Metadata-Version: 2.4
Name: qmri
Version: 0.1.0
Summary: Pure MRI signal models, fitting algorithms, and error propagation for quantitative MRI
Author-email: Gold Standard Phantoms <tom.hampshire@goldstandardphantoms.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/gold-standard-phantoms/qmri
Project-URL: Documentation, https://qmri.readthedocs.io
Project-URL: Repository, https://github.com/gold-standard-phantoms/qmri
Project-URL: Changelog, https://github.com/gold-standard-phantoms/qmri/blob/main/CHANGELOG.md
Keywords: mri,qmri,quantitative-mri,signal-processing,diffusion,relaxometry,perfusion,asl,t1-mapping,t2-mapping,adc
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.24
Requires-Dist: scipy>=1.10

# qmri

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

## Installation

```bash
pip install qmri
```

## Quick Start

```python
import numpy as np
from qmri.diffusion import adc

b_values = np.array([0, 500, 1000, 2000])
signal = np.array([1000, 606, 368, 135])
result = adc.fit(signal, b_values, method="iwlls")

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

## Documentation

See the full documentation at [qmri.readthedocs.io](https://qmri.readthedocs.io).

## License

MIT
