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https://github.com/imyizhang/mridatapy
A lightweight toolbox for downloading and processing mridata from mridata.org
https://github.com/imyizhang/mridatapy
mri mri-reconstruction mridata
Last synced: about 2 months ago
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A lightweight toolbox for downloading and processing mridata from mridata.org
- Host: GitHub
- URL: https://github.com/imyizhang/mridatapy
- Owner: imyizhang
- License: mit
- Created: 2021-03-15T22:48:40.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-23T18:39:00.000Z (almost 4 years ago)
- Last Synced: 2024-10-31T09:09:25.984Z (about 2 months ago)
- Topics: mri, mri-reconstruction, mridata
- Language: Jupyter Notebook
- Homepage: https://pypi.org/project/mridatapy/
- Size: 317 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# mridataPy
mridataPy is
* a lightweight toolbox for downloading and processing mridata from mridata.org
mridataPy supports to
* download dataset from either [mridata.org](http://mridata.org/) or [old.mridata.org](http://old.mridata.org/)
* load mridata to NumPy arrays, which can be stored to .npy files
* generate sampling masks that can densely sample the center region in k-space while subsample the outer region based on acceleration factor
* provide the ground truth reconstructed by applying RSS coil-combination to fullysampled data
* evaluate reconstructions with MSE, NMSE, PSNR, SSIM metrics
## Quickstart
Download one case of [Stanford Fullysampled 3D FSE Knees](http://mridata.org/list?project=Stanford%20Fullysampled%203D%20FSE%20Knees) (totally, 20 cases) from [mridata.org](http://mridata.org/):
```python
import mridatapy
mridata = mridatapy.data.MRIData()
mridata.download(num=1)
```
### Dependencies and Installation
#### Package Dependencies
`pip` will handle all package dependencies.
#### Install mridataPy
```bash
$ pip install mridatapy
```
## Documentation
### Module `data`
#### `MRIData`
```python
class mridatapy.data.MRIData(data_type=None, path=None)
```
* `urls`
**Attribute**: Whole lists of download URLs corresponding to mridata of the given data type.
* `filenames`
**Attribute**: Whole lists of download filenames corresponding to mridata of the given data type.
* `type`
**Attribute**: Data type of mridata.
* `dir`
**Attribute**: Directory to the folder "mridata/" as the default path for mridata.
* `download(num=None)`
**Instance method**: Downloads mridata of the given data type.
* `to_np(num=None, stack=None)`
**Instance method**: Loads mridata to complex-valued k-space NumPy arrays. If not exist, download first.
* `to_npy(path=None, num=None, stack=None)`
**Instance method**: Converts mridata to .npy files. If not exist, download first.
* `get(data_type)`
**Static method**: Gets whole lists of download URLs and filenames corresponding to mridata of the given data type to be downloaded.
* `fetch(url, filename, path)`
**Static method**: Fetches mridata given the specific pair of download URL and filename.
* `ismrmrd_to_np(file, filter=None, prewhiten=None, first_slice=None)`
**Static method**: Loads .h5 ISMRMRD file to complex-valued k-space NumPy array.
* `ismrmrd_to_npy(file, path=None, filter=None, prewhiten=None, first_slice=None)`
**Static method**: Converts .h5 ISMRMRD file to .npy file.
* `cfl_to_np(file)`
**Static method**: Loads .cfl file to complex-valued k-space NumPy array.
* `cfl_to_npy(file, path=None)`
**Static method**: Converts .cfl file to .npy file.
* `unzip(file, path=None, remove=None)`
**Static method**: Unzips .zip file.
* `load_npy(file)`
**Static method**: Loads .npy file.
#### `RandomLine`
```python
class mridatapy.data.RandomLine(acceleration_factor, center_fraction)
```
Generates a sampling mask of the given shape that can densely sample the center region in k-space while subsample the outer region based on acceleration factor. The mask randomly selects a subset of columns from input k-space data.
* `__call__(shape, dtype=numpy.complex64, max_attempts=30, tolerance=0.1, seed=None)`
Magic method enables instances to behave like functions.
#### `EquispacedLine`
```python
class mridatapy.data.EquispacedLine(acceleration_factor, center_fraction)
```
Generates a sampling mask of the given shape that can densely sample the center region in k-space while subsample the outer region based on acceleration factor. The mask selects a roughly equispaced subset of columns from input k-space data.
* `__call__(shape, dtype=numpy.complex64, max_attempts=30, tolerance=0.1, seed=None)`
Magic method enables instances to behave like functions.
#### `PoissonDisk`
```python
class mridatapy.data.PoissonDisk(acceleration_factor, center_fraction)
```
Generates a sampling mask of the given shape that can densely sample the center region in k-space while subsample the outer region based on acceleration factor. The mask selects a subset of points from input k-space data, characterized by the Poisson disk sampling pattern.
* `__call__(shape, dtype=numpy.complex64, max_attempts=30, tolerance=0.1, seed=None)`
Magic method enables instances to behave like functions.
### Module `utils`
#### `fft_centered`
```python
function mridatapy.utils.fft_centered(input, shape=None, dim=None, norm=None)
```
Computes the centered N dimensional discrete Fourier transform (FFT) of input.
#### `ifft_centered`
```python
function mridatapy.utils.ifft_centered(input, shape=None, dim=None, norm=None)
```
Computes the centered N dimensional inverse discrete Fourier transform (IFFT) of input.
#### `root_sum_squares`
```python
function mridatapy.utils.root_sum_squares(input, dim, complex=False)
```
Computes the Root Sum of Squares (RSS) of input along the a given dimension (coil dimension).
### Module `metrics`
#### `mean_squared_error`
```python
function mridatapy.metrics.mean_squared_error(gt, pred)
```
Computes the Mean Squared Error (MSE) between two images.
#### `normalized_mse`
```python
function mridatapy.metrics.normalized_mse(gt, pred)
```
Computes the Normalized Mean Squared Error (NMSE) between two images.
#### `peak_signal_noise_ratio`
```python
function mridatapy.metrics.peak_signal_noise_ratio(gt, pred, data_range=None)
```
Computes the Peak Signal to Noise Ratio (PSNR) between two images.
#### `structural_similarity`
```python
function mridatapy.metrics.structural_similarity(gt, pred, data_range=None)
```
Computes the Structural Similarity Index (SSIM) between two images.
## Related Projects
* [mridata.org](http://mridata.org/)
* [mridata-python](https://github.com/mikgroup/mridata-python) ([pypi.org/project/mridata/](https://pypi.org/project/mridata/))
* [SigPy](https://github.com/mikgroup/sigpy)