# GaussianMaskGenerator

### *class* deepinv.physics.generator.GaussianMaskGenerator(img_size, acceleration=4, center_fraction=None, rng=None, device='cpu', \*args, \*\*kwargs)

Bases: [`RandomMaskGenerator`](https://deepinv.org/api/stubs/deepinv.physics.generator.RandomMaskGenerator.html.md#deepinv.physics.generator.RandomMaskGenerator)

Generator for MRI Cartesian acceleration masks using Gaussian undersampling.

Generate a mask of vertical lines for MRI acceleration with fixed sampling in low frequencies (center of k-space) and Gaussian undersampling in the high frequencies.

The high frequencies are selected according to a tail-adjusted Gaussian pdf. This ensures that the expected number of rows selected is equal to (N / acceleration).

Supports k-t sampling, where the Gaussian mask varies randomly across time.

The mask is repeated across channels and randomly varies across batch dimension.

Algorithm taken from Schlemper *et al.*<sup>[1](#footcite-schlemper2017deep)</sup>.

For parameter descriptions see [`deepinv.physics.generator.mri.BaseMaskGenerator`](https://deepinv.org/api/stubs/deepinv.physics.generator.BaseMaskGenerator.html.md#deepinv.physics.generator.BaseMaskGenerator)

<hr />

* **Examples:**
  Gaussian random k-t mask generator for a 8x64x64 video:
  ```pycon
  >>> from deepinv.physics.generator import GaussianMaskGenerator
  >>> generator = GaussianMaskGenerator((2, 8, 64, 64), acceleration=8, center_fraction=0.04) # C, T, H, W
  >>> params = generator.step(batch_size=1)
  >>> mask = params["mask"]
  >>> mask.shape
  torch.Size([1, 2, 8, 64, 64])
  ```

<hr />

* **References:**

* <a id='footcite-schlemper2017deep'>**[1]**</a> Jo Schlemper, Jose Caballero, Joseph V Hajnal, Anthony N Price, and Daniel Rueckert. A deep cascade of convolutional neural networks for dynamic mr image reconstruction. *IEEE transactions on Medical Imaging*, 37(2):491–503, 2017.

#### get_pdf(W)

Create one-dimensional Gaussian probability density function across columns, ignoring any fixed sampling columns.

Add uniform distribution so that the probability of sampling high-frequency lines is non-zero.

* **Parameters:**
  **W** ([*int*](https://docs.python.org/3.12/library/functions.html#int)) – total number of columns (width of sampling mask)
* **Return torch.Tensor:**
  unnormalized 1D vector representing pdf evaluated across mask columns.
* **Return type:**
  [*Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)

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## Examples using `GaussianMaskGenerator`:

<div class="sphx-glr-thumbnails">
<!-- thumbnail-parent-div-open --><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform inference on and fine-tune the Reconstruct Anything Model (RAM) foundation model terris2025reconstruct to solve inverse problems.">![](auto_examples/models/images/thumb/sphx_glr_demo_foundation_model_thumb.png)

[Inference and fine-tune a foundation model](https://deepinv.org/auto_examples/models/demo_foundation_model.html.md)

  <div class="sphx-glr-thumbnail-title">Inference and fine-tune a foundation model</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example presents the various datasets, forward physics and models available in DeepInverse for Magnetic Resonance Imaging (MRI) problems:">![](auto_examples/physics/images/thumb/sphx_glr_demo_mri_tour_thumb.png)

[Tour of MRI functionality in DeepInverse](https://deepinv.org/auto_examples/physics/demo_mri_tour.html.md)

  <div class="sphx-glr-thumbnail-title">Tour of MRI functionality in DeepInverse</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example provides a tour of some of the forward operators implemented in DeepInverse. We restrict ourselves to operators where the signal is a 2D image. The full list of operators can be found in here.">![](auto_examples/physics/images/thumb/sphx_glr_demo_physics_tour_thumb.png)

[Tour of forward sensing operators](https://deepinv.org/auto_examples/physics/demo_physics_tour.html.md)

  <div class="sphx-glr-thumbnail-title">Tour of forward sensing operators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We demonstrate the self-supervised Artifact2Artifact loss for solving an undersampled sequential MRI reconstruction problem without ground truth.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_artifact2artifact_thumb.png)

[Self-supervised MRI reconstruction with Artifact2Artifact](https://deepinv.org/auto_examples/self-supervised-learning/demo_artifact2artifact.html.md)

  <div class="sphx-glr-thumbnail-title">Self-supervised MRI reconstruction with Artifact2Artifact</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We demonstrate scan-specific self-supervised learning, that is, learning to reconstruct MRI scans from a single accelerated sample without ground truth.">![](auto_examples/self-supervised-learning/images/thumb/sphx_glr_demo_scan_specific_thumb.png)

[Scan-specific zero-shot SSDU for MRI](https://deepinv.org/auto_examples/self-supervised-learning/demo_scan_specific.html.md)

  <div class="sphx-glr-thumbnail-title">Scan-specific zero-shot SSDU for MRI</div>
</div>
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