ConfUSIus 
ConfUSIus is a Python package and napari plugin 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, Iconeus, NIfTI, Zarr), with automatic fUSI-BIDS sidecars for NIfTI.
- 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.
- Brain Atlas Integration: Map fUSI data to standard brain atlases for region-based analysis.
- 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.
- Napari Plugin: Interactive data loading, live signals inspection, and quality control directly in the napari viewer—no scripting required.
- 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/confusius-tools/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.load("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., & Cybis Pereira, F. (2026). ConfUSIus (v0.7.0). Zenodo. https://doi.org/10.5281/zenodo.18611124
Or in BibTeX format:
@software{confusius,
author = {Le Meur-Diebolt, Samuel and Cybis Pereira, Felipe},
title = {ConfUSIus},
year = {2026},
publisher = {Zenodo},
version = {v0.7.0},
doi = {10.5281/zenodo.18611124},
url = {https://doi.org/10.5281/zenodo.18611124}
}
1 maintainer
Metadata
1 maintainer
Release files for confusius 0.7.0
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| confusius-0.7.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / confusius-0.7.0.tar.gz
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