deploy: 00d18ae

pr-98/.buildinfo

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+# Sphinx build info version 1
+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: f890332ff33e9a7902f7376252864dd5
+tags: 645f666f9bcd5a90fca523b33c5a78b7

pr-98/_downloads/110a8e9e8dde6fdb85f465e626976517/01_run_pet_non_tof_listmode_projector.py

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+"""
+PET non-TOF listmode projector
+==============================
+
+In this example we will show how to setup and use a non-TOF 
+PET listmode projector including geometrical forward projection
+in listmode, image-based resolution model and a listmode 
+attenuation model.
+
+.. tip::
+    parallelproj is python array API compatible meaning it supports different 
+    array backends (e.g. numpy, cupy, torch, ...) and devices (CPU or GPU).
+    Choose your preferred array API ``xp`` and device ``dev`` below.
+
+.. image:: https://mybinder.org/badge_logo.svg
+ :target: https://mybinder.org/v2/gh/gschramm/parallelproj/master?labpath=examples
+"""
+
+# %%
+import array_api_compat.numpy as xp
+
+# import array_api_compat.cupy as xp
+# import array_api_compat.torch as xp
+
+import parallelproj
+from array_api_compat import to_device
+import array_api_compat.numpy as np
+import matplotlib.pyplot as plt
+
+# choose a device (CPU or CUDA GPU)
+if "numpy" in xp.__name__:
+    # using numpy, device must be cpu
+    dev = "cpu"
+elif "cupy" in xp.__name__:
+    # using cupy, only cuda devices are possible
+    dev = xp.cuda.Device(0)
+elif "torch" in xp.__name__:
+    # using torch valid choices are 'cpu' or 'cuda'
+    dev = "cuda"
+
+# %%
+# Setup a small regular polygon PET scanner with 5 rings (polygons)
+# -----------------------------------------------------------------
+
+num_rings = 4
+scanner = parallelproj.RegularPolygonPETScannerGeometry(
+    xp,
+    dev,
+    radius=65.0,
+    num_sides=12,
+    num_lor_endpoints_per_side=15,
+    lor_spacing=2.3,
+    ring_positions=xp.linspace(-10, 10, num_rings),
+    symmetry_axis=1,
+)
+
+# %%
+# Generate 4 arbitrary listmode events
+# ------------------------------------
+
+start_ring = xp.asarray([2, 1, 0, 3], device=dev)
+start_xtal = xp.asarray([0, 59, 143, 75], device=dev)
+
+end_ring = xp.asarray([2, 0, 1, 3], device=dev)
+end_xtal = xp.asarray([79, 140, 33, 147], device=dev)
+
+event_start_coordinates = scanner.get_lor_endpoints(start_ring, start_xtal)
+event_end_coordinates = scanner.get_lor_endpoints(end_ring, end_xtal)
+
+print(event_start_coordinates)
+print(event_end_coordinates)
+
+# %%
+# Show the scanner geometry and the events
+# ----------------------------------------
+
+fig = plt.figure(figsize=(8, 8))
+ax = fig.add_subplot(111, projection="3d")
+scanner.show_lor_endpoints(ax)
+for i in range(event_start_coordinates.shape[0]):
+    ax.plot(
+        [float(event_start_coordinates[i, 0]), float(event_end_coordinates[i, 0])],
+        [float(event_start_coordinates[i, 1]), float(event_end_coordinates[i, 1])],
+        [float(event_start_coordinates[i, 2]), float(event_end_coordinates[i, 2])],
+    )
+fig.tight_layout()
+fig.show()
+
+
+# %%
+# Setup a listmode projector and a test image
+# -------------------------------------------
+
+img_shape = (40, 9, 40)
+voxel_size = (2.0, 3.0, 2.0)
+
+lm_proj = parallelproj.ListmodePETProjector(
+    event_start_coordinates, event_end_coordinates, img_shape, voxel_size
+)
+
+x = xp.ones(img_shape, dtype=xp.float32, device=dev)
+
+# %%
+# Perform listmode forward and back projections
+# ---------------------------------------------
+
+x_fwd = lm_proj(x)
+print(x_fwd)
+
+# back project a list of ones
+ones_list = xp.ones(lm_proj.num_events, dtype=xp.float32, device=dev)
+y_back = lm_proj.adjoint(ones_list)
+
+# %%
+# Show the backprojected list of ones (events)
+# --------------------------------------------
+
+fig2, ax2 = plt.subplots(3, 3, figsize=(8, 8))
+vmax = float(xp.max(y_back))
+for i in range(ax2.size):
+    if i < y_back.shape[1]:
+        axx = ax2.ravel()[i]
+        axx.imshow(
+            parallelproj.to_numpy_array(y_back[:, i, :].T),
+            cmap="Greys",
+            vmin=0,
+            vmax=vmax,
+        )
+        axx.set_title(f"img plane {i}", fontsize="medium")
+    else:
+        ax2.ravel()[i].set_axis_off()
+fig2.tight_layout()
+fig2.show()
+
+# %%
+# Combine the listmode projector with a resolution and attenuation model
+# ----------------------------------------------------------------------
+
+# setup a simple image-based resolution model with an Gaussian FWHM of 4.5mm
+res_model = parallelproj.GaussianFilterOperator(
+    lm_proj.in_shape, sigma=4.5 / (2.35 * lm_proj.voxel_size)
+)
+
+# define arbritrary attenuation factors
+att_list = xp.asarray([0.3, 0.4, 0.2, 0.6], device=dev)
+att_op = parallelproj.ElementwiseMultiplicationOperator(att_list)
+
+
+lm_proj_with_res_model_and_att = parallelproj.CompositeLinearOperator(
+    (att_op, lm_proj, res_model)
+)
+
+x_fwd2 = lm_proj_with_res_model_and_att(x)
+print(x_fwd2)
+
+y_back2 = lm_proj_with_res_model_and_att.adjoint(ones_list)
+
+# %%
+# Show the backprojected list of ones (events)
+# --------------------------------------------
+
+fig3, ax3 = plt.subplots(3, 3, figsize=(8, 8))
+vmax = float(xp.max(y_back2))
+for i in range(ax3.size):
+    if i < y_back.shape[1]:
+        axx = ax3.ravel()[i]
+        axx.imshow(
+            parallelproj.to_numpy_array(y_back2[:, i, :].T),
+            cmap="Greys",
+            vmin=0,
+            vmax=vmax,
+        )
+        axx.set_title(f"img plane {i}", fontsize="medium")
+    else:
+        ax3.ravel()[i].set_axis_off()
+fig3.tight_layout()
+fig3.show()

pr-98/_downloads/1209efffc3032a2746127056278142b7/01_run_pet_non_tof_listmode_projector.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# PET non-TOF listmode projector\n\nIn this example we will show how to setup and use a non-TOF \nPET listmode projector including geometrical forward projection\nin listmode, image-based resolution model and a listmode \nattenuation model.\n\n.. tip::\n    parallelproj is python array API compatible meaning it supports different \n    array backends (e.g. numpy, cupy, torch, ...) and devices (CPU or GPU).\n    Choose your preferred array API ``xp`` and device ``dev`` below.\n\n<img src=\"https://mybinder.org/badge_logo.svg\" target=\"https://mybinder.org/v2/gh/gschramm/parallelproj/master?labpath=examples\">\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import array_api_compat.numpy as xp\n\n# import array_api_compat.cupy as xp\n# import array_api_compat.torch as xp\n\nimport parallelproj\nfrom array_api_compat import to_device\nimport array_api_compat.numpy as np\nimport matplotlib.pyplot as plt\n\n# choose a device (CPU or CUDA GPU)\nif \"numpy\" in xp.__name__:\n    # using numpy, device must be cpu\n    dev = \"cpu\"\nelif \"cupy\" in xp.__name__:\n    # using cupy, only cuda devices are possible\n    dev = xp.cuda.Device(0)\nelif \"torch\" in xp.__name__:\n    # using torch valid choices are 'cpu' or 'cuda'\n    dev = \"cuda\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup a small regular polygon PET scanner with 5 rings (polygons)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "num_rings = 4\nscanner = parallelproj.RegularPolygonPETScannerGeometry(\n    xp,\n    dev,\n    radius=65.0,\n    num_sides=12,\n    num_lor_endpoints_per_side=15,\n    lor_spacing=2.3,\n    ring_positions=xp.linspace(-10, 10, num_rings),\n    symmetry_axis=1,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generate 4 arbitrary listmode events\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "start_ring = xp.asarray([2, 1, 0, 3], device=dev)\nstart_xtal = xp.asarray([0, 59, 143, 75], device=dev)\n\nend_ring = xp.asarray([2, 0, 1, 3], device=dev)\nend_xtal = xp.asarray([79, 140, 33, 147], device=dev)\n\nevent_start_coordinates = scanner.get_lor_endpoints(start_ring, start_xtal)\nevent_end_coordinates = scanner.get_lor_endpoints(end_ring, end_xtal)\n\nprint(event_start_coordinates)\nprint(event_end_coordinates)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Show the scanner geometry and the events\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(figsize=(8, 8))\nax = fig.add_subplot(111, projection=\"3d\")\nscanner.show_lor_endpoints(ax)\nfor i in range(event_start_coordinates.shape[0]):\n    ax.plot(\n        [float(event_start_coordinates[i, 0]), float(event_end_coordinates[i, 0])],\n        [float(event_start_coordinates[i, 1]), float(event_end_coordinates[i, 1])],\n        [float(event_start_coordinates[i, 2]), float(event_end_coordinates[i, 2])],\n    )\nfig.tight_layout()\nfig.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup a listmode projector and a test image\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "img_shape = (40, 9, 40)\nvoxel_size = (2.0, 3.0, 2.0)\n\nlm_proj = parallelproj.ListmodePETProjector(\n    event_start_coordinates, event_end_coordinates, img_shape, voxel_size\n)\n\nx = xp.ones(img_shape, dtype=xp.float32, device=dev)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Perform listmode forward and back projections\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "x_fwd = lm_proj(x)\nprint(x_fwd)\n\n# back project a list of ones\nones_list = xp.ones(lm_proj.num_events, dtype=xp.float32, device=dev)\ny_back = lm_proj.adjoint(ones_list)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Show the backprojected list of ones (events)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig2, ax2 = plt.subplots(3, 3, figsize=(8, 8))\nvmax = float(xp.max(y_back))\nfor i in range(ax2.size):\n    if i < y_back.shape[1]:\n        axx = ax2.ravel()[i]\n        axx.imshow(\n            parallelproj.to_numpy_array(y_back[:, i, :].T),\n            cmap=\"Greys\",\n            vmin=0,\n            vmax=vmax,\n        )\n        axx.set_title(f\"img plane {i}\", fontsize=\"medium\")\n    else:\n        ax2.ravel()[i].set_axis_off()\nfig2.tight_layout()\nfig2.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Combine the listmode projector with a resolution and attenuation model\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# setup a simple image-based resolution model with an Gaussian FWHM of 4.5mm\nres_model = parallelproj.GaussianFilterOperator(\n    lm_proj.in_shape, sigma=4.5 / (2.35 * lm_proj.voxel_size)\n)\n\n# define arbritrary attenuation factors\natt_list = xp.asarray([0.3, 0.4, 0.2, 0.6], device=dev)\natt_op = parallelproj.ElementwiseMultiplicationOperator(att_list)\n\n\nlm_proj_with_res_model_and_att = parallelproj.CompositeLinearOperator(\n    (att_op, lm_proj, res_model)\n)\n\nx_fwd2 = lm_proj_with_res_model_and_att(x)\nprint(x_fwd2)\n\ny_back2 = lm_proj_with_res_model_and_att.adjoint(ones_list)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Show the backprojected list of ones (events)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig3, ax3 = plt.subplots(3, 3, figsize=(8, 8))\nvmax = float(xp.max(y_back2))\nfor i in range(ax3.size):\n    if i < y_back.shape[1]:\n        axx = ax3.ravel()[i]\n        axx.imshow(\n            parallelproj.to_numpy_array(y_back2[:, i, :].T),\n            cmap=\"Greys\",\n            vmin=0,\n            vmax=vmax,\n        )\n        axx.set_title(f\"img plane {i}\", fontsize=\"medium\")\n    else:\n        ax3.ravel()[i].set_axis_off()\nfig3.tight_layout()\nfig3.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}