{
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   "source": [
    "# Algorithms"
   ]
  },
  {
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   "source": [
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from pytomography.io.SPECT import simind, dicom\n",
    "from pytomography.projectors.SPECT import SPECTSystemMatrix\n",
    "from pytomography.transforms.SPECT import SPECTAttenuationTransform, SPECTPSFTransform\n",
    "from pytomography.algorithms import OSEM\n",
    "from pytomography.likelihoods import PoissonLogLikelihood\n",
    "from pytomography.algorithms import OSEM, OSMAPOSL, BSREM, KEM\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from pytomography.priors import RelativeDifferencePrior\n",
    "from pytomography.priors import TopNAnatomyNeighbourWeight\n",
    "from pytomography.likelihoods import PoissonLogLikelihood\n",
    "from pytomography.transforms.shared import KEMTransform\n",
    "from pytomography.projectors.shared import KEMSystemMatrix\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "662fb3f3",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# CHANGE THIS TO WHERE YOU DOWNLOADED THE TUTORIAL DATA\n",
    "PATH = '/mnt/mydisk2/pytomo_tutorial_data/SPECT'"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
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   },
   "source": [
    "The code below is a summary of the SIMIND/DICOM introduciton tutorials. You can select whether or not to explore the SIMIND/DICOM data here\n",
    "\n",
    "* This demonstrates what kind of code is reusable whether or not you're reconstructing SIMIND or DICOM data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "878763e4",
   "metadata": {
    "execution": {
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   "source": [
    "TYPE = 'DICOM' # DICOM or SIMIND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90402353",
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    {
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     "text": [
      "Given photopeak energy 208.0 keV and CT energy 130 keV from the CT DICOM header, the HU->mu conversion from the following configuration is used: 208.0 keV SPECT energy, 130 keV CT energy, and scanner model symbiat2\n",
      "Given photopeak energy 208.0 keV and CT energy 130 keV from the CT DICOM header, the HU->mu conversion from the following configuration is used: 208.0 keV SPECT energy, 130 keV CT energy, and scanner model symbiat2\n"
     ]
    }
   ],
   "source": [
    "if TYPE=='SIMIND':\n",
    "    data_path = os.path.join(PATH, 'SIMIND-Jaszak', 'lu177_SYME_jaszak')\n",
    "    IDX_AXIAL = 64 # for plotting\n",
    "    photopeak_path = os.path.join(data_path,'tot_w4.h00')\n",
    "    lower_path = os.path.join(data_path, 'tot_w5.h00')\n",
    "    upper_path = os.path.join(data_path, 'tot_w6.h00')\n",
    "    path_amap = os.path.join(data_path,'amap.hct')\n",
    "    object_meta, proj_meta = simind.get_metadata(photopeak_path)\n",
    "    photopeak = simind.get_projections(photopeak_path)\n",
    "    activity = 1000 # MBq \n",
    "    time_per_proj = 15 # s\n",
    "    photopeak = torch.poisson(photopeak * activity * time_per_proj)\n",
    "    lower = simind.get_projections(lower_path)\n",
    "    upper = simind.get_projections(upper_path)\n",
    "    lower_realization = torch.poisson(lower * activity * time_per_proj)\n",
    "    upper_realization = torch.poisson(upper * activity * time_per_proj)\n",
    "    ww_peak, ww_lower, ww_upper = [simind.get_energy_window_width(path) for path in [photopeak_path, lower_path, upper_path]]\n",
    "    scatter = simind.compute_EW_scatter(lower_realization, upper_realization , ww_lower, ww_upper, ww_peak)\n",
    "    amap = simind.get_attenuation_map(path_amap)\n",
    "    psf_meta = simind.get_psfmeta_from_header(photopeak_path)\n",
    "    att_transform = SPECTAttenuationTransform(amap)\n",
    "    psf_transform = SPECTPSFTransform(psf_meta)\n",
    "elif TYPE=='DICOM':\n",
    "    data_path = os.path.join(PATH, 'Lu177-NEMA-SymT2')\n",
    "    IDX_AXIAL = 61 # for plotting\n",
    "    path_CT = os.path.join(data_path, 'CT')\n",
    "    files_CT = [os.path.join(path_CT, file) for file in os.listdir(path_CT)]\n",
    "    file_NM = os.path.join(data_path, 'projection_data.dcm')\n",
    "    object_meta, proj_meta = dicom.get_metadata(file_NM, index_peak=0)\n",
    "    photopeak = dicom.get_projections(file_NM, index_peak=0)\n",
    "    scatter = dicom.get_energy_window_scatter_estimate(file_NM, index_peak=0, index_lower=1, index_upper=2)\n",
    "    att_transform = SPECTAttenuationTransform(filepath=files_CT)\n",
    "    att_transform.configure(object_meta, proj_meta)\n",
    "    amap = att_transform.attenuation_map\n",
    "    collimator_name = 'SY-ME'\n",
    "    energy_kev = 208 #keV\n",
    "    intrinsic_resolution=0.38 #mm\n",
    "    psf_meta = dicom.get_psfmeta_from_scanner_params(\n",
    "        collimator_name,\n",
    "        energy_kev,\n",
    "        intrinsic_resolution=intrinsic_resolution\n",
    "    )\n",
    "    psf_transform = SPECTPSFTransform(psf_meta)\n",
    "    \n",
    "system_matrix = SPECTSystemMatrix(\n",
    "    obj2obj_transforms = [att_transform,psf_transform],\n",
    "    proj2proj_transforms = [],\n",
    "    object_meta = object_meta,\n",
    "    proj_meta = proj_meta\n",
    ")\n",
    "likelihood = PoissonLogLikelihood(\n",
    "    system_matrix = system_matrix,\n",
    "    projections = photopeak,\n",
    "    additive_term = scatter\n",
    ")"
   ]
  },
  {
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   },
   "source": [
    "## Reconstruction Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
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   "source": [
    "With the likelihood established, we can now reconstruct our data. This tutorial will focus on ordered-subset preconditioned gradient ascent algorithms, which all have the form\n",
    "\n",
    "$$\\hat{f}^{n+1} = \\hat{f}^{n} + C^{n}(\\hat{f}^{n}) \\left[\\nabla_{f} L(g|f^{n}) - \\beta \\nabla_{f} V \\right] $$\n",
    "\n",
    "* $\\hat{f}^{n}$ is the object estimate of the nth iteration\n",
    "* $C^{n}(\\hat{f}^{n})$ is the preconditioner, which can depend on the current image estimate $\\hat{f}$\n",
    "* $\\nabla_{f} L(g|\\hat{f}^{n})$ is the gradient of the likelihood function\n",
    "* $\\nabla_{f} V$ is the gradient of some additional penalization or prior function, and $\\beta$ is a scaling factor."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### 4.1 OSEM\n",
    "\n",
    "The ordered-subset expectation maximum algorithm (OSEM) in SPECT imaging corresponds to\n",
    "\n",
    "* $C^{n}(\\hat{f}^{n}) = \\hat{f}^{n} / H^T 1$\n",
    "* $\\nabla_{f} L(g|f^{n}) = H^T \\left(\\frac{g}{H \\hat{f}^{n} + s}-H^T 1\\right)$ -> this is because of Poisson statistics\n",
    "* $V=0$\n",
    "\n",
    "so the reconstruction algorithm can be written as\n",
    "\n",
    "$$\\hat{f}^{n} = \\left[\\frac{1}{H^T 1} H^T \\left(\\frac{g}{H\\hat{f}^{n}+s}\\right)\\right] \\hat{f}^{n}$$\n",
    "\n",
    "To reconstruct with OSEM, we first create the reconstruction algorithm, then specify the number of iterations/subsets required for reconstruction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04378b92",
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   "source": [
    "recon_algorithm = OSEM(likelihood)\n",
    "recon_OSEM = recon_algorithm(n_iters = 4, n_subsets = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43604853",
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   },
   "source": [
    "### 4.2 OSMAPOSL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1276c8",
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   "source": [
    "The ordered subset maximum a posteriori one step late (OSMAPOSL) algorithm is an extension of OSEM that encorporates a prior function $V$\n",
    "\n",
    "* $C^{n}(\\hat{f}^{n}) = \\frac{\\hat{f}^{n}}{H^T 1 + \\beta \\nabla_{f^{n}} V}$\n",
    "* $\\nabla_{f^{n}} L(g|f^{n}) = H^T \\left(\\frac{g}{H \\hat{f}^{n} + s}-H^T 1\\right)$ -> this is because of Poisson statistics\n",
    "\n",
    "Thus the algorithm can be written as\n",
    "\n",
    "$$\\hat{f}^{n} = \\left[\\frac{1}{H^T 1  + \\beta \\nabla_{f} V} H^T \\left(\\frac{g}{H\\hat{f}^{n}+s}\\right)\\right] \\hat{f}^{n} $$\n",
    "\n",
    "The user need to provide the smoothing function $V$. In this case, we'll use a relative difference penalty for each voxel that only includes contributing terms from the 8 nearest neighbours based on the anatomical image.\n",
    "\n",
    "* `beta` (which is $\\beta$ above) is the scaling factor for the prior\n",
    "* `gamma` is a hyperparameter for the relative difference prior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d37bbc65",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-11-19T22:21:01.080059Z"
    },
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   "source": [
    "weight_top8anatomy = TopNAnatomyNeighbourWeight(amap, N_neighbours=8)\n",
    "prior_rdpap = RelativeDifferencePrior(beta=0.3, gamma=2, weight=weight_top8anatomy)\n",
    "# to use all nearest neighbours (non attenuation map based) use the code below:\n",
    "# prior_rdp = RelativeDifferencePrior(beta=0.3, gamma=2)\n",
    "recon_algorithm_osmaposl = OSMAPOSL(\n",
    "    likelihood = likelihood,\n",
    "    prior = prior_rdpap)\n",
    "recon_osmaposl = recon_algorithm_osmaposl(n_iters = 40, n_subsets = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4656dfb9",
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    },
    "tags": []
   },
   "source": [
    "### 4.3 BSREM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d367ce",
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   "source": [
    "The Block Sequential Regularized Expectation Maximum (BSREM) algorithm is another way of incorporating a prior funciton in a reconstruction algorithm. It has the form\n",
    "\n",
    "* $C^{n}(\\hat{f}^{n}) = \\alpha_n \\frac{\\hat{f}^{n}}{\\omega_m H^T 1}$ where $\\alpha_n$ is a user defined \"relaxation\" sequence and $\\omega_n = S_n/M$ is the number of elements in the subset $S_n$ considered in iteration $n$ divided by the total number of subsets $M$\n",
    "* $\\nabla_{f} L(g|f^{n}) = H^T \\left(\\frac{g}{H \\hat{f}^{n} + s}-H^T 1\\right)$ -> this is because of Poisson statistics\n",
    "\n",
    "The reconstruction algorithm can thus be written as\n",
    "\n",
    "$$\\hat{f}^{n+1} = \\hat{f}^{n} + \\alpha_n \\frac{\\hat{f}^{n}}{\\omega_n H^T 1} \\left[H^T \\left(\\frac{g}{H \\hat{f}^{n} + s} -1 \\right) - \\beta \\nabla_{f^{n}} V \\right] $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "210453a4",
   "metadata": {
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     "end_time": "2024-11-19T22:21:01.107529",
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    "tags": []
   },
   "source": [
    "In addition to the prior $V$, we also specify the relaxation sequence $\\alpha_n$ when creating the algorithm (if `relaxation_sequence` is not provided, it defaults to 1 for all $n$)"
   ]
  },
  {
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   "execution_count": 7,
   "id": "ffce5898",
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   "source": [
    "weight_top8anatomy = TopNAnatomyNeighbourWeight(amap, N_neighbours=8)\n",
    "prior_rdpap = RelativeDifferencePrior(beta=0.3, gamma=2, weight=weight_top8anatomy)\n",
    "recon_algorithm_bsrem = BSREM(\n",
    "    likelihood = likelihood,\n",
    "    prior = prior_rdpap,\n",
    "    relaxation_sequence = lambda n: 1/(n/50+1))\n",
    "recon_bsrem = recon_algorithm_bsrem(40,8)"
   ]
  },
  {
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   "id": "48ebc119",
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    "tags": []
   },
   "source": [
    "### 4.4 KEM"
   ]
  },
  {
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   "source": [
    "The kernel expectation maximum (KEM) algorithm assumes the reconstructed object $\\hat{f}^{n}$ can be represented by a kernel operator acting on a correspond 3D \"image\" of coefficients $\\hat{\\alpha}^{n}$ such that $\\hat{f}^{n} = K \\hat{\\alpha}^{n}$. The coefficient image $\\hat{\\alpha}^{n}$ is obtained via regular OSEM updates with a modified system matrix $\\tilde{H} = H K$, so image updates (in SPECT/PET) have the form:\n",
    "\n",
    "$$\\hat{\\alpha}^{n+1} = \\left[\\frac{1}{\\tilde{H}^T 1} \\tilde{H}^T \\left(\\frac{g}{\\tilde{H}\\hat{\\alpha}^{n}+s}\\right)\\right] \\hat{\\alpha}^{n}$$\n",
    "\n",
    "and the final image is givenby $\\hat{f}^{n} = K \\hat{\\alpha}^{n}$.\n",
    "\n",
    "We start by defining the kernel transform $K$. The matrix $K$ is a square matrix with components\n",
    "\n",
    "$$K_{ij} = \\left(\\prod_{v \\in S_v} k_v(v_i, v_j; ...) \\right) k_x(x_i, x_j; ...)$$\n",
    "\n",
    "where \n",
    "\n",
    "* $v$ corresponds to a \"support object\" (such as a CT scan, MRI, PET). These are denoted as `support_objects`\n",
    "* $S_v$ corresponds to the set of all support objects used\n",
    "* $k_v$ is the support kernel for support image $v$. These are denoted as `support_kernels`.\n",
    "* $x_i$ is the position of voxel $i$\n",
    "* $k_x$ is the kernel that weights based on positional differences. This is denoted as `distance_kernel`.\n",
    "* ... are additional parameters for each kernel. They are denoted as `distance_kernel_params` or `support_kernel_params`/\n",
    "\n",
    "In the `KEMTransform`, one gives a list of all the support objects via the `support_objects` argument. By default, the support kernel for each support object will be\n",
    "\n",
    "$$k_v(v_i, v_j; \\sigma) = \\exp\\left( -\\frac{(v_i-v_j)^2}{2\\sigma^2}\\right)$$\n",
    "\n",
    "In this default kernel, there is *one* additional required argument: $\\sigma$. The values of $\\sigma$ can be given using the `support_kernels_params` argument. The argument is a list of lists, where each internal list corresponds to all the additional required parameters for each kernel function. In the example below, one support object is used, and the function takes in one additional argument, so specifying $\\sigma=0.005$ cm $^{-1}$ requires giving `support_kernels_params=[[0.005]]`. Since there is a single distance kernel, any additional parameters to that kernel are given by a single list to the `distance_kernel_params` argument Alternative kernels can be specified using the `support_kernels` and `distance_kernel` arguments of the function; in this tutorial, we make use of the default ones:\n",
    "\n",
    "* **Note**: You may have to set `kernel_on_gpu=False` if your computer runs out of GPU memory; this means the kernel gets stored in regular RAM. Only portions of it get put on GPU during run time, but this results in a much longer run time (3x as long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "35dbce01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:22:25.466112Z",
     "iopub.status.busy": "2024-11-19T22:22:25.465614Z",
     "iopub.status.idle": "2024-11-19T22:22:25.470095Z",
     "shell.execute_reply": "2024-11-19T22:22:25.469447Z"
    },
    "papermill": {
     "duration": 0.010804,
     "end_time": "2024-11-19T22:22:25.471547",
     "exception": false,
     "start_time": "2024-11-19T22:22:25.460743",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "kem_transform = KEMTransform(\n",
    "    support_objects=[amap],\n",
    "    support_kernels_params=[[0.005]],\n",
    "    distance_kernel_params=[0.4],\n",
    "    top_N = 40,\n",
    "    kernel_on_gpu=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a2b2ae",
   "metadata": {
    "papermill": {
     "duration": 0.003842,
     "end_time": "2024-11-19T22:22:25.478993",
     "exception": false,
     "start_time": "2024-11-19T22:22:25.475151",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This transform is used to build the system matrix $\\tilde{H} = HK$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b11d598",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:22:25.487686Z",
     "iopub.status.busy": "2024-11-19T22:22:25.487234Z",
     "iopub.status.idle": "2024-11-19T22:22:53.330168Z",
     "shell.execute_reply": "2024-11-19T22:22:53.329113Z"
    },
    "papermill": {
     "duration": 27.849244,
     "end_time": "2024-11-19T22:22:53.331999",
     "exception": false,
     "start_time": "2024-11-19T22:22:25.482755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "system_matrix_kem = KEMSystemMatrix(system_matrix, kem_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3450413",
   "metadata": {
    "papermill": {
     "duration": 0.003698,
     "end_time": "2024-11-19T22:22:53.340462",
     "exception": false,
     "start_time": "2024-11-19T22:22:53.336764",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "One also has to define a new likelihood function, since the system matrix has now changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1b78781",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:22:53.352208Z",
     "iopub.status.busy": "2024-11-19T22:22:53.351644Z",
     "iopub.status.idle": "2024-11-19T22:22:53.356047Z",
     "shell.execute_reply": "2024-11-19T22:22:53.355374Z"
    },
    "papermill": {
     "duration": 0.011351,
     "end_time": "2024-11-19T22:22:53.357615",
     "exception": false,
     "start_time": "2024-11-19T22:22:53.346264",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "likelihood_kem = PoissonLogLikelihood(system_matrix_kem, photopeak, additive_term=scatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e8f8e15",
   "metadata": {
    "papermill": {
     "duration": 0.003749,
     "end_time": "2024-11-19T22:22:53.365106",
     "exception": false,
     "start_time": "2024-11-19T22:22:53.361357",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We then create the KEM reconstruction algorithm, which iterates over the $\\hat{\\alpha}^{n,m}$ and automatically computes $\\hat{f}^{n,m} = K \\hat{\\alpha}^{n,m}$ at output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35bc79e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:22:53.374762Z",
     "iopub.status.busy": "2024-11-19T22:22:53.374305Z",
     "iopub.status.idle": "2024-11-19T22:24:17.445692Z",
     "shell.execute_reply": "2024-11-19T22:24:17.444630Z"
    },
    "papermill": {
     "duration": 84.078753,
     "end_time": "2024-11-19T22:24:17.447677",
     "exception": false,
     "start_time": "2024-11-19T22:22:53.368924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "recon_algorithm_kem = KEM(likelihood_kem)\n",
    "recon_kem = recon_algorithm_kem(40,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364996cb",
   "metadata": {
    "papermill": {
     "duration": 0.00356,
     "end_time": "2024-11-19T22:24:17.455650",
     "exception": false,
     "start_time": "2024-11-19T22:24:17.452090",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c22ac043",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:24:17.464834Z",
     "iopub.status.busy": "2024-11-19T22:24:17.464212Z",
     "iopub.status.idle": "2024-11-19T22:24:17.468089Z",
     "shell.execute_reply": "2024-11-19T22:24:17.467543Z"
    },
    "papermill": {
     "duration": 0.009941,
     "end_time": "2024-11-19T22:24:17.469288",
     "exception": false,
     "start_time": "2024-11-19T22:24:17.459347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "recons = [recon_OSEM, recon_osmaposl, recon_bsrem, recon_kem]\n",
    "recon_names = ['OSEM', 'OSMAPOSL', 'BSREM', 'KEM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52ee2a9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-19T22:24:17.477660Z",
     "iopub.status.busy": "2024-11-19T22:24:17.477128Z",
     "iopub.status.idle": "2024-11-19T22:24:17.670307Z",
     "shell.execute_reply": "2024-11-19T22:24:17.669597Z"
    },
    "papermill": {
     "duration": 0.199305,
     "end_time": "2024-11-19T22:24:17.672145",
     "exception": false,
     "start_time": "2024-11-19T22:24:17.472840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,4,figsize=(10,3), gridspec_kw={'wspace':0.0})\n",
    "for i in range(4):\n",
    "    plt.sca(ax[i])\n",
    "    plt.imshow(recons[i][:,:,IDX_AXIAL].cpu().T , cmap='magma', interpolation='gaussian', origin='lower', vmax=20)\n",
    "    plt.text(0.03, 0.97, recon_names[i], color='white', fontsize=12, fontweight='bold', transform=plt.gca().transAxes, ha='left', va='top')\n",
    "    plt.xlim(35,90)\n",
    "    plt.ylim(95,35)\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytomo",
   "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": "3.12.9"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 302.20795,
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