ConfUSIus

Warning

ConfUSIus is currently in pre-alpha and under active development. The API is subject to change, and features may be incomplete or unstable.

ConfUSIus is a Python package for handling, visualization, preprocessing, and statistical analysis of functional ultrasound imaging (fUSI) data.

Features

• I/O Operations: Load and save fUSI data in various formats (AUTC, EchoFrame, NIfTI, Zarr)
• Beamformed IQ Processing: Process raw beamformed IQ signals into power Doppler, velocity, and other derived metrics
• Quality Control: Compute quality metrics (DVARS, tSNR, CV) to assess data quality
• Registration: Motion correction and spatial alignment tools
• Signal Extraction: Extract signals from regions of interest using spatial masks
• Signal Processing: Denoising, filtering, detrending, and confound regression
• Visualization: Rich plotting utilities for fUSI data exploration
• Xarray Integration: Seamless integration with Xarray for labeled multi-dimensional arrays

Installation

1. Setup a virtual environment

We recommend that you install ConfUSIus in a virtual environment to avoid dependency conflicts with other Python packages. Using uv, you may create a new project folder with a virtual environment as follows:

uv init new_project

If you already have a project folder, you may create a virtual environment as follows:

uv venv

2. Install ConfUSIus

ConfUSIus is available on PyPI. Install it using:

uv add confusius

Or with pip:

pip install confusius

To install the latest development version from GitHub:

uv add git+https://github.com/sdiebolt/confusius.git

3. Check installation

Check that ConfUSIus is correctly installed by opening a Python interpreter and importing the package:

import confusius

If no error is raised, you have installed ConfUSIus correctly.

Quick Start

import confusius as cf

# Load fUSI data
data = cf.io.load_nifti("path/to/data.nii.gz")

# Perform motion correction
corrected_data = data.fusi.register.volumewise()

# Visualize with Napari
corrected_data.fusi.plot()

See the documentation for more detailed usage examples and tutorials.

Citing ConfUSIus

If you use ConfUSIus in your research, please cite it using the following reference:

Le Meur-Diebolt, S. (2026). ConfUSIus (v0.0.1-a7). Zenodo. https://doi.org/10.5281/zenodo.18611124

Or in BibTeX format:

@software{confusius,
  author    = {Le Meur-Diebolt, Samuel},
  title     = {ConfUSIus},
  year      = {2026},
  publisher = {Zenodo},
  version   = {v0.0.1-a7},
  doi       = {10.5281/zenodo.18611124},
  url       = {https://doi.org/10.5281/zenodo.18611124}
}