add Conjugate Gradient with Momentum

examples/example_cg.py

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+"""Example of using the Conjugate Gradient method."""
+
+import numpy as np
+import mrinufft
+from brainweb_dl import get_mri
+from mrinufft.extras.gradient import cg
+from mrinufft.density import voronoi
+from matplotlib import pyplot as plt
+from scipy.datasets import face
+
+samples_loc = mrinufft.initialize_2D_radial(Nc=64, Ns=172)
+image = get_mri(sub_id=4)
+image = np.flipud(image[90])
+
+NufftOperator = mrinufft.get_operator("gpunufft")  # get the operator
+density = voronoi(samples_loc)  # get the density
+
+nufft = NufftOperator(
+    samples_loc, shape=image.shape, density=density, n_coils=1
+)  # create the NUFFT operator
+
+kspace_data = nufft.op(image)  # get the k-space data
+reconstructed_image = cg(nufft, kspace_data)  # reconstruct the image
+
+
+# Display the results
+plt.figure(figsize=(10, 5))
+plt.subplot(1, 2, 1)
+plt.title("Original Image")
+plt.imshow(abs(image), cmap="gray")
+
+plt.subplot(1, 2, 2)
+plt.title("Reconstructed Image")
+plt.imshow(abs(reconstructed_image), cmap="gray")
+plt.show()

src/mrinufft/extras/gradient.py

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+"""Conjugate gradient optimization algorithm for image reconstruction."""
+
+import numpy as np
+
+from mrinufft.operators.base import with_numpy
+
+
+@with_numpy
+def cg(operator, kspace_data, x_init=None, num_iter=10, tol=1e-4):
+    """
+    Perform conjugate gradient (CG) optimization for image reconstruction.
+
+    The image is updated using the gradient of a data consistency term,
+    and a velocity vector is used to accelerate convergence.
+
+    Parameters
+    ----------
+    kspace_data : numpy.ndarray
+              The k-space data to be used for image reconstruction.
+
+    x_init : numpy.ndarray, optional
+              An initial guess for the image. If None, an image of zeros with the same
+              shape as the expected output is used. Default is None.
+
+    num_iter : int, optional
+              The maximum number of iterations to perform. Default is 10.
+
+    tol : float, optional
+              The tolerance for convergence. If the norm of the gradient falls below
+              this value or the dot product between the image and k-space data is
+              non-positive, the iterations stop. Default is 1e-4.
+
+    Returns
+    -------
+    image : numpy.ndarray
+              The reconstructed image after the optimization process.
+    """
+    Lipschitz_cst = operator.get_lipschitz_cst()
+    image = (
+        np.zeros(operator.shape, dtype=type(kspace_data[0]))
+        if x_init is None
+        else x_init
+    )
+    velocity = np.zeros_like(image)
+
+    grad = operator.data_consistency(image, kspace_data)
+    velocity = tol * velocity + grad / Lipschitz_cst
+    image = image - velocity
+
+    for _ in range(num_iter):
+        grad_new = operator.data_consistency(image, kspace_data)
+        if np.linalg.norm(grad_new) <= tol:
+            break
+
+        beta = np.dot(grad_new.flatten(), grad_new.flatten()) / np.dot(
+            grad.flatten(), grad.flatten()
+        )
+        velocity = grad_new + beta * velocity
+
+        image = image - velocity / Lipschitz_cst
+
+    return image

tests/test_cg.py

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+"""Test for the cg function."""
+
+import numpy as np
+import pytest
+from pytest_cases import parametrize_with_cases, parametrize, fixture
+from mrinufft.extras.gradient import cg
+from mrinufft import get_operator
+from case_trajectories import CasesTrajectories
+
+from helpers import (
+    image_from_op,
+    param_array_interface,
+)
+from helpers import assert_almost_allclose
+
+
+@fixture(scope="module")
+@parametrize(
+    "backend",
+    [
+        "bart",
+        "finufft",
+        "cufinufft",
+        "gpunufft",
+        "sigpy",
+        "torchkbnufft-cpu",
+        "torchkbnufft-gpu",
+        "tensorflow",
+    ],
+)
+@parametrize_with_cases(
+    "kspace_locs, shape",
+    cases=[
+        CasesTrajectories.case_random2D,
+        CasesTrajectories.case_grid2D,
+        CasesTrajectories.case_grid3D,
+    ],
+)
+def operator(
+    request,
+    backend="pynfft",
+    kspace_locs=None,
+    shape=None,
+    n_coils=1,
+):
+    """Generate an operator."""
+    if backend in ["pynfft", "sigpy"] and kspace_locs.shape[-1] == 3:
+        pytest.skip("3D for slow cpu is not tested")
+    return get_operator(backend)(kspace_locs, shape, n_coils=n_coils, smaps=None)
+
+
+@fixture(scope="module")
+def image_data(operator):
+    """Generate a random image. Remains constant for the module."""
+    return image_from_op(operator)
+
+
+@param_array_interface
+def test_cg(operator, array_interface, image_data):
+    """Compare the interface to the raw NUDFT implementation."""
+    kspace_nufft = operator.op(image_data).squeeze()
+
+    image_cg = cg(operator, kspace_nufft)
+    kspace_cg = operator.op(image_cg).squeeze()
+
+    assert_almost_allclose(
+        kspace_cg,
+        kspace_nufft,
+        atol=2e-1,
+        rtol=1e-1,
+        mismatch=20,
+    )