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Welcome to ConfUSIus

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

Pre-Alpha Status

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

  • User Guide

    Learn how to use ConfUSIus for fUSI data analysis.

  • API Reference

    Comprehensive reference of all modules, classes, and functions.

  • Community

    Join our weekly drop-in hours on Discord (Thursdays 3-4pm UTC).

Features

Design Philosophy

ConfUSIus is not designed as an out-of-the-box, end-to-end fUSI analysis pipeline. Because the fUSI field has not yet converged on standard processing workflows, ConfUSIus instead aims to provide the fundamental building blocks needed to implement any processing workflow described in the fUSI literature, or to design entirely new ones. Researchers can combine these blocks to build analysis pipelines suited to their experimental needs.

  • I/O Operations: Load and save fUSI data in various formats (AUTC, EchoFrame, Iconeus SCAN, NIfTI, Zarr, etc.)
  • 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 and reconstruct signals from regions of interest using spatial masks.
  • Signal Processing: Denoising, filtering, detrending, and confound regression for hemodynamic signals.
  • Visualization: Rich plotting utilities for fUSI data exploration.
  • Xarray Integration: Seamless integration with Xarray for labeled multi-dimensional arrays.

Installation

pip install confusius

Or with uv:

uv add confusius

See the Installation Guide for detailed instructions.

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()