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Updated: Jul 13, 2025

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Published on: February 24, 2021

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks.

Jiahao Ren¹, Xiaocen Wang¹, Chang Liu¹

¹State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

This study introduces a 3D AI algorithm for precise brain ultrasound reconstruction, overcoming skull limitations. The Brain Imaging Full Convolution Network (BIFCN) offers a faster, safer alternative for brain imaging.

Keywords:

brain image reconstruction machine learning real-time imaging ultrasound

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Non-invasive Parenchymal, Vascular and Metabolic High-frequency Ultrasound and Photoacoustic Rat Deep Brain Imaging

Non-invasive Parenchymal, Vascular and Metabolic High-frequency Ultrasound and Photoacoustic Rat Deep Brain Imaging

Published on: March 2, 2015

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Ultrasound imaging offers advantages over MRI and CT but faces challenges in adult transcranial brain imaging due to skull acoustic impedance.
Conventional ultrasound struggles with skull penetration, limiting its application in brain imaging.

Purpose of the Study:

To develop and validate a 3D AI algorithm for precise transcranial brain ultrasound reconstruction.
To overcome the limitations of conventional ultrasound in adult brain imaging.

Main Methods:

A 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), was developed, integrating waveform modeling and deep learning.
The BIFCN network architecture includes an input layer, four convolution layers, and one pooling layer for training.
The algorithm was tested using simulation experiments and laboratory reconstructions with pure water as the initial model.

Main Results:

Simulation experiments showed a high Pearson correlation coefficient between reconstructed and true brain images.
Laboratory results demonstrated impressive 3D reconstruction accuracy, even without prior information.
The 3D BIFCN network trains in 8 hours and reconstructs 10 samples in 12.67 seconds, indicating high efficiency.

Conclusions:

The 3D BIFCN algorithm provides an accurate and efficient method for 3D brain imaging using ultrasound.
This AI-driven approach enables fast and precise brain tissue imaging, mapping wavefield data to 3D models.
The frequency shift phenomenon observed in blood may offer new quantitative insights for whole-brain blood imaging using BIFCN.