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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...

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Related Experiment Video

Updated: May 28, 2025

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Improved image reconstruction from brain activity through automatic image captioning.

Fatemeh Kalantari¹, Karim Faez², Hamidreza Amindavar¹

¹Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.

Scientific Reports

|February 10, 2025

Summary

This summary is machine-generated.

This study introduces a novel method for reconstructing images from brain signals by combining visual and semantic information. The approach significantly enhances image reconstruction quality and accuracy compared to previous methods.

Keywords:

Bootstrapping language-image pre-training Brain human activity Latent diffusion model Semantic image reconstruction Visual and semantic decoding Visual cortex

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Neuroscience
Computer Vision
Machine Learning

Background:

Image reconstruction from functional magnetic resonance imaging (fMRI) brain signals has advanced, but accuracy and quality remain challenges.
Previous methods often struggle with insufficient accuracy when decoding only visual information.
Integrating semantic information is recommended but faces significant difficulties.

Purpose of the Study:

To develop an improved image reconstruction method by combining complex semantic details with visual details.
To enhance the accuracy and quality of images reconstructed from brain activity.
To address the limitations of existing fMRI-based image reconstruction techniques.

Main Methods:

A two-module approach: visual reconstruction (using deep generator network and VGG19) and semantic reconstruction (using BLIP and LDM models).
Visual reconstruction decodes visual information from brain data and optimizes image generation.
Semantic reconstruction uses image captions (via BLIP) and brain data to decode semantic features, which then condition LDM for enhanced reconstruction.

Main Results:

The proposed method significantly improves reconstruction quality, outperforming Shen et al.'s method quantitatively and qualitatively.
Achieved high accuracy scores: 0.812 (Inception), 0.815 (CLIP) for semantic content, and 0.328 (SSIM) for low-level metrics.
Demonstrated success in reconstructing artificial shapes and imagined images with notable CLIP and SSIM scores.

Conclusions:

Combining semantic and visual information is crucial for high-fidelity image reconstruction from brain signals.
The proposed dual-module approach effectively decodes and integrates both visual and semantic brain data.
This methodology represents a significant step forward in brain-computer interfaces and neuroimaging applications.