Published 1st version

.gitignore

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+myEnv/
+**/__pycache__
+*.mat
+*.ini
+*.png
+env/
+data/
+*.dat
+
+**/.ipynb_checkpoints
+**/.virtual_documents
+
+Readme.pdf

Readme.md

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+# FastPtx: a python pTx pulse design tool for freely optimizing RF and gradient pulses with autodifferentiation
+
+## Author:
+
+Dario Bosch \<[email protected]>
+
+## Other Contributors:
+
+- Qi Wang \<[email protected]>
+	- helped with porting the bloch simulation code from C++ to Python
+	- helped with pytorch support
+- Alexander Loktyushin \<[email protected]>
+	- gave valueable input on optimization with pytorch
+    - helped working out bugs
+
+# How to use
+
+The following steps describe using Python Virtual Environments.
+If you prefer using Anaconda you can try adapting those steps. I don't see why it shouldn't work
+
+1. Open a terminal in the projects' main directory
+1. Create a python virtual environment:
+`python -m venv ./env`
+1. Activate the environment:
+`source ./env/bin/activate`
+1. Install the necessary python packages
+	- `pip install -r ./requirements.txt`
+1. Get the example data and put it into the directory ./data/ by running the downloadFiles.sh script
+    - `./downloadFiles.sh`
+1. open either `spyder` or `jupyter lab` from the active shell
+1. modify and run `calc_smallFA_paper.py`
+    - the line `dev = torch.device('cuda')` sets the calculation to happen on a GPU. If you don't have a CUDA-enabled GPU, set it to `cpu` instead.
+    - `do_UP = False` switches between tailored and universal pulses
+    - Flip angle, pulse duration, etc can also be controlled by changing the settings in the beginning of the file.
+    - Run by either executing the `calc_smallFA_paper.ipynb` notebook in jupyter or by running the `calc_smallFA_paper.py` script directly
+1. You can create an animation (gif) for an optimized pulse using the `animatePulse.ipynb` notebook
+
+# Citing
+If you use this code, please cite the corresponding paper:
+
+   Bosch, D.; Scheffler, K.: FastPtx: A Versatile Toolbox for Rapid, Joint Design of pTx RF and Gradient Pulses Using Pytorch's Autodifferentiation. Magnetic Resonance Materials in Physics, Biology and Medicine

animatePulse.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79067cb5-631f-4bd6-884f-e637bcac1431",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# %load animatePulse.py\n",
+    "#!/usr/bin/env python3\n",
+    "\"\"\"\n",
+    "Created on Sun Mar 20 23:52:57 2022\n",
+    "\n",
+    "@author: dario\n",
+    "\"\"\"\n",
+    "\n",
+    "from tools.io import readData, combineData, vectorizeData, \\\n",
+    "    writePulse, readVOPfile, interpData, addLocalB0data, readPulse\n",
+    "    \n",
+    "from tools.bloch import bloch\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "import os\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "fname = './data/dbase/GHAF-HEY2.mat'\n",
+    "maskName = 'brainMask'\n",
+    "dat = readData(fname,maskName=maskName)\n",
+    "\n",
+    "dat_v = vectorizeData(dat)\n",
+    "\n",
+    "[p,g] = readPulse('./UP_20deg_500us_16Tx31Rx.ini',orientation='unity')\n",
+    "p *= 10\n",
+    "nSteps = p.shape[0];\n",
+    "lim = bloch.getLimits()\n",
+    "lim['plotscale'] = 12\n",
+    "lim['maxRF'] = p.abs().max()\n",
+    "\n",
+    "f = bloch.plotVec(p,g,dat_v,10e-6,lim)\n",
+    "filename  = f'img/test.png'\n",
+    "f.savefig(filename)\n",
+    "\n",
+    "os.system('rm ./img/anim_*.png')\n",
+    "\n",
+    "for i in tqdm(range(nSteps)):\n",
+    "    mp = p.clone();\n",
+    "    mg = g.clone();\n",
+    "    mp[i+1:,:] = 0\n",
+    "    mg[:,i+1:] = 0\n",
+    "    f = bloch.plotVec(mp,mg,dat_v,10e-6,lim,save=True)\n",
+    "    filename = f'img/anim_{i:03}.png'\n",
+    "    #f = plt.gcf()\n",
+    "    f.savefig(filename)\n",
+    "    plt.close();\n",
+    "#    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14800445-b449-4c56-94ba-590de8bfe5d9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "os.system('pwd')\n",
+    "os.system(f'bash -c \"convert -crop 1160x1070+00+60 +repage -delay 20 -loop 0   ./img/anim_{{000..{nSteps-2:003}}}.png ./img/animated.gif\"')\n",
+    "os.system(f'convert -crop 1160x1070+00+60 +repage img/anim_{nSteps-1:03}.png ./img/temp.png')\n",
+    "os.system(f'convert ./img/animated.gif -delay 300 -loop 0 img/temp.png ./img/animated.gif')\n",
+    "os.system('convert ./img/animated.gif ./img/animated.mp4')\n",
+    "# for i in tqdm(range(nSteps)):\n",
+    "#     os.system(f'convert -crop 1160x0+00+60 +repage img/anim_{i:03}.png /tmp/temp.png')\n",
+    "#     if i==0:\n",
+    "#         os.system(f'convert -delay 20 -loop 0 /tmp/temp.png /tmp/animated.gif')\n",
+    "#     elif i==nSteps-1:\n",
+    "#         os.system(f'convert /tmp/animated.gif -delay 300 -loop 0 -crop 1160x0+00+60 +repage img/anim_{i:03}.png /tmp/animated.gif')\n",
+    "#     else:\n",
+    "#         os.system(f'convert /tmp/animated.gif -delay 20 -loop 0  /tmp/temp.png /tmp/animated.gif')\n",
+    "# os.system('mv /tmp/animated.gif ./img/animated.gif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7b1c9a2-61c3-4601-bd0f-35e26b7a288c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

calc_HF_pulse.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "601628c8-9cf1-41f1-b963-b1244909e981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run calc_HF_pulse.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a3fc916-3b63-41ba-b652-43b9f8bb81ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}