Update README.md

README.md

@@ -1,3 +1,59 @@
----
-license: mit
----
+---
+library_name: sklearn
+tags:
+- medical-imaging
+- mri
+- diffusion-mri
+- ivim
+- dki
+- physics
+license: mit
+language:
+- en
+---
+
+# dMRI-IVIM-ML-Toolkit: Fast Diffusion MRI Analysis
+
+This is the official model repository for the paper:
+**"Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis"** (Journal of Medical Physics, 2024).
+
+## Model Description
+
+These are pre-trained Machine Learning models (Random Forest, Extra Trees) designed to estimate **IVIM** (Intravoxel Incoherent Motion) and **DKI** (Diffusion Kurtosis Imaging) parameters from diffusion-weighted MRI signals.
+
+- **Input:** Normalized MRI signal attenuation curve (multi-b-value).
+- **Output:** Diffusion ($D$), Pseudo-diffusion ($D^*$), Perfusion fraction ($f$), and Kurtosis ($K$).
+- **Speedup:** ~230x faster than standard non-linear least squares fitting.
+
+## How to Use
+
+You can load these models using `joblib` and `scikit-learn` in Python.
+
+```python
+import joblib
+import numpy as np
+
+# Load the pre-trained model
+model = joblib.load("ivim_dki_extratrees.joblib")
+
+# Example signal (normalized)
+# Shape: (n_samples, n_b_values)
+signal = np.array([[1.0, 0.8, 0.5, ...]]) 
+
+# Predict parameters [D, f, D*, K]
+params = model.predict(signal)
+
+```
+
+## Citation 
+```
+@article{PrietoGonzalez2024,
+  title={Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis},
+  author={Prieto-González, Leonar Steven and Agulles-Pedrós, Luis},
+  journal={Journal of Medical Physics},
+  volume={49},
+  issue={2},
+  pages={189--202},
+  year={2024}
+}
+```