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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Related Experiment Video

Updated: Dec 6, 2025

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
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A modular and scalable computational framework for interactive immersion into imaging data with a holographic

Jose D Velazco-Garcia1, Dipan J Shah2, Ernst L Leiss1

  • 1MRI Lab, Dept. of CS, University of Houston, 4800 Calhoun Road PGH 501, Houston, TX, USA.

Computer Methods and Programs in Biomedicine
|October 12, 2020
PubMed
Summary

This study introduces the FI3D framework for interactive augmented reality (AR) visualization of 3D/4D medical imaging data. The framework enables real-time processing and fusion of complex datasets for enhanced medical applications.

Keywords:
Augmented realityComputational frameworkHuman-cyber InterfaceInteractive ImmersionMedical imagingRendering and visualization

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Area of Science:

  • Medical Imaging
  • Augmented Reality
  • Data Visualization

Background:

  • Modern imaging scanners generate large 3D/4D multimodal datasets.
  • Visualizing fused images, segmentations, and information from these datasets is challenging.
  • Augmented reality (AR) with head-mounted displays (HMDs) shows potential for immersive data interaction.

Purpose of the Study:

  • To describe the FI3D framework for interactive immersion with medical imaging data.
  • To integrate image processing, analytics, and rendering with an AR interface.
  • To provide a foundation for AR-based medical applications.

Main Methods:

  • The FI3D framework was designed with modules for peripheral communication (scanners, HMDs) and computational power.
  • A dedicated computational unit handles demanding processes in real-time.
  • HMDs serve as display and input peripherals (gestures, voice commands), with customizable processing modules.

Main Results:

  • The FI3D framework was utilized to process, render, and visualize 4D CINE MRI cardiac data.
  • Left ventricle endocardium and epicardium were segmented using machine learning and visualized in 4D on a HoloLens HMD.
  • The system achieved a sustained image stream of 1 image/sec (512x512 resolution) and hologram updates at 62.5 Hz, supporting segmentation tasks.

Conclusions:

  • A system design and framework (FI3D) were developed for AR visualization in medical applications.
  • The framework addresses technical challenges in the developmental pipeline for AR medical tools.
  • FI3D serves as a foundation for future AR-enhanced medical imaging applications.