TomographyModel

The TomographyModel provides the basic interface for all specific geometries for tomographic projection and reconstruction.

Constructor

class mbirjax.TomographyModel(sinogram_shape, **kwargs)

Bases: object

Represents a general model for tomographic reconstruction using MBIRJAX. This class encapsulates the parameters and methods for the forward and back projection processes required in tomographic imaging.

Note that this class is a template for specific subclasses. TomographyModel by itself does not implement projectors or recon. Use self.print_params() to print the parameters of the model after initialization.

Parameters:

• sinogram_shape (tuple) – The shape of the sinogram array expected (num_views, num_det_rows, num_det_channels).

• **kwargs (dict) – Arbitrary keyword arguments for setting model parameters dynamically.

Sets up the reconstruction size and parameters.

Recon and Projection

TomographyModel.recon(sinogram, weights=1.0, num_iterations=13, init_recon=None)

Perform MBIR reconstruction using the Multi-Granular Vector Coordinate Descent algorithm. This function takes care of generating its own partitions and partition sequence.

Parameters:

• sinogram (jax array) – 3D sinogram data with shape (num_views, num_det_rows, num_det_channels).

• weights (scalar or jax array) – scalar or 3D positive weights with same shape as error_sinogram.

• num_iterations (int) – number of iterations of the VCD algorithm to perform.

• init_recon (jax array) – optional reconstruction to be used for initialization.

Returns:

[recon, fm_rmse] – reconstruction and array of loss for each iteration.

TomographyModel.forward_project(recon)

Perform a full forward projection at all voxels in the field-of-view.

Parameters:

recon (jnp array) – The 3D reconstruction array.

Returns:

jnp array – The resulting 3D sinogram after projection.

TomographyModel.back_project(sinogram)

Perform a full back projection at all voxels in the field-of-view.

Parameters:

sinogram (jnp array) – 3D jax array containing sinogram.

Returns:

jnp array – The resulting 3D sinogram after projection.

Parameter Handling

TomographyModel.set_params(no_warning=False, no_compile=False, **kwargs)

Updates parameters using keyword arguments. After setting parameters, it checks if key geometry-related parameters have changed and, if so, recompiles the projectors.

Parameters:

• no_warning (bool, optional, default=False) – This is used internally to allow for some initial parameter setting.

• no_compile (bool, optional, default=False) – Prevent (re)compiling the projectors. Used for initialization.

• **kwargs – Arbitrary keyword arguments where keys are parameter names and values are the new parameter values.

Raises:

NameError – If any key provided in kwargs is not a recognized parameter.

TomographyModel.get_params(parameter_names)

Get the values of the listed parameter names. Raises an exception if a parameter name is not defined in parameters.

Parameters:

parameter_names – String or list of strings

Returns:

Single value or list of values

Data Generation

static TomographyModel.gen_weights(sinogram, weight_type)

Compute the weights used in MBIR reconstruction.

Parameters:

• sinogram (jax array) – 3D jax array containing sinogram with shape (num_views, num_det_rows, num_det_channels).

• weight_type (string) – Type of noise model used for data - weight_type = ‘unweighted’ => return numpy.ones(sinogram.shape). - weight_type = ‘transmission’ => return numpy.exp(-sinogram). - weight_type = ‘transmission_root’ => return numpy.exp(-sinogram/2). - weight_type = ‘emission’ => return 1/(numpy.absolute(sinogram) + 0.1).

Returns:

(jax array) – Weights used in mbircone reconstruction, with the same array shape as sinogram.

Raises:

Exception – Raised if weight_type is not one of the above options.

TomographyModel.gen_3d_sl_phantom()

Generates a 3D Shepp-Logan phantom.

Returns:

ndarray – A 3D numpy array of shape specified by TomographyModel class parameters.