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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: May 24, 2026

Adaptation of a Haptic Robot in a 3T fMRI

Adaptation of a Haptic Robot in a 3T fMRI

Published on: October 4, 2011

Haptic editing of MRI brain data.

Alexei Sourin¹, Shamima Yasmin

¹Nanyang Technological University, Singapore. assourin@ntu.edu.sg

Studies in Health Technology and Informatics

|February 24, 2012

Summary

This summary is machine-generated.

This study introduces a novel haptic friction-based method for identifying and correcting automated brain segmentation errors. This technique enhances precision in medical imaging and aids visually impaired users in navigating complex data surfaces.

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Area of Science:

Medical Imaging
Human-Computer Interaction
Neuroscience

Background:

Automated brain segmentation in MRI can produce errors.
Identifying these errors is crucial for accurate analysis.
Previous methods involved visual marking and interactive editing.

Purpose of the Study:

To introduce a new haptic friction-based approach for identifying and correcting segmentation errors.
To improve the accuracy and efficiency of medical data editing.
To explore applications for visually impaired users.

Main Methods:

A haptic proxy is moved along the reconstructed brain surface.
Varying friction is felt by the user to indicate discrepancies.
The surface is 'rubbed' to simulate polishing and identify errors.

Main Results:

The haptic friction method effectively highlights areas with segmentation errors.
The approach allows for intuitive error identification through touch.
Novice users can identify error-prone areas without extensive training.

Conclusions:

Haptic friction offers a promising new modality for medical data editing.
This method enhances the identification and correction of automated segmentation errors.
The technology has potential applications beyond medical imaging, including accessibility tools for the visually impaired.