Multi-Modal Networks for Real-Time Monitoring of Intracranial Acoustic Field During tFUS Therapy

Overview

This repository contains the implementation of Multi-modal networks for real-time monitoring of intracranial acoustic field during transcranial focused ultrasound therapy.

Features

• Convolutional neural network (CNN) based Autoencoder and U-Net
• Swin Transformer based U-Net
• Python codes for training, evaluation, loading dataset
• Pre-trained model weights

Note: Dataset is not provided due to privacy concerns.

Requirements

• torch
• tqdm
• timm
• einops
• matplotlib
• numpy
• h5py
• natsort

Install all prerequisites with pip install -r requirements.txt

Installation

Clone this repository: git clone https://github.com/Minjee-Seo/tFUS-Multimodal.git

Pre-trained model weights

You can get following model weights from here:

• CT-trained Autoencoder (AECT.pth)
• MR-trained Autoencoder (AEMR.pth)
• CT-trained CNN U-Net (UNetCT.pth)
• MR-trained CNN U-Net (UNetMR.pth)
• CT-trained Swin U-Net (SwinCT.pth)
• MR-trained Swin U-Net (SwinMR.pth)
• Transfer learning CNN U-Net (UNetCTpMR.pth)

Usage

1. Edit dataset.py to load your data.
2. Run python train.py --your_project_name for training.
3. Run python test.py --your_project_name for evaluation.

Contact

For any queries, please reach out to Minjee Seo.