Denoise
=======

denoise - Diffusion-Weighted Image Noise Reduction

Part of the micaflow processing pipeline for neuroimaging data.

This module denoises diffusion-weighted images (DWI) using the Patch2Self algorithm,
which leverages redundant information across diffusion gradients to remove noise 
without requiring additional reference scans. Patch2Self is a self-supervised learning 
approach that improves image quality and enhances subsequent diffusion analyses by 
removing random noise while preserving anatomical structures.

Features:
--------
- Self-supervised learning approach requiring no separate reference data
- Adapts to the unique noise characteristics of each dataset
- Preserves anatomical structure while effectively removing noise
- Compatible with standard diffusion MRI acquisition protocols
- Improves subsequent analyses such as fiber tracking and diffusion metrics

API Usage:
---------
micaflow denoise 
    --input <path/to/dwi.nii.gz>
    --bval <path/to/dwi.bval>
    --bvec <path/to/dwi.bvec>
    --output <path/to/denoised_dwi.nii.gz>

Python Usage:
-----------
>>> from micaflow.scripts.denoise import run_denoise
>>> run_denoise(
...     moving="raw_dwi.nii.gz",
...     moving_bval="dwi.bval", 
...     moving_bvec="dwi.bvec",
...     output="denoised_dwi.nii.gz"
... )

Command Line Usage
-----------------

.. code-block:: bash

    micaflow denoise [options]

Source Code
-----------

View the source code: `GitHub Repository <https://github.com/MICA-LAB/micaflow/blob/main/micaflow/scripts/denoise.py>`_

Description
-----------

This script denoises diffusion-weighted images (DWI) using the Patch2Self 
    algorithm, which leverages redundant information across diffusion gradients
    to remove noise without requiring additional reference scans.

Full Help
---------

.. code-block:: text

    
        ╔════════════════════════════════════════════════════════════════╗
        ║                     DWI IMAGE DENOISING                        ║
        ╚════════════════════════════════════════════════════════════════╝
        
        This script denoises diffusion-weighted images (DWI) using the Patch2Self 
        algorithm, which leverages redundant information across diffusion gradients
        to remove noise without requiring additional reference scans.
        
        ────────────────────────── USAGE ──────────────────────────
          micaflow denoise [options]
        
        ─────────────────── REQUIRED ARGUMENTS ───────────────────
          --input     : Path to the input DWI image (.nii.gz)
          --bval      : Path to the b-values file (.bval)
          --bvec      : Path to the b-vectors file (.bvec)
          --output    : Output path for the denoised image (.nii.gz)
        
        ─────────────────── EXAMPLE USAGE ───────────────────
          micaflow denoise \
            --input raw_dwi.nii.gz \
            --bval dwi.bval \
            --bvec dwi.bvec \
            --output denoised_dwi.nii.gz
        
        ────────────────────────── NOTES ─────────────────────────
        - Patch2Self is a self-supervised learning method for denoising
        - Processing preserves anatomical structure while removing noise
        - The implementation uses OLS regression with b0 threshold of 50 s/mm²
        - B0 volumes are not denoised separately in this implementation