| --- |
| license: bsd-3-clause |
| library_name: scikit-learn |
| pipeline_tag: tabular-regression |
| tags: |
| - MRI |
| - medical-imaging |
| - ZAP |
| - random-forest |
| - regression |
| --- |
| |
| # ZAP Random Forest Regression Models |
|
|
| Trained random forest regression models for accelerated liver Z-spectrum analysis proton (ZAP) parameter estimation. |
|
|
| ## Model configurations |
|
|
| Two acquisition configurations are provided: |
|
|
| - **12 frequency offsets:** greater acquisition acceleration |
| - **20 frequency offsets:** improved quantitative agreement |
|
|
| ## Model outputs |
|
|
| Each configuration estimates the following voxel-wise parameters: |
|
|
| $$ |
| F_f,\qquad T_{2,f}^{\mathrm{ex}},\qquad T_{2,r}^{\mathrm{ex}}. |
| $$ |
| |
| Here: |
| |
| - \\(F_f\\) is the relatively free fraction. |
| - \\(T_{2,f}^{\mathrm{ex}}\\) is the apparent transverse exchange time of the relatively free component. |
| - \\(T_{2,r}^{\mathrm{ex}}\\) is the apparent transverse exchange time of the restricted component. |
|
|
| The restricted fraction is obtained as |
|
|
| $$ |
| F_r = 1-F_f. |
| $$ |
|
|
| ## Model files |
|
|
| The release archive, `liver_zap_rfr_models_v1.zip`, contains: |
|
|
| - trained scikit-learn random forest regression models |
| - selected frequency offsets for both acquisition configurations |
| - model manifest |
| - software-environment information |
| - release notes |
| - BSD 3-Clause license |
| - SHA-256 integrity checksums |
|
|
| ## Source code |
|
|
| The associated source code is available at [github.com/vmalis/ZAP-RFR](https://github.com/vmalis/ZAP-RFR). |
|
|
| ## Intended use |
|
|
| These models are intended for research evaluation of accelerated liver ZAP imaging. They are not medical devices and are not intended for diagnosis or treatment decisions. |
|
|
| Application to different scanners, acquisition protocols, tissues, or populations requires independent validation. |
|
|
| ## Security |
|
|
| The models use Python `joblib` serialization and therefore have Python pickle semantics. Load model files only from trusted sources and verify their SHA-256 checksums before use. |
