Add support for 3D tomographic projection with astra

.github/workflows/test_examples.yml

@@ -69,4 +69,4 @@ jobs:
       # Run example test
       - name: Run example test
         run: |
-          ${GITHUB_WORKSPACE}/examples/scriptcheck.sh -e -d -t
+          ${GITHUB_WORKSPACE}/examples/scriptcheck.sh -e -d -t -g

CHANGES.rst

@@ -6,21 +6,22 @@ SCICO Release Notes
 Version 0.0.4   (unreleased)
 ----------------------------
 
-• New `Function` class for representing array-to-array mappings with more than
+• Add new `Function` class for representing array-to-array mappings with more than
   one input.
-• New methods and a function for computing Jacobian-vector products for `Operator`
+• Add new methods and a function for computing Jacobian-vector products for `Operator`
   objects.
-• New proximal ADMM solvers.
-• New ADMM subproblem solvers for problems involving a sum-of-convolutions
+• Add new proximal ADMM solvers.
+• Add new ADMM subproblem solvers for problems involving a sum-of-convolutions
   operator.
 • Extend support for other ML models including UNet, ODP and MoDL.
 • Add functionality for training Flax-based ML models and for data generation.
 • Enable diagnostics for ML training loops.
 • Change required packages and version numbers, including more recent version
   for `flax`.
-• New methods and a function for computing Jacobian-vector products for
+• Add new methods and a function for computing Jacobian-vector products for
   `Operator` objects.
 • Drop support for Python 3.7.
+• Add support for 3D tomographic projection with the ASTRA Toolbox.
 
 
 

docs/source/examples.rst

@@ -25,6 +25,7 @@ Computed Tomography
    examples/ct_abel_tv_admm
    examples/ct_abel_tv_admm_tune
    examples/ct_astra_noreg_pcg
+   examples/ct_astra_3d_tv_admm
    examples/ct_astra_tv_admm
    examples/ct_astra_weighted_tv_admm
    examples/ct_svmbir_tv_multi
@@ -126,6 +127,7 @@ Total Variation
    examples/ct_abel_tv_admm
    examples/ct_abel_tv_admm_tune
    examples/ct_astra_tv_admm
+   examples/ct_astra_3d_tv_admm
    examples/ct_astra_weighted_tv_admm
    examples/ct_svmbir_tv_multi
    examples/deconv_circ_tv_admm
@@ -193,6 +195,7 @@ ADMM
    examples/ct_abel_tv_admm
    examples/ct_abel_tv_admm_tune
    examples/ct_astra_tv_admm
+   examples/ct_astra_3d_tv_admm
    examples/ct_astra_weighted_tv_admm
    examples/ct_svmbir_tv_multi
    examples/ct_svmbir_ppp_bm3d_admm_cg

docs/source/team.rst

@@ -21,8 +21,9 @@ Emeritus Developers
 Contributors
 ------------
 
+- `Weijie Gan <https://github.com/wjgancn>`_ (Non-blind variant of DnCNN)
 - `Oleg Korobkin <https://github.com/korobkin>`_ (BlockArray improvements)
-- `Yanpeng Yuan <https://github.com/yanpeng7>`_ (ASTRA interface improvements)
-- `Saurav Maheshkar <https://github.com/SauravMaheshkar>`_ (Improvements to pre-commit configuration)
 - `Andrew Leong <https://scholar.google.com/citations?user=-2wRWbcAAAAJ&hl=en>`_ (Improvements to optics module documentation)
-- `Weijie Gan <https://github.com/wjgancn>`_ (Non-blind variant of DnCNN)
+- `Saurav Maheshkar <https://github.com/SauravMaheshkar>`_ (Improvements to pre-commit configuration)
+- `Yanpeng Yuan <https://github.com/yanpeng7>`_ (ASTRA interface improvements)
+- `Li-Ta (Ollie) Lo <https://github.com/ollielo>`_ (ASTRA interface improvements)

examples/scriptcheck.sh

@@ -12,20 +12,23 @@ Usage: $SCRIPT [-h] [-d]
           [-e] Display excerpt of error message on failure
           [-d] Skip tests involving additional data downloads
           [-t] Skip tests related to learned model training
+          [-g] Skip tests that need a GPU
 EOF
 )
 
 OPTIND=1
 DISPLAY_ERROR=0
 SKIP_DOWNLOAD=0
 SKIP_TRAINING=0
-while getopts ":hedt" opt; do
+SKIP_GPU=0
+while getopts ":hedtg" opt; do
     case $opt in
-	h) echo "$USAGE"; exit 0;;
-	e) DISPLAY_ERROR=1;;
-	d) SKIP_DOWNLOAD=1;;
-	t) SKIP_TRAINING=1;;
-	\?) echo "Error: invalid option -$OPTARG" >&2
+    h) echo "$USAGE"; exit 0;;
+    e) DISPLAY_ERROR=1;;
+    d) SKIP_DOWNLOAD=1;;
+    t) SKIP_TRAINING=1;;
+    g) SKIP_GPU=1;;
+    \?) echo "Error: invalid option -$OPTARG" >&2
             echo "$USAGE" >&2
             exit 1
             ;;
@@ -74,15 +77,19 @@ for f in $SCRIPTPATH/scripts/*.py; do
 
     # Skip problem cases.
     if [ $SKIP_DOWNLOAD -eq 1 ] && grep -q '_microscopy' <<< $f; then
-	printf "%s\n" skipped
-	continue
+        printf "%s\n" skipped
+        continue
     fi
     if [ $SKIP_TRAINING -eq 1 ]; then
-	if grep -q '_datagen' <<< $f || grep -q '_train' <<< $f; then
-	    printf "%s\n" skipped
-	    continue
+    if grep -q '_datagen' <<< $f || grep -q '_train' <<< $f; then
+        printf "%s\n" skipped
+        continue
         fi
     fi
+    if [ $SKIP_GPU -eq 1 ] && grep -q '_astra_3d' <<< $f; then
+        printf "%s\n" skipped
+        continue
+    fi
 
     # Create temporary copy of script with all algorithm maxiter values set
     # to small number and final input statements commented out.
@@ -95,9 +102,9 @@ for f in $SCRIPTPATH/scripts/*.py; do
     else
         printf "%s\n" FAILED
         retval=1
-	if [ $DISPLAY_ERROR -eq 1 ]; then
-	   echo "$output" | tail -8 | sed -e 's/^/    /'
-	fi
+    if [ $DISPLAY_ERROR -eq 1 ]; then
+       echo "$output" | tail -8 | sed -e 's/^/    /'
+    fi
     fi
 
     # Remove temporary script.

examples/scripts/README.rst

@@ -15,6 +15,8 @@ Computed Tomography
       Parameter Tuning for TV-Regularized Abel Inversion
    `ct_astra_noreg_pcg.py <ct_astra_noreg_pcg.py>`_
       CT Reconstruction with CG and PCG
+   `ct_astra_3d_tv_admm.py <ct_astra_3d_tv_admm.py>`_
+      3D TV-Regularized Sparse-View CT Reconstruction
    `ct_astra_tv_admm.py <ct_astra_tv_admm.py>`_
       TV-Regularized Sparse-View CT Reconstruction
    `ct_astra_weighted_tv_admm.py <ct_astra_weighted_tv_admm.py>`_
@@ -153,6 +155,8 @@ Total Variation
       Parameter Tuning for TV-Regularized Abel Inversion
    `ct_astra_tv_admm.py <ct_astra_tv_admm.py>`_
       TV-Regularized Sparse-View CT Reconstruction
+   `ct_astra_3d_tv_admm.py <ct_astra_3d_tv_admm.py>`_
+      3D TV-Regularized Sparse-View CT Reconstruction
    `ct_astra_weighted_tv_admm.py <ct_astra_weighted_tv_admm.py>`_
       TV-Regularized Low-Dose CT Reconstruction
    `ct_svmbir_tv_multi.py <ct_svmbir_tv_multi.py>`_
@@ -244,6 +248,8 @@ ADMM
       Parameter Tuning for TV-Regularized Abel Inversion
    `ct_astra_tv_admm.py <ct_astra_tv_admm.py>`_
       TV-Regularized Sparse-View CT Reconstruction
+   `ct_astra_3d_tv_admm.py <ct_astra_3d_tv_admm.py>`_
+      3D TV-Regularized Sparse-View CT Reconstruction
    `ct_astra_weighted_tv_admm.py <ct_astra_weighted_tv_admm.py>`_
       TV-Regularized Low-Dose CT Reconstruction
    `ct_svmbir_tv_multi.py <ct_svmbir_tv_multi.py>`_

examples/scripts/ct_astra_3d_tv_admm.py

@@ -0,0 +1,116 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# This file is part of the SCICO package. Details of the copyright
+# and user license can be found in the 'LICENSE.txt' file distributed
+# with the package.
+
+r"""
+3D TV-Regularized Sparse-View CT Reconstruction
+===============================================
+
+This example demonstrates solution of a sparse-view, 3D CT
+reconstruction problem with isotropic total variation (TV)
+regularization
+
+  $$\mathrm{argmin}_{\mathbf{x}} \; (1/2) \| \mathbf{y} - A \mathbf{x}
+  \|_2^2 + \lambda \| C \mathbf{x} \|_{2,1} \;,$$
+
+where $A$ is the Radon transform, $\mathbf{y}$ is the sinogram, $C$ is
+a 3D finite difference operator, and $\mathbf{x}$ is the desired
+image.
+"""
+
+
+import numpy as np
+
+import jax
+
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+from scico import functional, linop, loss, metric, plot
+from scico.examples import create_tangle_phantom
+from scico.linop.radon_astra import TomographicProjector
+from scico.optimize.admm import ADMM, LinearSubproblemSolver
+from scico.util import device_info
+
+"""
+Create a ground truth image and projector.
+"""
+
+Nx = 128
+Ny = 256
+Nz = 64
+
+tangle = create_tangle_phantom(Nx, Ny, Nz)
+tangle = jax.device_put(tangle)
+
+n_projection = 10  # number of projections
+angles = np.linspace(0, np.pi, n_projection)  # evenly spaced projection angles
+A = TomographicProjector(
+    tangle.shape, [1.0, 1.0], [Nz, max(Nx, Ny)], angles
+)  # Radon transform operator
+y = A @ tangle  # sinogram
+
+
+"""
+Set up ADMM solver object.
+"""
+λ = 2e0  # L1 norm regularization parameter
+ρ = 5e0  # ADMM penalty parameter
+maxiter = 25  # number of ADMM iterations
+cg_tol = 1e-4  # CG relative tolerance
+cg_maxiter = 25  # maximum CG iterations per ADMM iteration
+
+# The append=0 option makes the results of horizontal and vertical
+# finite differences the same shape, which is required for the L21Norm,
+# which is used so that g(Cx) corresponds to isotropic TV.
+C = linop.FiniteDifference(input_shape=tangle.shape, append=0)
+g = λ * functional.L21Norm()
+
+f = loss.SquaredL2Loss(y=y, A=A)
+
+x0 = A.T(y)
+
+solver = ADMM(
+    f=f,
+    g_list=[g],
+    C_list=[C],
+    rho_list=[ρ],
+    x0=x0,
+    maxiter=maxiter,
+    subproblem_solver=LinearSubproblemSolver(cg_kwargs={"tol": cg_tol, "maxiter": cg_maxiter}),
+    itstat_options={"display": True, "period": 5},
+)
+
+"""
+Run the solver.
+"""
+print(f"Solving on {device_info()}\n")
+solver.solve()
+hist = solver.itstat_object.history(transpose=True)
+tangle_recon = solver.x
+
+print(
+    "TV Restruction\nSNR: %.2f (dB), MAE: %.3f"
+    % (metric.snr(tangle, tangle_recon), metric.mae(tangle, tangle_recon))
+)
+
+"""
+Show the recovered image.
+"""
+fig, ax = plot.subplots(nrows=1, ncols=2, figsize=(7, 5))
+plot.imview(tangle[32], title="Ground truth (central slice)", cbar=None, fig=fig, ax=ax[0])
+
+plot.imview(
+    tangle_recon[32],
+    title="TV Reconstruction (central slice)\nSNR: %.2f (dB), MAE: %.3f"
+    % (metric.snr(tangle, tangle_recon), metric.mae(tangle, tangle_recon)),
+    fig=fig,
+    ax=ax[1],
+)
+divider = make_axes_locatable(ax[1])
+cax = divider.append_axes("right", size="5%", pad=0.2)
+fig.colorbar(ax[1].get_images()[0], cax=cax, label="arbitrary units")
+fig.show()
+
+input("\nWaiting for input to close figures and exit")

examples/scripts/index.rst

@@ -12,6 +12,7 @@ Computed Tomography
    - ct_abel_tv_admm.py
    - ct_abel_tv_admm_tune.py
    - ct_astra_noreg_pcg.py
+   - ct_astra_3d_tv_admm.py
    - ct_astra_tv_admm.py
    - ct_astra_weighted_tv_admm.py
    - ct_svmbir_tv_multi.py
@@ -95,6 +96,7 @@ Total Variation
    - ct_abel_tv_admm.py
    - ct_abel_tv_admm_tune.py
    - ct_astra_tv_admm.py
+   - ct_astra_3d_tv_admm.py
    - ct_astra_weighted_tv_admm.py
    - ct_svmbir_tv_multi.py
    - deconv_circ_tv_admm.py
@@ -150,6 +152,7 @@ ADMM
    - ct_abel_tv_admm.py
    - ct_abel_tv_admm_tune.py
    - ct_astra_tv_admm.py
+   - ct_astra_3d_tv_admm.py
    - ct_astra_weighted_tv_admm.py
    - ct_svmbir_tv_multi.py
    - ct_svmbir_ppp_bm3d_admm_cg.py

scico/examples.py

@@ -511,6 +511,40 @@ def create_conv_sparse_phantom(Nx: int, Nnz: int) -> Tuple[np.ndarray, np.ndarra
     return h, x
 
 
+def create_tangle_phantom(nx: int, ny: int, nz: int) -> snp.Array:
+    """Construct a volume phantom.
+
+    Args:
+        nx: x-size of output.
+        ny: y-size of output.
+        nz: z-size of output.
+
+    Returns:
+        An array with shape (nz, ny, nx).
+
+    """
+    xs = 1.0 * np.linspace(-1.0, 1.0, nx)
+    ys = 1.0 * np.linspace(-1.0, 1.0, ny)
+    zs = 1.0 * np.linspace(-1.0, 1.0, nz)
+
+    # default ordering for meshgrid is `xy`, this makes inputs of length
+    # M, N, P will create a mesh of N, M, P. Thus we want ys, zs and xs.
+    xx, yy, zz = np.meshgrid(ys, zs, xs, copy=True)
+    xx = 3.0 * xx
+    yy = 3.0 * yy
+    zz = 3.0 * zz
+    values = (
+        xx * xx * xx * xx
+        - 5.0 * xx * xx
+        + yy * yy * yy * yy
+        - 5.0 * yy * yy
+        + zz * zz * zz * zz
+        - 5.0 * zz * zz
+        + 11.8
+    ) * 0.2 + 0.5
+    return (values < 2.0).astype(float)
+
+
 def spnoise(
     img: Union[np.ndarray, snp.Array], nfrac: float, nmin: float = 0.0, nmax: float = 1.0
 ) -> Union[np.ndarray, snp.Array]: