qmri¶

Pure MRI signal models, fitting algorithms, and error propagation for quantitative MRI.

qmri provides mathematical models for quantitative MRI (qMRI) analysis. It is designed to be:

Pure — Core package depends only on NumPy and SciPy
Simple — Clean function-based API, no complex abstractions
Typed — Full type annotations for IDE support
Tested — Comprehensive test coverage

Installation¶

# Core package (numpy + scipy only)
pip install qmri

# With file I/O support (adds nibabel)
pip install qmri-io

# With end-to-end processing pipelines
pip install qmri-pipelines

# With CLI (adds click, rich)
pip install qmri-cli

# With visualisation (adds matplotlib)
pip install qmri-viz

# With Digital Reference Objects for validation
pip install qmri-dro

Quick Example¶

import numpy as np
from qmri.diffusion import adc

# DWI signal data
b_values = np.array([0, 500, 1000, 2000])
signal = np.array([1000, 606, 368, 135])

# Fit ADC using iterative weighted least squares
result = adc.fit(signal, b_values, method="iwlls")

print(f"ADC: {result.adc:.2e} mm²/s")
print(f"S₀: {result.s0:.0f}")
print(f"R²: {result.r_squared:.4f}")

Packages¶

Package	Description	Dependencies
`qmri`	Core signal models and fitting	numpy, scipy
`qmri-io`	NIFTI/DICOM/BIDS I/O	+ nibabel
`qmri-pipelines`	End-to-end file-in / file-out workflows	qmri, qmri-io
`qmri-cli`	Command-line interface	+ click, rich
`qmri-viz`	Visualisation utilities	+ matplotlib
`qmri-dro`	Digital Reference Objects for validation	qmri

Modules¶

qmri.diffusion — ADC fitting, DWI signal generation, b-value calibration
qmri.relaxometry — T1 and T2 mapping
qmri.perfusion — ASL quantification, General Kinetic Model
qmri.thermometry — MR thermometry models
qmri.fitting — Least squares algorithms, bootstrap
qmri.errors — R², RMSE, uncertainty propagation
qmri.constants — Physical constants
qmri.dro — Digital Reference Objects for validation and testing

License¶

MIT License — see LICENSE for details.