alzheimer's disease genetic factors on limbic white matter microstructure.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

IS&T International Symposium on Electronic Imaging·2025

Same journal

From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding.

Tianyuan Yao¹, Zhiyuan Li², Praitayini Kanakaraj¹

¹Department of Computer Science, Vanderbilt University, Nashville, TN, USA.

Proceedings of Spie--The International Society for Optical Engineering

|December 1, 2025

Summary

This summary is machine-generated.

A new Polyhedra Encoding Transformer (PE-Transformer) method improves diffusion MRI analysis by handling spherical signals. This novel approach enhances accuracy in estimating brain microstructural properties and structural connectivity.

Keywords:

Deep learning Diffusion MRI Estimation Transformer

More Related Videos

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

Related Experiment Videos

Last Updated: Jan 9, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

Area of Science:

Neuroimaging
Biomedical Engineering
Computer Vision

Background:

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is crucial for noninvasive brain microstructural and connectivity analysis.
Machine learning enhances dMRI analysis speed and accuracy, but traditional models struggle with unique gradient encoding distributions.
Existing deep learning methods often use unsuitable embeddings, neglecting dMRI's specific signal characteristics.

Purpose of the Study:

To introduce a novel deep learning method, the Polyhedra Encoding Transformer (PE-Transformer), specifically designed for dMRI data.
To address the limitations of traditional deep learning models in analyzing dMRI's spherical signals and gradient encodings.
To improve the accuracy of estimating brain microstructural properties and structural connectivity using dMRI.

Main Methods:

Developed the PE-Transformer, which resamples spherical signals using an icosahedral projection onto a unit sphere.
Generated embeddings from these resampled signals, incorporating orientational information from the icosahedral structure.
Utilized a transformer encoder to process these specialized embeddings for dMRI analysis.

Main Results:

PE-Transformer demonstrated superior accuracy in estimating multi-compartment models compared to conventional methods.
The method achieved higher accuracy in estimating Fiber Orientation Distributions (FOD) across various gradient encoding protocols.
Outperformed standard Convolutional Neural Network (CNN) architectures and traditional transformer models in dMRI analysis tasks.

Conclusions:

The PE-Transformer offers a significant advancement for dMRI data analysis, particularly for handling spherical signals.
This method provides more accurate estimations of brain microstructural properties and structural connectivity.
PE-Transformer represents a promising data-driven approach for enhancing neuroimaging analysis.