Cloudmr-tools

Cloudmr-tools provides tools for advanced multi-coil reconstruction methods for Magnetic Resonance Imaging (MRI). Designed for researchers and developers in the field of MRI, this package supports streamlined implementation of reconstruction techniques like RSS, SENSE, and G-Factor calculation.

Quickstart

```python from cmtools.cm2D import cm2DReconB1,cm2DReconRSS,cm2DReconSENSE,cm2DGFactorSENSE

S= your multi coil K-Space 2D signal

N=your multi coil K-Space 2D noise or Noise covariance

L0=cm2DReconRSS() L0.setSignalKSpace(S) L0.setNoiseKSpace(N) plt.figure() plt.imshow(np.abs(L0.getOutput())) plt.colorbar() plt.title('RSS Reconstruction')

```

Installation

```

create an environment

python3 -m venv CMT source CMT/bin/activate pip install git+https://github.com/cloudmrhub/cloudmr-tools.git ```

Live Example

https://colab.research.google.com/drive/1WIEwRrNy9rpo2XzVdwRHRtH4ImPaan?usp=sharing

Cite Us

If you use Cloudmr-tools in your research, please cite:

Montin E, Lattanzi R. Seeking a Widely Adoptable Practical Standard to Estimate Signal-to-Noise Ratio in Magnetic Resonance Imaging for Multiple-Coil Reconstructions. J Magn Reson Imaging. 2021 Dec;54(6):1952-1964. doi: 10.1002/jmri.27816. Epub 2021 Jul 4. PMID: 34219312; PMCID: PMC8633048.

Versioning

The Cloudmr-tools package has two versions:

V1 (Deprecated)

• Name: cloudmrhub
• Status: Deprecated, but still functional for backward compatibility. (v1 branch)
• Details: This version is no longer actively maintained and will not receive updates or bug fixes.

Version 2 (Current)

• Name: cloudmr-tools
• Status: Actively maintained (main branch).
• Details: This is the recommended version for new projects. It includes updated functionality and better support for advanced features.

Key Differences

| Feature | Version 1 (cloudmrhub) | Version 2 (cloudmr-tools) | |-------------------------|------------------------------|-----------------------------| | Maintenance | Deprecated | Actively maintained | | Compatibility | Legacy projects | New and legacy projects |

| Features | Limited | Updated and expanded |

Migration

If you're currently using Version 1 of the library, consider migrating to Version 2 to take advantage of the latest features and updates.

If you need to continue using the Version 1 code, simply change the import path from cloudmrhub to cmtools. For example:

Original (Version 1): python import numpy as np import cloudmrhub.cm2D as cm2D Modified version (Version 2) python import numpy as np import cmtools.cm2D as cm2D

Explanation of the Code and Main Functions

Below is a high-level summary of the repository’s structure and functionality:

1. cmtools/cm.py

   • Utilities for MRI data processing, including coil-sensitivity maps, GRAPPA recon, noise pre-whitening, and simpler SENSE-based reconstructions.
     
   • Provides various classes for 2D/3D image data (e.g., i2d, k2d), helper functions (e.g., getGRAPPAKspace, prewhiteningSignal), and logging/export support.

2. cmtools/espirit.py

   • Implements ESPIRiT to generate coil-sensitivity maps using multi-channel k-space data.
     
   • Core functions like espirit(...) and espirit_proj(...) let you compute coil maps and project coil images onto the ESPIRiT operator space.

3. cmtools/version.py

   • Simple script for printing package versions of dependencies.

4. cmtools/cfl.py

   • Helper functions readcfl and writecfl to read/write BART .cfl/.hdr files.

5. cmtools/cmaws.py

   • Handles AWS S3 interactions: uploading/downloading of data, retrieving files, and credential management.
     
   • Includes the cmrOutput class, which simplifies exporting and zipping results for local storage or S3 uploads.

6. tests.py and tests2.py

   • Example scripts demonstrating how to run recon steps (using GRAPPA, SENSE, or custom coil-sensitivity methods).
     
   • Show how to integrate with cmtools pipelines for quick testing and validation.

7. pyproject.toml

   • Project metadata (e.g., name, version, build dependencies) and configuration for build tools.

Refer to individual script docstrings or the code itself for more information on each function’s parameters and usage.

Contributors

Dr. Eros Montin, PhD\ \ \

Prof. Riccardo Lattanzi, PhD\ \ \