{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torchvision.transforms.functional import rotate\n",
    "from pytomography.utils import rotate_detector_z\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Last Time"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implemented\n",
    "\n",
    "$$H = \\sum_{\\theta} P(\\theta) A(\\theta) \\otimes \\hat{\\theta}$$\n",
    "\n",
    "and\n",
    "\n",
    "$$H^T = \\sum_{\\theta} A^T(\\theta)P^T(\\theta)  \\otimes \\hat{\\theta}^T$$\n",
    "\n",
    "where $A(\\theta)$ applies attenuation correction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Create objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dx = 0.3 #cm\n",
    "x = torch.linspace(-1,1,128)\n",
    "xv, yv, zv = torch.meshgrid(x,x,x, indexing='ij')\n",
    "# SPECT Object\n",
    "obj = (xv**2 + 0.9*zv**2 < 0.5) * (torch.abs(yv)<0.8)\n",
    "obj = obj.to(torch.float).unsqueeze(dim=0)\n",
    "# CT Object\n",
    "mu = (xv**2 + 0.9*zv**2 < 0.3) * (torch.abs(yv)<0.6)\n",
    "mu = mu.to(torch.float).unsqueeze(dim=0) * 0.1 #cm^-1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Forward and back project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rev_cumsum(x: torch.Tensor):\n",
    "    return torch.cumsum(x.flip(dims=(1,)), dim=1).flip(dims=(1,)) - x/2\n",
    "\n",
    "# Forward Projection\n",
    "angles = np.arange(0,360.,3)\n",
    "image = torch.zeros((1,len(angles),128,128))\n",
    "for i,angle in enumerate(angles):\n",
    "    mu_i = rotate_detector_z(mu, angle)\n",
    "    p_i = torch.exp(-rev_cumsum(mu_i * dx))\n",
    "    object_i = rotate_detector_z(obj,angle)\n",
    "    image[:,i] = (object_i*p_i).sum(axis=1)\n",
    "    \n",
    "# Back Projection\n",
    "obj_bp = torch.zeros([1, 128, 128, 128])\n",
    "for i,angle in enumerate(angles):\n",
    "    obj_bp_i = torch.ones([1, 128, 128, 128]) * image[:,i].unsqueeze(dim=1)\n",
    "    mu_i = rotate_detector_z(mu, angle)\n",
    "    p_i = torch.exp(-rev_cumsum(mu_i * dx))\n",
    "    obj_bp_i = obj_bp_i * p_i\n",
    "    obj_bp_i = rotate_detector_z(obj_bp_i, angle, negative=True)\n",
    "    obj_bp += obj_bp_i"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This Time"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to add PSF blurring to $H$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../images/psf.png\" alt= “” width=\"600\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The red distributions to the right represent probability distributions of where the emission is detected on the detector. The closer the emission is to the detector, the narrower the response function.\n",
    "\n",
    "* For many realistic cases, the curve is Gaussian, and the relationship between $\\sigma$ and the distance to the detector $z$ follows a linear relationship:\n",
    "\n",
    "$$\\sigma(r) = ar+b$$\n",
    "\n",
    "So how do we simulate this in $H$?\n",
    "\n",
    "1. Use different blurring kernels for each location, dependent on location from the detector with width given by $\\sigma=ar+b$\n",
    "2. Forward project"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../images/psf2.png\" alt= “” width=\"600\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this we'll need a few functions:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Get radial distances from projection to scanner, given $R$ (detector radius) and $dx$ (spacing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distance(Lx: int, R: float, dx: float):\n",
    "    if Lx%2==0:\n",
    "        r = R + (Lx/2 - np.arange(Lx) - 1/2) * dx\n",
    "    else:\n",
    "        r = R + ((Lx-1)/2 - np.arange(Lx) ) * dx\n",
    "    # Correction for if radius of scanner is inside the the bounds\n",
    "    r[r<0] = 0\n",
    "    return r"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This function uses `get_distance` to get $\\sigma$ at each point in the array. `collimator_slope` is $a$, and `collimator_intercept` is $b$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sigma(\n",
    "        radius: float,\n",
    "        dx: float,\n",
    "        Lx: tuple,\n",
    "        collimator_slope: float,\n",
    "        collimator_intercept: float\n",
    "    ) -> np.array:\n",
    "        distances = get_distance(Lx, radius, dx)\n",
    "        sigma = collimator_slope * distances + collimator_intercept\n",
    "        return sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.1835, 3.1625, 3.1415, 3.1205, 3.0995, 3.0785, 3.0575, 3.0365,\n",
       "       3.0155, 2.9945, 2.9735, 2.9525, 2.9315, 2.9105, 2.8895, 2.8685,\n",
       "       2.8475, 2.8265, 2.8055, 2.7845, 2.7635, 2.7425, 2.7215, 2.7005,\n",
       "       2.6795, 2.6585, 2.6375, 2.6165, 2.5955, 2.5745, 2.5535, 2.5325,\n",
       "       2.5115, 2.4905, 2.4695, 2.4485, 2.4275, 2.4065, 2.3855, 2.3645,\n",
       "       2.3435, 2.3225, 2.3015, 2.2805, 2.2595, 2.2385, 2.2175, 2.1965,\n",
       "       2.1755, 2.1545, 2.1335, 2.1125, 2.0915, 2.0705, 2.0495, 2.0285,\n",
       "       2.0075, 1.9865, 1.9655, 1.9445, 1.9235, 1.9025, 1.8815, 1.8605,\n",
       "       1.8395, 1.8185, 1.7975, 1.7765, 1.7555, 1.7345, 1.7135, 1.6925,\n",
       "       1.6715, 1.6505, 1.6295, 1.6085, 1.5875, 1.5665, 1.5455, 1.5245,\n",
       "       1.5035, 1.4825, 1.4615, 1.4405, 1.4195, 1.3985, 1.3775, 1.3565,\n",
       "       1.3355, 1.3145, 1.2935, 1.2725, 1.2515, 1.2305, 1.2095, 1.1885,\n",
       "       1.1675, 1.1465, 1.1255, 1.1045, 1.0835, 1.0625, 1.0415, 1.0205,\n",
       "       0.9995, 0.9785, 0.9575, 0.9365, 0.9155, 0.8945, 0.8735, 0.8525,\n",
       "       0.8315, 0.8105, 0.7895, 0.7685, 0.7475, 0.7265, 0.7055, 0.6845,\n",
       "       0.6635, 0.6425, 0.6215, 0.6005, 0.5795, 0.5585, 0.5375, 0.5165])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma = get_sigma(radius=25, dx=0.3, Lx=128, collimator_slope=0.07, collimator_intercept=0.1)\n",
    "sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember the object shape is (1,128,128,128) and we want to blur the first axis. We can build a convolutional neural network to apply a seperate Gaussian blurring kernel for each slice on axis 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = len(sigma)\n",
    "kernel_size = 17\n",
    "delta = 1e-9\n",
    "layer = torch.nn.Conv2d(N, N, kernel_size, groups=N, padding='same',\n",
    "                        padding_mode='zeros', bias=0)\n",
    "x_grid, y_grid = torch.meshgrid(2*[torch.arange(-int(kernel_size//2), int(kernel_size//2)+1)], indexing='ij')\n",
    "x_grid = x_grid.unsqueeze(dim=0).repeat((N,1,1))\n",
    "y_grid = y_grid.unsqueeze(dim=0).repeat((N,1,1))\n",
    "sigma = torch.tensor(sigma, dtype=torch.float32).reshape((N,1,1))\n",
    "kernel = torch.exp(-(x_grid**2 + y_grid**2) / (2*sigma**2 + delta))\n",
    "kernel = kernel / kernel.sum(axis=(1,2)).reshape(N,1,1)\n",
    "layer.weight.data = kernel.unsqueeze(dim=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the blurring is independent of the detector angle (unless the detector follows a non-circular path, and in the source code, we adjust the code to allow for this generality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forward Projection\n",
    "angles = np.arange(0,360.,3)\n",
    "image = torch.zeros((1,len(angles),128,128))\n",
    "for i,angle in enumerate(angles):\n",
    "    object_i = layer(rotate_detector_z(obj,angle))\n",
    "    image[:,i] = object_i.sum(axis=1)\n",
    "image = image.detach() #because was passed through network"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at some resulting images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iis = [0,10,20,30,40,50,60]\n",
    "fig, ax = plt.subplots(1,7,figsize=(15,3))\n",
    "[a.pcolormesh(image[0,i].T) for (a, i) in zip(ax, iis)]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_bp = torch.zeros([1, 128, 128, 128])\n",
    "for i,angle in enumerate(angles):\n",
    "    obj_bp_i = torch.ones([1, 128, 128, 128]) * image[:,i].unsqueeze(dim=1)\n",
    "    obj_bp_i = layer(obj_bp_i)\n",
    "    obj_bp_i = rotate_detector_z(obj_bp_i, angle, negative=True)\n",
    "    obj_bp += obj_bp_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "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": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
