{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Standardized Uptake Value Ratio\n", "\n", "To illustrate standardized uptake value ratio (SUVR) calculation,\n", "we will download an 18F-AV45 amyloid\n", "PET scan from The Dallas Lifespan Brain Study via OpenNeuro.\n", "This PET scan is reconstructed as a single time frame, so it is a 3-D image.\n", "(We can still use _Dynamic PET_ to read it, but most of the functions\n", "implemented in _Dynamic PET_ will not be relevant.)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import requests\n", "\n", "\n", "outdir = Path.cwd() / \"nb_data\"\n", "outdir.mkdir(exist_ok=True)\n", "\n", "petjson_fname = outdir / \"pet_av45.json\"\n", "pet_fname = outdir / \"pet_av45.nii.gz\"\n", "\n", "baseurl = \"https://s3.amazonaws.com/openneuro.org/ds004856/sub-1003/ses-wave1/pet/\"\n", "\n", "peturl = (\n", " baseurl\n", " + \"sub-1003_ses-wave1_trc-18FAV45_run-1_pet.nii.gz\"\n", " + \"?versionId=qL.9p.hInakWrNSF1LeefT4VOIuBy6Xm\"\n", ")\n", "\n", "if not petjson_fname.exists():\n", " r = requests.get(\n", " baseurl\n", " + \"sub-1003_ses-wave1_trc-18FAV45_run-1_pet.json\"\n", " + \"?versionId=HvaYMcTWZjYwq6GVwjfeePZ9dKAtJlFM\",\n", " timeout=10,\n", " )\n", " r.raise_for_status()\n", " with open(petjson_fname, \"wb\") as f:\n", " f.write(r.content)\n", "\n", "if not pet_fname.exists():\n", " with requests.get(peturl, timeout=10, stream=True) as r:\n", " r.raise_for_status()\n", " with open(pet_fname, \"wb\") as f:\n", " for chunk in r.iter_content(chunk_size=8192):\n", " f.write(chunk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the time of writing of this notebook, this dataset is not PET-BIDS valid.\n", "Trying to read it with the `load` function from\n", "`dynamicpet.petbids.petbidsimage` will fail.\n", "Because of this, we need to fix the json first.\n", "\n", "We can first read the json using the `read_json` from the same module.\n", "`read_json` does not perform any validity checks (and does not look at the\n", "corresponding imaging data at all)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from dynamicpet.petbids.petbidsjson import read_json\n", "\n", "\n", "json = read_json(petjson_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The problem with this dataset is that the image contains only a single time\n", "frame, but the json indicates two time frames, with the second one having a\n", "duration of 0." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FrameDuration: [600000]\n", "FrameTimesStart: [0]\n" ] } ], "source": [ "print(f\"FrameDuration: {json[\"FrameDuration\"]}\")\n", "print(f\"FrameTimesStart: {json[\"FrameTimesStart\"]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We modify these tags by keeping their first element only." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FrameDuration: [600000]\n", "FrameTimesStart: [0]\n" ] } ], "source": [ "json.update(\n", " {\n", " \"FrameDuration\": json[\"FrameDuration\"][:1],\n", " \"FrameTimesStart\": json[\"FrameTimesStart\"][:1],\n", " }\n", ")\n", "\n", "print(f\"FrameDuration: {json[\"FrameDuration\"]}\")\n", "print(f\"FrameTimesStart: {json[\"FrameTimesStart\"]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the frame timing information is fixed, we update the json file using\n", "the `write_json` function:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from dynamicpet.petbids.petbidsjson import write_json\n", "\n", "\n", "write_json(json, petjson_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, reading in this dataset will work:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from dynamicpet.petbids.petbidsimage import load\n", "\n", "\n", "pet = load(pet_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To calculate SUVR, we need to specify a reference region.\n", "Usually, some type of cerebellar reference would be used for\n", "18F-AV45.\n", "In this notebook, however, we use an (approximate) whole brain reference region\n", "for simplicity." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from nilearn.image import threshold_img\n", "from nilearn.masking import compute_background_mask\n", "from nilearn.plotting import plot_anat\n", "from scipy.ndimage import binary_fill_holes\n", "\n", "\n", "# get an approximate brain mask\n", "refmask = compute_background_mask(\n", " threshold_img(pet.img, threshold=1400, two_sided=False),\n", " connected=True,\n", " opening=3,\n", ")\n", "refmask_data = binary_fill_holes(refmask.get_fdata())\n", "\n", "refmask = refmask.__class__(refmask_data.astype(\"float\"), affine=refmask.affine)\n", "display = plot_anat(\n", " pet_fname,\n", " colorbar=True,\n", " draw_cross=False,\n", " title=\"Reference region (brain) mask overlaid on PET\",\n", ")\n", "display.add_overlay(refmask, alpha=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we calculate the mean time activity \"curve\" (TAC) in this reference region\n", "mask." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2910.44148055]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reftac = pet.mean_timeseries_in_mask(mask=refmask.get_fdata())\n", "\n", "reftac.dataobj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since this PET image has a single time frame, the \"TAC\" will also have a\n", "single element." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2910.44148055]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reftac.dataobj" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can fit the SUVR model as follows:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from dynamicpet.kineticmodel.suvr import SUVR\n", "\n", "\n", "res = SUVR(reftac, pet)\n", "res.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the names of the parameters using the `get_param_names` function.\n", "The only parameter available in the `SUVR` class is `suvr`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['suvr']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.get_param_names()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best way to access the calculated parameter is via the `get_parameter`\n", "function, which will appropriately reshape the calculated parameter if needed\n", "and make it into a `SpatialImage`.\n", "We can then use `nilearn` functions to plot the image." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_anat(\n", " res.get_parameter(\"suvr\"),\n", " colorbar=True,\n", " draw_cross=False,\n", " title=\"SUVR image\",\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Command line interface\n", "\n", "Instead of using the Python API, we can also perform SUVR calculation via the\n", "command line.\n", "\n", "First, we need to have the reference region mask to disk." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "refmask.to_filename(\"nb_data/refmask.nii.gz\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can run the command from terminal:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "!kineticmodel nb_data/pet_av45.nii.gz --model SUVR --refmask nb_data/refmask.nii.gz --outputdir nb_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SUVR image will be saved to a file suffixed with `_km-suvr_kp-suvr`,\n", "where `km` stands for kinetic model and `kp` stands for kinetic parameter.\n", "This file naming convention is based on the PET-BIDS Derivatives Extension\n", "(work in progress)." ] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "hackthon", "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": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 2 }