FigB_fast_mri_noise_experiment_complete-R2_R4.ipynb

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 "cells": [
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   "execution_count": 1,
   "id": "4aaf82bd-c34d-446c-9506-ad38b82037bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "#os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" # so the IDs match nvidia-smi\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\" # \"0, 1\" for multiple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab44ab40-57f4-42de-8157-4aed9a834918",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-16 14:23:43.685418: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-12-16 14:23:44.357590: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sigpy as sp\n",
    "import sigpy.mri as mr\n",
    "import sigpy.plot as pl\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import loadmat, savemat\n",
    "from scipy import fft, fftpack\n",
    "from random import randrange\n",
    "import re\n",
    "\n",
    "import h5py\n",
    "from scipy.ndimage import zoom\n",
    "\n",
    "#from skimage.metrics import structural_similarity as ssim\n",
    "#from skimage.metrics import normalized_root_mse as nrmse\n",
    "#from skimage.metrics import peak_signal_noise_ratio as psnr\n",
    "\n",
    "# functions for metrics adapted from .py file provided by Efrat\n",
    "from metrics import calc_NRMSE as nrmse\n",
    "from metrics import calc_SSIM as ssim\n",
    "\n",
    "from ulf_recon_fns import ulfl1recon, applyMask, poissonDiscSigpy, find_lamda_mask, find_iter_mask, coil_combine, awgn\n",
    "#import display_fns\n",
    "\n",
    "import automap_fns #import automapRecon\n",
    "import unrolling_fns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a76a2a0-b114-4b5a-aaae-bceeef46bae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#font = {'family' : 'normal',\n",
    "#        'weight' : 'bold',\n",
    "#        'size'   : 22}\n",
    "\n",
    "#plt.rc('font', **font)\n",
    "plt.rc('font', size=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f818cac8-9ec5-4814-8739-9a2d0e39b33b",
   "metadata": {},
   "source": [
    "### Set mask parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861ed2b3-ffc7-4171-8098-59f1680527ef",
   "metadata": {},
   "source": [
    "## Importing fastMRI data and downsampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25676454-d49b-4296-8cc9-aa56c8060c56",
   "metadata": {},
   "source": [
    "The downsampled FastMRI data was saved externally to save processing time, it can be regenerated by uncommenting the following block of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4392e05-2fd8-41e2-bbb7-fc9078a84f59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# loading previously downsampled fast MRI data\n",
    "inputfile = 'data/fastmri/fastmri_75_64_25_val_all.npz'\n",
    "data = np.load(inputfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64a0805e-a2ac-43ac-8059-8db7f580dd2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ksp_fs_sc_all = data['ksp_fs_sc_all']\n",
    "ksp_us_sc_all = data['ksp_us_sc_all']\n",
    "img_gt_sc_all = data['img_gt_sc_all']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c94d5b5-c929-4860-a522-ea867296d7a8",
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     "execution_count": 6,
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     "output_type": "execute_result"
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   ],
   "source": [
    "ksp_fs_sc_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4cf1ead-c25d-46d2-9d74-d9262f2d2be7",
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     "data": {
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     "execution_count": 7,
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   "source": [
    "img_gt_sc_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5cd02c97-d11b-4a61-84a1-3a4038176bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# comment out unneeded code block below\n",
    "\n",
    "#quick run to generate images at high/low SNR\n",
    "noise_dB = np.array([60,70,80]) \n",
    "n_ex_cs = 3\n",
    "n_ex_test = 3\n",
    "\n",
    "#test run to generate manuscript plots\n",
    "# noise_dB = np.arange(50,92,2)\n",
    "# n_ex_cs = 3\n",
    "# n_ex_test = 3\n",
    "\n",
    "# #long run to generate manuscript plots\n",
    "# noise_dB = np.arange(50,92,2)\n",
    "# n_ex_cs = 20\n",
    "# n_ex_test = 100\n",
    "\n",
    "noise_dB_zero = noise_dB - 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f245945c-597d-4f89-a5e4-3c55ad77b6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 30, 40])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_dB_zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c094e8ef-8233-44f9-99bf-67750b4f07af",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#loss_fn = 'perceptual'\n",
    "loss_fn = 'mse'\n",
    "\n",
    "# defining mask\n",
    "R=2\n",
    "calib_size=10\n",
    "in_seed = 0\n",
    "imSize = img_gt_sc_all[:,:,:,0].shape\n",
    "mask = poissonDiscSigpy(imSize,R, in_seed,calib_size)\n",
    "\n",
    "mask_R2 = mask\n",
    "\n",
    "mps = np.ones(ksp_fs_sc_all[:,:,:,:,0].shape,dtype=complex) #single coil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed57f3b3-c309-413b-adde-3970ad0f833b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if R==2:\n",
    "    unroll_model_pth = 'trained_models/Unrolling/Unroll_mse_R2.pth'\n",
    "    model_real_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r2_real_noise_bkgd_20240318/checkpoint/model.keras' #fast mri model\n",
    "    model_imag_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r2_imag_noise_bkgd_20240318/checkpoint/model.keras'\n",
    "    \n",
    "elif R==4:\n",
    "    unroll_model_pth = 'trained_models/Unrolling/Unroll_mse_R4.pth'\n",
    "    model_real_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r4_real_noise_bkgd_20240328/checkpoint/model.keras' #fast mri model\n",
    "    model_imag_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r4_imag_noise_bkgd_20240328/checkpoint/model.keras'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd744ca5-bd93-4d4d-a93f-e55b35e37a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mdic = {\"mask\":mask}\n",
    "#savemat(\"mask_R2_PD.mat\",mdic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "338a191e-f33e-4b68-9569-130887771f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lamda_opt_vals = np.zeros((noise_dB.size,n_ex_cs))\n",
    "iter_opt_vals = np.zeros((noise_dB.size,n_ex_cs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb9608af-dbf8-42d8-87a0-112472a4f4db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/david/repos/Low_Field_MRI_Recon/metrics.py:39: RuntimeWarning: invalid value encountered in divide\n",
      "  im2_mag_uint8 = (I_pred * 255 / np.max(I_pred)).astype('uint8')\n",
      "/home/david/repos/Low_Field_MRI_Recon/metrics.py:39: RuntimeWarning: invalid value encountered in cast\n",
      "  im2_mag_uint8 = (I_pred * 255 / np.max(I_pred)).astype('uint8')\n"
     ]
    }
   ],
   "source": [
    "for j, noise_val in enumerate(noise_dB):\n",
    "    for k in range(n_ex_cs):\n",
    "        kspTestScaled = ksp_fs_sc_all[:,:,:,:,k]\n",
    "        volumeGTscaled = img_gt_sc_all[:,:,:,k]\n",
    "        \n",
    "        kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "        uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "        lamda_opt_vals[j,k] = find_lamda_mask(uskspace, volumeGTscaled, mps,calib_size, show_plot=False)\n",
    "        iter_opt_vals[j,k] = find_iter_mask(uskspace,volumeGTscaled, lamda_opt_vals[j,k], mps, show_plot=False)\n",
    "\n",
    "lamda_opt = lamda_opt_vals.mean(axis=1)\n",
    "iter_opt = iter_opt_vals.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8bea416-8079-4195-8e91-e05cca92d6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "nrmse_cs_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "nrmse_ifft_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "nrmse_amap_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "nrmse_unroll_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "\n",
    "ssim_cs_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "ssim_ifft_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "ssim_amap_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "ssim_unroll_vals = np.zeros((noise_dB.size,n_ex_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0ea5bde-7d9f-45d0-af2e-8cca3d608a1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-16 14:29:59.109295: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2024-12-16 14:29:59.109325: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:134] retrieving CUDA diagnostic information for host: autobeast\n",
      "2024-12-16 14:29:59.109332: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:141] hostname: autobeast\n",
      "2024-12-16 14:29:59.109384: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:165] libcuda reported version is: 550.54.14\n",
      "2024-12-16 14:29:59.109402: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:169] kernel reported version is: 550.54.14\n",
      "2024-12-16 14:29:59.109407: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:248] kernel version seems to match DSO: 550.54.14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/david/repos/Low_Field_MRI_Recon/metrics.py:38: RuntimeWarning: invalid value encountered in divide\n",
      "  im1_mag_uint8 = (I_true * 255 / np.max(I_true)).astype('uint8')\n",
      "/home/david/repos/Low_Field_MRI_Recon/metrics.py:38: RuntimeWarning: invalid value encountered in cast\n",
      "  im1_mag_uint8 = (I_true * 255 / np.max(I_true)).astype('uint8')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n"
     ]
    }
   ],
   "source": [
    "for j, noise_val in enumerate(noise_dB):\n",
    "    for k in range(n_ex_test):\n",
    "        kspTestScaled = ksp_fs_sc_all[:,:,:,:,k+n_ex_cs]\n",
    "        volumeGTscaled = img_gt_sc_all[:,:,:,k+n_ex_cs]\n",
    "        \n",
    "        kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "        uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "    \n",
    "        volifftrecon_mc = sp.ifft(uskspace,axes=[1,2,3])\n",
    "        volifftrecon = coil_combine(volifftrecon_mc,mps)# np.sqrt(np.sum(np.abs(volifftrecon_mc)**2,axis=0)).reshape(imSize)\n",
    "\n",
    "        volcsrecon = ulfl1recon(uskspace,mask,lamda_opt[j],iter_opt[j],mps)\n",
    "\n",
    "        amap_scale = 1\n",
    "        unroll_scale = 1#1/8\n",
    "        volunrollrecon = unrolling_fns.unrollingRecon(uskspace*unroll_scale,mask,unroll_model_pth)/unroll_scale\n",
    "        #volunrollrecon = np.roll(volunrollrecon,(1,1),axis=(0,2))\n",
    "        volamaprecon, volamaprecon_mc = automap_fns.automapRecon(uskspace*amap_scale,mps,model_real_dir,model_imag_dir)\n",
    "        volamaprecon = volamaprecon/amap_scale\n",
    "        volamaprecon_mc = volamaprecon_mc/amap_scale\n",
    "\n",
    "        mask_metrics = np.ma.getmask(np.ma.masked_less(abs(volumeGTscaled),0.05)) # a mask in the image domain for calculating image metrics (not for masking kspace)\n",
    "\n",
    "        volumeGTscaled[mask_metrics]=0\n",
    "        volcsrecon[mask_metrics]=0\n",
    "        volifftrecon[mask_metrics]=0\n",
    "        volamaprecon[mask_metrics]=0\n",
    "        volunrollrecon[mask_metrics]=0\n",
    "\n",
    "        nrmse_cs_vals[j,k] = nrmse(np.abs(volcsrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        nrmse_ifft_vals[j,k] = nrmse(np.abs(volifftrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        nrmse_amap_vals[j,k] = nrmse(np.abs(volamaprecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        nrmse_unroll_vals[j,k] = nrmse(np.abs(volunrollrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "\n",
    "        ssim_cs_vals[j,k] = ssim(np.abs(volcsrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        ssim_ifft_vals[j,k] = ssim(np.abs(volifftrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        ssim_amap_vals[j,k] = ssim(np.abs(volamaprecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        ssim_unroll_vals[j,k] = ssim(np.abs(volunrollrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b762d5cd-6ae4-40de-9830-16198e5304cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "nrmse_cs = nrmse_cs_vals.mean(axis=1)\n",
    "nrmse_ifft = nrmse_ifft_vals.mean(axis=1)\n",
    "nrmse_amap = nrmse_amap_vals.mean(axis=1)\n",
    "nrmse_unroll = nrmse_unroll_vals.mean(axis=1)\n",
    "\n",
    "nrmse_cs_sd = nrmse_cs_vals.std(axis=1)\n",
    "nrmse_ifft_sd = nrmse_ifft_vals.std(axis=1)\n",
    "nrmse_amap_sd = nrmse_amap_vals.std(axis=1)\n",
    "nrmse_unroll_sd = nrmse_unroll_vals.std(axis=1)\n",
    "\n",
    "ssim_cs = ssim_cs_vals.mean(axis=1)\n",
    "ssim_ifft = ssim_ifft_vals.mean(axis=1)\n",
    "ssim_amap = ssim_amap_vals.mean(axis=1)\n",
    "ssim_unroll = ssim_unroll_vals.mean(axis=1)\n",
    "\n",
    "ssim_cs_sd = ssim_cs_vals.std(axis=1)\n",
    "ssim_ifft_sd = ssim_ifft_vals.std(axis=1)\n",
    "ssim_amap_sd = ssim_amap_vals.std(axis=1)\n",
    "ssim_unroll_sd = ssim_unroll_vals.std(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46c5be34-8140-4eb7-9594-4f675338ee43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax3),(ax2, ax4)) = plt.subplots(2,2)\n",
    "fig.set_size_inches(6,6)\n",
    "fig.subplots_adjust(wspace=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "283084f6-0e87-481b-83e3-0f2c1d9bfa1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.YTick at 0x7f84b87ba440>,\n",
       " <matplotlib.axis.YTick at 0x7f84b87ba110>,\n",
       " <matplotlib.axis.YTick at 0x7f84b81839a0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line1 = ax1.plot(noise_dB_zero,nrmse_ifft, '-')\n",
    "line2 = ax1.plot(noise_dB_zero,nrmse_cs, '-')\n",
    "line3 = ax1.plot(noise_dB_zero,nrmse_amap, '-')\n",
    "line4 = ax1.plot(noise_dB_zero,nrmse_unroll, '-')\n",
    "#ax1.set(title='Comparison of Reconstruction Methods, R='+str(R),\n",
    "ax1.set(ylabel='NRMSE',\n",
    "xlabel='Input SNR (dB)')\n",
    "#ax1.set_xscale('log')\n",
    "ax1.legend(['Zero-filled', 'CS', 'AUTOMAP', 'Unrolled'])\n",
    "#ax1.legend([line1, line2, line3], ['label1', 'label2', 'label3'])\n",
    "#plt.ylim(0,0.085)\n",
    "#plt.xlim(55,90)\n",
    "fill1 = ax1.fill_between(noise_dB_zero,nrmse_ifft-nrmse_ifft_sd,nrmse_ifft+nrmse_ifft_sd,alpha=0.25)\n",
    "fill2 = ax1.fill_between(noise_dB_zero,nrmse_cs-nrmse_cs_sd,nrmse_cs+nrmse_cs_sd,alpha=0.25)\n",
    "fill3 = ax1.fill_between(noise_dB_zero,nrmse_amap-nrmse_amap_sd,nrmse_amap+nrmse_amap_sd,alpha=0.25)\n",
    "fill4 = ax1.fill_between(noise_dB_zero,nrmse_unroll-nrmse_unroll_sd,nrmse_unroll+nrmse_unroll_sd,alpha=0.25)\n",
    "\n",
    "step=10\n",
    "ax1.set_xticks(np.arange(noise_dB_zero[0],noise_dB_zero[-1]+step,step))\n",
    "ax1.set_yticks([0,0.05,0.1])\n",
    "\n",
    "line1 = ax3.plot(noise_dB_zero,ssim_ifft, '-')\n",
    "line2 = ax3.plot(noise_dB_zero,ssim_cs, '-')\n",
    "line3 = ax3.plot(noise_dB_zero,ssim_amap, '-')\n",
    "line4 = ax3.plot(noise_dB_zero,ssim_unroll, '-')\n",
    "#ax3.set(title='Comparison of Reconstruction Methods, R='+str(R),\n",
    "ax3.set(ylabel='SSIM',\n",
    "xlabel='Input SNR (dB)')\n",
    "#ax1.set_xscale('log')\n",
    "ax3.legend(['Zero-filled', 'CS', 'AUTOMAP', 'Unrolled AI'])\n",
    "#ax1.legend([line1, line2, line3], ['label1', 'label2', 'label3'])\n",
    "#plt.ylim(0,0.085)\n",
    "#plt.xlim(55,90)\n",
    "\n",
    "fill1 = ax3.fill_between(noise_dB_zero,ssim_ifft-ssim_ifft_sd,ssim_ifft+ssim_ifft_sd,alpha=0.25)\n",
    "fill2 = ax3.fill_between(noise_dB_zero,ssim_cs-ssim_cs_sd,ssim_cs+ssim_cs_sd,alpha=0.25)\n",
    "fill3 = ax3.fill_between(noise_dB_zero,ssim_amap-ssim_amap_sd,ssim_amap+ssim_amap_sd,alpha=0.25)\n",
    "fill4 = ax3.fill_between(noise_dB_zero,ssim_unroll-ssim_unroll_sd,ssim_unroll+ssim_unroll_sd,alpha=0.25)\n",
    "\n",
    "step=10\n",
    "ax3.set_xticks(np.arange(noise_dB_zero[0],noise_dB_zero[-1]+step,step))\n",
    "ax3.set_yticks([0.8,0.9,1])\n",
    "\n",
    "#plt.show()\n",
    "\n",
    "#fig.savefig('export_figs/fastmri_noise_curves_R'+str(R)+'.pdf',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99d79d88-6e17-4e5b-a9a7-24fa87a00828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e598e7c0-0568-4295-b7cb-6f0ce368d35a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Low SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "66119f54-77f1-423e-bfeb-58c90affa07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_no = 125\n",
    "kspTestScaled = ksp_fs_sc_all[:,:,:,:,test_no]\n",
    "volumeGTscaled = img_gt_sc_all[:,:,:,test_no]\n",
    "mask_metrics = np.ma.getmask(np.ma.masked_less(abs(volumeGTscaled),0.05)) # a mask in the image domain for calculating image metrics (not for masking kspace)\n",
    "\n",
    "l = 0\n",
    "noise_val = noise_dB[l]\n",
    "lamda_val = lamda_opt[l]\n",
    "iter_val = iter_opt[l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b1fb15d-d3ad-4ecf-b1b7-90fa1ba37d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4fdeff84-bbd0-4936-a7f8-a6365482b855",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n"
     ]
    }
   ],
   "source": [
    "kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "\n",
    "volcsrecon = ulfl1recon(uskspace,mask,lamda_val,iter_val,mps)\n",
    "    \n",
    "volifftrecon_mc = sp.ifft(uskspace,axes=[1,2,3])\n",
    "volifftrecon = coil_combine(volifftrecon_mc,mps)# np.sqrt(np.sum(np.abs(volifftrecon_mc)**2,axis=0)).reshape(imSize)\n",
    "\n",
    "amap_scale = 1\n",
    "unroll_scale = 1#0.25#1/8\n",
    "volunrollrecon = unrolling_fns.unrollingRecon(uskspace*unroll_scale,mask,unroll_model_pth)/unroll_scale\n",
    "#volunrollrecon = np.roll(volunrollrecon,(-1,-1),axis=(0,2))\n",
    "volamaprecon, volamaprecon_mc = automap_fns.automapRecon(uskspace*amap_scale,mps,model_real_dir,model_imag_dir)\n",
    "volamaprecon = volamaprecon/amap_scale\n",
    "volamaprecon_mc = volamaprecon_mc/amap_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dc9a3832-04f8-46f2-b511-39b60d70a9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # calculating SNR of input kspace (fully sampled)\n",
    "# fs_recon = sp.ifft(kspInput,axes=[1,2,3])\n",
    "# im_slice = fs_recon[0,:,32,:]\n",
    "# fig=plt.figure()\n",
    "# plt.imshow((abs(im_slice)))\n",
    "# noise = np.std(np.abs(im_slice[0:8,10:20]))\n",
    "# signal = np.mean(np.abs(im_slice[20:40,0:10]))\n",
    "# snr=signal/noise\n",
    "# print('SNR')\n",
    "# snr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f93965b-45c9-4e31-85a5-e011984bba85",
   "metadata": {},
   "source": [
    "### Background Masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8ad5671-5d8a-4190-b7b6-130f380b995f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'trained_models/AUTOMAP/train_25x75_fmri-hcp_r2_real_noise_bkgd_20240318/checkpoint/model.keras'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_real_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "736ff93b-cd11-4f9f-a252-a31fcd8a88f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "volumeGTscaledmasked = np.copy(volumeGTscaled[:,:,1:])\n",
    "volumeGTscaledmasked[mask_metrics[:,:,1:]]=0\n",
    "vol1 = np.copy(volifftrecon[:,:,1:])#np.ma.masked_array(volifftrecon, mask = mask_metrics)\n",
    "vol1[mask_metrics[:,:,1:]]=0\n",
    "vol1title = 'Zero-filled IFFT\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol2 = np.copy(volcsrecon[:,:,1:])\n",
    "vol2[mask_metrics[:,:,1:]]=0\n",
    "vol2title = 'Compressed Sensing\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol3 = np.copy(volamaprecon[:,:,1:])\n",
    "vol3[mask_metrics[:,:,1:]]=0\n",
    "vol3title = 'AUTOMAP\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol4 = np.copy(volunrollrecon[:,:,1:])\n",
    "vol4[mask_metrics[:,:,1:]]=0\n",
    "vol4title = 'Unrolling\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "\n",
    "vol1_low_R2 = vol1\n",
    "vol2_low_R2 = vol2\n",
    "vol3_low_R2 = vol3\n",
    "vol4_low_R2 = vol4\n",
    "\n",
    "#slc=10\n",
    "#fig_comp = display_fns.compareFourPlot(volumeGTscaledmasked,vol1,vol1title,vol2,vol2title,vol3,vol3title,vol4,vol4title,slc, scale_err=3)\n",
    "#fig_comp.savefig('export_figs/fastmri_imgs_masked_snr'+str(noise_val)+'dB.pdf',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b09322-96ee-4454-aec3-e522e9b544d8",
   "metadata": {},
   "source": [
    "## High SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f6f43b5-3ad3-4e8e-907e-d7163156a28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_no=125\n",
    "kspTestScaled = ksp_fs_sc_all[:,:,:,:,test_no]\n",
    "volumeGTscaled = img_gt_sc_all[:,:,:,test_no]\n",
    "mask_metrics = np.ma.getmask(np.ma.masked_less(abs(volumeGTscaled),0.05)) # a mask in the image domain for calculating image metrics (not for masking kspace)\n",
    "\n",
    "l=-1\n",
    "noise_val = noise_dB[l]\n",
    "lamda_val = lamda_opt[l]\n",
    "iter_val = iter_opt[l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d88729a5-950a-4957-a6e9-dcdab66dbb2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eacadaf5-1fa8-430e-8c52-7d19ac04fd09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n"
     ]
    }
   ],
   "source": [
    "kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "volcsrecon = ulfl1recon(uskspace,mask,lamda_val,iter_val,mps)\n",
    "    \n",
    "volifftrecon_mc = sp.ifft(uskspace,axes=[1,2,3])\n",
    "volifftrecon = coil_combine(volifftrecon_mc,mps)# np.sqrt(np.sum(np.abs(volifftrecon_mc)**2,axis=0)).reshape(imSize)\n",
    "\n",
    "amap_scale = 1\n",
    "unroll_scale = 1#0.25#1/8\n",
    "volunrollrecon = unrolling_fns.unrollingRecon(uskspace*unroll_scale,mask,unroll_model_pth)/unroll_scale\n",
    "#volunrollrecon = np.roll(volunrollrecon,(-1,-1),axis=(0,2))\n",
    "volamaprecon, volamaprecon_mc = automap_fns.automapRecon(uskspace*amap_scale,mps,model_real_dir,model_imag_dir)\n",
    "volamaprecon = volamaprecon/amap_scale\n",
    "volamaprecon_mc = volamaprecon_mc/amap_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7911cd70-92fb-44a5-9bcb-45a288af797d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating SNR of input kspace (fully sampled)\n",
    "# fs_recon = sp.ifft(kspInput,axes=[1,2,3])\n",
    "# im_slice = fs_recon[0,:,32,:]\n",
    "# fig=plt.figure()\n",
    "# plt.imshow((abs(im_slice)))\n",
    "# noise = np.std(np.abs(im_slice[0:8,10:20]))\n",
    "# signal = np.mean(np.abs(im_slice[20:40,0:10]))\n",
    "# snr=signal/noise\n",
    "# print('SNR')\n",
    "# snr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063a3189-21af-4290-ba73-43ac8803f0ef",
   "metadata": {},
   "source": [
    "### Background Masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8d480b98-211a-4485-8e9b-a95cc3bbac81",
   "metadata": {},
   "outputs": [],
   "source": [
    "volumeGTscaledmasked = np.copy(volumeGTscaled[:,:,1:])\n",
    "volumeGTscaledmasked[mask_metrics[:,:,1:]]=0\n",
    "vol1 = np.copy(volifftrecon[:,:,1:])#np.ma.masked_array(volifftrecon, mask = mask_metrics)\n",
    "vol1[mask_metrics[:,:,1:]]=0\n",
    "vol1title = 'Zero-filled IFFT\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol2 = np.copy(volcsrecon[:,:,1:])\n",
    "vol2[mask_metrics[:,:,1:]]=0\n",
    "vol2title = 'Compressed Sensing\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol3 = np.copy(volamaprecon[:,:,1:])\n",
    "vol3[mask_metrics[:,:,1:]]=0\n",
    "vol3title = 'AUTOMAP\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol4 = np.copy(volunrollrecon[:,:,1:])\n",
    "vol4[mask_metrics[:,:,1:]]=0\n",
    "vol4title = 'Unrolling\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "\n",
    "vol1_high_R2 = vol1\n",
    "vol2_high_R2 = vol2\n",
    "vol3_high_R2 = vol3\n",
    "vol4_high_R2 = vol4\n",
    "\n",
    "#slc=10\n",
    "#fig_comp = display_fns.compareFourPlot(volumeGTscaledmasked,vol1,vol1title,vol2,vol2title,vol3,vol3title,vol4,vol4title,slc, scale_err=3)\n",
    "\n",
    "#fig_comp.savefig('export_figs/fastmri_imgs_masked_snr'+str(noise_val)+'dB.pdf',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bbaec820-99a7-4c37-a904-6c674aaa80ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "GT_R2 = volumeGTscaledmasked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c910358c-0403-4c20-81f0-222306101b7d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#loss_fn = 'perceptual'\n",
    "loss_fn = 'mse'\n",
    "# defining mask\n",
    "R=4\n",
    "calib_size=10\n",
    "in_seed = 0\n",
    "imSize = img_gt_sc_all[:,:,:,0].shape\n",
    "mask = poissonDiscSigpy(imSize,R, in_seed,calib_size)\n",
    "mask_R4 = mask\n",
    "\n",
    "mps = np.ones(ksp_fs_sc_all[:,:,:,:,0].shape,dtype=complex) #single coil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3e9c1074-1889-459a-a72c-cccc6b223e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "if R==2:\n",
    "    unroll_model_pth = 'trained_models/Unrolling/Unroll_mse_R2.pth'\n",
    "    model_real_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r2_real_noise_bkgd_20240318/checkpoint/model.keras' #fast mri model\n",
    "    model_imag_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r2_imag_noise_bkgd_20240318/checkpoint/model.keras'\n",
    "    \n",
    "elif R==4:\n",
    "    unroll_model_pth = 'trained_models/Unrolling/Unroll_mse_R4.pth'\n",
    "    model_real_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r4_real_noise_bkgd_20240328/checkpoint/model.keras' #fast mri model\n",
    "    model_imag_dir = 'trained_models/AUTOMAP/train_25x75_fmri-hcp_r4_imag_noise_bkgd_20240328/checkpoint/model.keras'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eea0694a-7f96-4d15-9576-bb888cb6fd98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mdic = {\"mask\":mask}\n",
    "#savemat(\"mask_R2_PD.mat\",mdic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "94a2fd6c-a346-4d59-a203-458942db32fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lamda_opt_vals = np.zeros((noise_dB.size,n_ex_cs))\n",
    "iter_opt_vals = np.zeros((noise_dB.size,n_ex_cs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5f52701d-0eed-4d35-be78-70ab1928ec57",
   "metadata": {},
   "outputs": [],
   "source": [
    "for j, noise_val in enumerate(noise_dB):\n",
    "    for k in range(n_ex_cs):\n",
    "        kspTestScaled = ksp_fs_sc_all[:,:,:,:,k]\n",
    "        volumeGTscaled = img_gt_sc_all[:,:,:,k]\n",
    "        \n",
    "        kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "        uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "        lamda_opt_vals[j,k] = find_lamda_mask(uskspace, volumeGTscaled, mps,calib_size, show_plot=False)\n",
    "        iter_opt_vals[j,k] = find_iter_mask(uskspace,volumeGTscaled, lamda_opt_vals[j,k], mps, show_plot=False)\n",
    "\n",
    "lamda_opt = lamda_opt_vals.mean(axis=1)\n",
    "iter_opt = iter_opt_vals.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5a44fa6f-af31-4405-bd15-b4fe36570e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "nrmse_cs_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "nrmse_ifft_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "nrmse_amap_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "nrmse_unroll_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "\n",
    "ssim_cs_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "ssim_ifft_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "ssim_amap_vals = np.zeros((noise_dB.size,n_ex_test))\n",
    "ssim_unroll_vals = np.zeros((noise_dB.size,n_ex_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6fee2302-1c08-494c-a2a5-86c1d204bb27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n",
      "(1, 75, 64, 25)\n"
     ]
    }
   ],
   "source": [
    "for j, noise_val in enumerate(noise_dB):\n",
    "    for k in range(n_ex_test):\n",
    "        kspTestScaled = ksp_fs_sc_all[:,:,:,:,k+n_ex_cs]\n",
    "        volumeGTscaled = img_gt_sc_all[:,:,:,k+n_ex_cs]\n",
    "        \n",
    "        kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "        uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "    \n",
    "        volifftrecon_mc = sp.ifft(uskspace,axes=[1,2,3])\n",
    "        volifftrecon = coil_combine(volifftrecon_mc,mps)# np.sqrt(np.sum(np.abs(volifftrecon_mc)**2,axis=0)).reshape(imSize)\n",
    "\n",
    "        volcsrecon = ulfl1recon(uskspace,mask,lamda_opt[j],iter_opt[j],mps)\n",
    "\n",
    "        amap_scale = 1\n",
    "        unroll_scale = 1#1/8\n",
    "        volunrollrecon = unrolling_fns.unrollingRecon(uskspace*unroll_scale,mask,unroll_model_pth)/unroll_scale\n",
    "        #volunrollrecon = np.roll(volunrollrecon,(1,1),axis=(0,2))\n",
    "        volamaprecon, volamaprecon_mc = automap_fns.automapRecon(uskspace*amap_scale,mps,model_real_dir,model_imag_dir)\n",
    "        volamaprecon = volamaprecon/amap_scale\n",
    "        volamaprecon_mc = volamaprecon_mc/amap_scale\n",
    "\n",
    "        mask_metrics = np.ma.getmask(np.ma.masked_less(abs(volumeGTscaled),0.05)) # a mask in the image domain for calculating image metrics (not for masking kspace)\n",
    "\n",
    "        volumeGTscaled[mask_metrics]=0\n",
    "        volcsrecon[mask_metrics]=0\n",
    "        volifftrecon[mask_metrics]=0\n",
    "        volamaprecon[mask_metrics]=0\n",
    "        volunrollrecon[mask_metrics]=0\n",
    "\n",
    "        nrmse_cs_vals[j,k] = nrmse(np.abs(volcsrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        nrmse_ifft_vals[j,k] = nrmse(np.abs(volifftrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        nrmse_amap_vals[j,k] = nrmse(np.abs(volamaprecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        nrmse_unroll_vals[j,k] = nrmse(np.abs(volunrollrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "\n",
    "        ssim_cs_vals[j,k] = ssim(np.abs(volcsrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        ssim_ifft_vals[j,k] = ssim(np.abs(volifftrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        ssim_amap_vals[j,k] = ssim(np.abs(volamaprecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "        ssim_unroll_vals[j,k] = ssim(np.abs(volunrollrecon[:,:,1:]),np.abs(volumeGTscaled[:,:,1:]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ae0fcd8a-94e7-4809-aa33-c0ff72dc3c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "nrmse_cs = nrmse_cs_vals.mean(axis=1)\n",
    "nrmse_ifft = nrmse_ifft_vals.mean(axis=1)\n",
    "nrmse_amap = nrmse_amap_vals.mean(axis=1)\n",
    "nrmse_unroll = nrmse_unroll_vals.mean(axis=1)\n",
    "\n",
    "nrmse_cs_sd = nrmse_cs_vals.std(axis=1)\n",
    "nrmse_ifft_sd = nrmse_ifft_vals.std(axis=1)\n",
    "nrmse_amap_sd = nrmse_amap_vals.std(axis=1)\n",
    "nrmse_unroll_sd = nrmse_unroll_vals.std(axis=1)\n",
    "\n",
    "ssim_cs = ssim_cs_vals.mean(axis=1)\n",
    "ssim_ifft = ssim_ifft_vals.mean(axis=1)\n",
    "ssim_amap = ssim_amap_vals.mean(axis=1)\n",
    "ssim_unroll = ssim_unroll_vals.mean(axis=1)\n",
    "\n",
    "ssim_cs_sd = ssim_cs_vals.std(axis=1)\n",
    "ssim_ifft_sd = ssim_ifft_vals.std(axis=1)\n",
    "ssim_amap_sd = ssim_amap_vals.std(axis=1)\n",
    "ssim_unroll_sd = ssim_unroll_vals.std(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "52d8cf35-6f6a-4e27-be4b-3ad32dfbb6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ax2.clear()\n",
    "#ax4.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "698b8574-0f19-41a5-8133-b04b5632bcdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line1 = ax2.plot(noise_dB_zero,nrmse_ifft, '-')\n",
    "line2 = ax2.plot(noise_dB_zero,nrmse_cs, '-')\n",
    "line3 = ax2.plot(noise_dB_zero,nrmse_amap, '-')\n",
    "line4 = ax2.plot(noise_dB_zero,nrmse_unroll, '-')\n",
    "#ax2.set(title='Comparison of Reconstruction Methods, R='+str(R),\n",
    "ax2.set(ylabel='NRMSE',\n",
    "xlabel='Input SNR (dB)')\n",
    "#ax2.set_xscale('log')\n",
    "ax2.legend(['Zero-filled', 'CS', 'AUTOMAP', 'Unrolled'])\n",
    "#ax2.legend([line1, line2, line3], ['label1', 'label2', 'label3'])\n",
    "#plt.ylim(0,0.085)\n",
    "#plt.xlim(55,90)\n",
    "fill1 = ax2.fill_between(noise_dB_zero,nrmse_ifft-nrmse_ifft_sd,nrmse_ifft+nrmse_ifft_sd,alpha=0.25)\n",
    "fill2 = ax2.fill_between(noise_dB_zero,nrmse_cs-nrmse_cs_sd,nrmse_cs+nrmse_cs_sd,alpha=0.25)\n",
    "fill3 = ax2.fill_between(noise_dB_zero,nrmse_amap-nrmse_amap_sd,nrmse_amap+nrmse_amap_sd,alpha=0.25)\n",
    "fill4 = ax2.fill_between(noise_dB_zero,nrmse_unroll-nrmse_unroll_sd,nrmse_unroll+nrmse_unroll_sd,alpha=0.25)\n",
    "\n",
    "step=10\n",
    "ax2.set_xticks(np.arange(noise_dB_zero[0],noise_dB_zero[-1]+step,step))\n",
    "ax2.set_yticks([0,0.05,0.1])\n",
    "\n",
    "line1 = ax4.plot(noise_dB_zero,ssim_ifft, '-')\n",
    "line2 = ax4.plot(noise_dB_zero,ssim_cs, '-')\n",
    "line3 = ax4.plot(noise_dB_zero,ssim_amap, '-')\n",
    "line4 = ax4.plot(noise_dB_zero,ssim_unroll, '-')\n",
    "#ax4.set(title='Comparison of Reconstruction Methods, R='+str(R),\n",
    "ax4.set(ylabel='SSIM',\n",
    "xlabel='Input SNR (dB)')\n",
    "#ax2.set_xscale('log')\n",
    "ax4.legend(['Zero-filled', 'CS', 'AUTOMAP', 'Unrolled AI'])\n",
    "#ax2.legend([line1, line2, line3], ['label1', 'label2', 'label3'])\n",
    "#plt.ylim(0,0.085)\n",
    "#plt.xlim(55,90)\n",
    "\n",
    "fill1 = ax4.fill_between(noise_dB_zero,ssim_ifft-ssim_ifft_sd,ssim_ifft+ssim_ifft_sd,alpha=0.25)\n",
    "fill2 = ax4.fill_between(noise_dB_zero,ssim_cs-ssim_cs_sd,ssim_cs+ssim_cs_sd,alpha=0.25)\n",
    "fill3 = ax4.fill_between(noise_dB_zero,ssim_amap-ssim_amap_sd,ssim_amap+ssim_amap_sd,alpha=0.25)\n",
    "fill4 = ax4.fill_between(noise_dB_zero,ssim_unroll-ssim_unroll_sd,ssim_unroll+ssim_unroll_sd,alpha=0.25)\n",
    "\n",
    "step=10\n",
    "ax4.set_xticks(np.arange(noise_dB_zero[0],noise_dB_zero[-1]+step,step))\n",
    "ax4.set_yticks([0.8,0.9,1])\n",
    "\n",
    "ax1.set_title('(a) R = 2 Subsampling',x=0,y=1.04)\n",
    "ax2.set_title('(b) R = 4 Subsampling',x=0,y=1.04)\n",
    "\n",
    "for ax in [ax1,ax2]:\n",
    "    #ax.vlines(23,0,0.1,color='tab:gray',linestyle='--')\n",
    "    ax.set_ylim(0,0.1)\n",
    "    #ax.set_xlim(10,50)\n",
    "\n",
    "for ax in [ax3,ax4]:\n",
    "    #ax.vlines(23,0,1,color='tab:gray',linestyle='--')\n",
    "    ax.set_ylim(0.7,1)\n",
    "    #ax.set_xlim(10,50)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig\n",
    "#plt.show()\n",
    "\n",
    "#fig.savefig('export_figs/fastmri_noise_curves_all.pdf',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a146114-7e9a-4eaa-8f5b-123e85f1b208",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Low SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "93e1176e-f943-4ae4-b657-74e5b3cb33ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_no = 125\n",
    "kspTestScaled = ksp_fs_sc_all[:,:,:,:,test_no]\n",
    "volumeGTscaled = img_gt_sc_all[:,:,:,test_no]\n",
    "mask_metrics = np.ma.getmask(np.ma.masked_less(abs(volumeGTscaled),0.05)) # a mask in the image domain for calculating image metrics (not for masking kspace)\n",
    "\n",
    "l = 0\n",
    "noise_val = noise_dB[l]\n",
    "lamda_val = lamda_opt[l]\n",
    "iter_val = iter_opt[l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3df5538e-a755-4cd8-890a-490d804f5648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "83949ba8-94e9-4ec0-9e30-dc08da57a195",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n"
     ]
    }
   ],
   "source": [
    "kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "\n",
    "volcsrecon = ulfl1recon(uskspace,mask,lamda_val,iter_val,mps)\n",
    "    \n",
    "volifftrecon_mc = sp.ifft(uskspace,axes=[1,2,3])\n",
    "volifftrecon = coil_combine(volifftrecon_mc,mps)# np.sqrt(np.sum(np.abs(volifftrecon_mc)**2,axis=0)).reshape(imSize)\n",
    "\n",
    "amap_scale = 1\n",
    "unroll_scale = 1#0.25#1/8\n",
    "volunrollrecon = unrolling_fns.unrollingRecon(uskspace*unroll_scale,mask,unroll_model_pth)/unroll_scale\n",
    "#volunrollrecon = np.roll(volunrollrecon,(-1,-1),axis=(0,2))\n",
    "volamaprecon, volamaprecon_mc = automap_fns.automapRecon(uskspace*amap_scale,mps,model_real_dir,model_imag_dir)\n",
    "volamaprecon = volamaprecon/amap_scale\n",
    "volamaprecon_mc = volamaprecon_mc/amap_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "413a4b82-096d-456b-aefb-bee3d65b0690",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # calculating SNR of input kspace (fully sampled)\n",
    "# fs_recon = sp.ifft(kspInput,axes=[1,2,3])\n",
    "# im_slice = fs_recon[0,:,32,:]\n",
    "# fig=plt.figure()\n",
    "# plt.imshow((abs(im_slice)))\n",
    "# noise = np.std(np.abs(im_slice[0:8,10:20]))\n",
    "# signal = np.mean(np.abs(im_slice[20:40,0:10]))\n",
    "# snr=signal/noise\n",
    "# print('SNR')\n",
    "# snr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15523123-e3ac-456c-9d97-39bdc0526b87",
   "metadata": {},
   "source": [
    "### Background Masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0579a994-8e54-4caf-a452-712d52887a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'trained_models/AUTOMAP/train_25x75_fmri-hcp_r4_real_noise_bkgd_20240328/checkpoint/model.keras'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_real_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "951a5cfb-ac23-413c-843c-485f63877f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "volumeGTscaledmasked = np.copy(volumeGTscaled[:,:,1:])\n",
    "volumeGTscaledmasked[mask_metrics[:,:,1:]]=0\n",
    "vol1 = np.copy(volifftrecon[:,:,1:])#np.ma.masked_array(volifftrecon, mask = mask_metrics)\n",
    "vol1[mask_metrics[:,:,1:]]=0\n",
    "vol1title = 'Zero-filled IFFT\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol2 = np.copy(volcsrecon[:,:,1:])\n",
    "vol2[mask_metrics[:,:,1:]]=0\n",
    "vol2title = 'Compressed Sensing\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol3 = np.copy(volamaprecon[:,:,1:])\n",
    "vol3[mask_metrics[:,:,1:]]=0\n",
    "vol3title = 'AUTOMAP\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol4 = np.copy(volunrollrecon[:,:,1:])\n",
    "vol4[mask_metrics[:,:,1:]]=0\n",
    "vol4title = 'Unrolling\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "\n",
    "vol1_low_R4 = vol1\n",
    "vol2_low_R4 = vol2\n",
    "vol3_low_R4 = vol3\n",
    "vol4_low_R4 = vol4\n",
    "\n",
    "#slc=10\n",
    "#fig_comp = display_fns.compareFourPlot(volumeGTscaledmasked,vol1,vol1title,vol2,vol2title,vol3,vol3title,vol4,vol4title,slc, scale_err=3)\n",
    "#fig_comp.savefig('export_figs/fastmri_imgs_masked_snr'+str(noise_val)+'dB.pdf',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e981321c-2d83-4c0f-95d7-c0e08135b49f",
   "metadata": {},
   "source": [
    "## High SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "06ed8a1d-c488-4aaa-903d-4746e1d7858c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_no=125\n",
    "kspTestScaled = ksp_fs_sc_all[:,:,:,:,test_no]\n",
    "volumeGTscaled = img_gt_sc_all[:,:,:,test_no]\n",
    "mask_metrics = np.ma.getmask(np.ma.masked_less(abs(volumeGTscaled),0.05)) # a mask in the image domain for calculating image metrics (not for masking kspace)\n",
    "\n",
    "l=-1\n",
    "noise_val = noise_dB[l]\n",
    "lamda_val = lamda_opt[l]\n",
    "iter_val = iter_opt[l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "364db9e8-693f-4055-a748-d78cee421c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fd865459-851a-491e-8242-bd0bacf8f3e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 75, 64, 25)\n"
     ]
    }
   ],
   "source": [
    "kspInput = awgn(kspTestScaled,noise_val,L=1)\n",
    "uskspace = applyMask(kspInput,mask)\n",
    "\n",
    "volcsrecon = ulfl1recon(uskspace,mask,lamda_val,iter_val,mps)\n",
    "    \n",
    "volifftrecon_mc = sp.ifft(uskspace,axes=[1,2,3])\n",
    "volifftrecon = coil_combine(volifftrecon_mc,mps)# np.sqrt(np.sum(np.abs(volifftrecon_mc)**2,axis=0)).reshape(imSize)\n",
    "\n",
    "amap_scale = 1\n",
    "unroll_scale = 1#0.25#1/8\n",
    "volunrollrecon = unrolling_fns.unrollingRecon(uskspace*unroll_scale,mask,unroll_model_pth)/unroll_scale\n",
    "#volunrollrecon = np.roll(volunrollrecon,(-1,-1),axis=(0,2))\n",
    "volamaprecon, volamaprecon_mc = automap_fns.automapRecon(uskspace*amap_scale,mps,model_real_dir,model_imag_dir)\n",
    "volamaprecon = volamaprecon/amap_scale\n",
    "volamaprecon_mc = volamaprecon_mc/amap_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0fcc6a98-0a25-498f-a4e1-634f0fbd9dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating SNR of input kspace (fully sampled)\n",
    "# fs_recon = sp.ifft(kspInput,axes=[1,2,3])\n",
    "# im_slice = fs_recon[0,:,32,:]\n",
    "# fig=plt.figure()\n",
    "# plt.imshow((abs(im_slice)))\n",
    "# noise = np.std(np.abs(im_slice[0:8,10:20]))\n",
    "# signal = np.mean(np.abs(im_slice[20:40,0:10]))\n",
    "# snr=signal/noise\n",
    "# print('SNR')\n",
    "# snr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34606b09-0b5a-41d0-8415-90ada9e130af",
   "metadata": {},
   "source": [
    "### Background Masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "abe82658-e504-4ab9-92c7-dc63247c6ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "volumeGTscaledmasked = np.copy(volumeGTscaled[:,:,1:])\n",
    "volumeGTscaledmasked[mask_metrics[:,:,1:]]=0\n",
    "vol1 = np.copy(volifftrecon[:,:,1:])#np.ma.masked_array(volifftrecon, mask = mask_metrics)\n",
    "vol1[mask_metrics[:,:,1:]]=0\n",
    "vol1title = 'Zero-filled IFFT\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol2 = np.copy(volcsrecon[:,:,1:])\n",
    "vol2[mask_metrics[:,:,1:]]=0\n",
    "vol2title = 'Compressed Sensing\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol3 = np.copy(volamaprecon[:,:,1:])\n",
    "vol3[mask_metrics[:,:,1:]]=0\n",
    "vol3title = 'AUTOMAP\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "vol4 = np.copy(volunrollrecon[:,:,1:])\n",
    "vol4[mask_metrics[:,:,1:]]=0\n",
    "vol4title = 'Unrolling\\nInp. SNR='+str(noise_val)+', R = '+str(R)\n",
    "\n",
    "vol1_high_R4 = vol1\n",
    "vol2_high_R4 = vol2\n",
    "vol3_high_R4 = vol3\n",
    "vol4_high_R4 = vol4\n",
    "\n",
    "#slc=10\n",
    "#fig_comp = display_fns.compareFourPlot(volumeGTscaledmasked,vol1,vol1title,vol2,vol2title,vol3,vol3title,vol4,vol4title,slc, scale_err=3)\n",
    "\n",
    "#fig_comp.savefig('export_figs/fastmri_imgs_masked_snr'+str(noise_val)+'dB.pdf',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c7980f3-a284-484b-8371-d2aa3ac2d2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "GT_R4 = volumeGTscaledmasked"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f321aa4-3e6e-4e61-a397-13ac76d93766",
   "metadata": {},
   "source": [
    "## Comparing images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0a7b2124-a633-4940-9ac0-532eab9a89fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "slc=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1e850bdb-0d08-4e37-9ef9-9b46a4cb1c2d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 850x500 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " vol1title = 'Zero-filled'\n",
    "# vol2 = np.copy(volcsrecon[:,:,1:])\n",
    " #vol2[mask_metrics[:,:,1:]]=0\n",
    " vol2title = 'Comp. Sensing'\n",
    "# vol3 = np.copy(volamaprecon[:,:,1:])\n",
    "# #vol3[mask_metrics[:,:,1:]]=0\n",
    " vol3title = 'AUTOMAP'\n",
    "# vol4 = np.copy(volunrollrecon[:,:,1:])\n",
    "# #vol4[mask_metrics[:,:,1:]]=0\n",
    " vol4title = 'Unrolled AI'\n",
    "\n",
    "font_lw = 500\n",
    "\n",
    "#    GT_R2 = np.swapaxes(GT_R2,0,1)\n",
    "#    vol1 = np.swapaxes(vol1,0,1)\n",
    "#    vol2 = np.swapaxes(vol2,0,1)\n",
    "#    vol3 = np.swapaxes(vol3,0,1)\n",
    "#    vol4 = np.swapaxes(vol4,0,1)\n",
    "\n",
    "# RO 64, 2.5 mm\n",
    "# PE1 75, 3.5mm\n",
    "# PE2 15, 8 mm\n",
    "asp_ratio = (GT_R2.shape[1]*3.5)/(GT_R2.shape[0]*2.5)\n",
    "\n",
    "#plt.style.use('default')\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "figimgs, axs = plt.subplots(4, 5,figsize = (8.5,5))\n",
    "\n",
    "figimgs.subplots_adjust(hspace=1.4)\n",
    "figimgs.subplots_adjust(wspace=-0.6)\n",
    "\n",
    "max_GT_R2 = np.amax(np.abs(GT_R2[:,:,slc]))\n",
    "\n",
    "im = axs[0, 0].imshow(np.abs(GT_R2[:,:,slc].T), cmap= 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "\n",
    "axs[0, 0].set_xticks([])\n",
    "axs[0, 0].set_yticks([])\n",
    "axs[0, 0].set_aspect(asp_ratio)\n",
    "axs[0, 0].set_xlabel('PE1')\n",
    "axs[0, 0].set_ylabel('RO')\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "\n",
    "im = axs[0, 1].imshow(np.abs(vol1_high_R2[:,:,slc].T), cmap = 'gray', vmin = 0,vmax = 1.05*max_GT_R2)\n",
    "axs[0, 1].set_title(vol1title,fontsize=10, fontweight=font_lw)\n",
    "axs[0, 1].set_xticks([])\n",
    "axs[0, 1].set_yticks([])\n",
    "axs[0, 1].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[0, 1].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol1_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol1_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "axs[0, 1].set_ylabel('SNR '+str(noise_dB_zero[-1])+' dB',fontsize=10, fontweight=font_lw)\n",
    "\n",
    "im = axs[1, 1].imshow(np.abs(vol1_low_R2[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "#axs[1, 1].set_title(vol1title + ' Diff')\n",
    "axs[1, 1].set_xticks([])\n",
    "axs[1, 1].set_yticks([])\n",
    "axs[1, 1].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[1, 1].set_xlabel('SSIM ='+str(round(ssim(np.abs(vol1_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE ='+str(round(nrmse(np.abs(vol1_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "axs[1, 1].set_ylabel('SNR '+str(noise_dB_zero[0])+' dB',fontsize=10, fontweight=font_lw)\n",
    "\n",
    "\n",
    "im = axs[0, 2].imshow(np.abs(vol2_high_R2[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "axs[0, 2].set_title(vol2title, fontsize=10, fontweight=font_lw)\n",
    "axs[0, 2].set_xticks([])\n",
    "axs[0, 2].set_yticks([])\n",
    "axs[0, 2].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[0, 2].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol2_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol2_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[1, 2].imshow(np.abs(vol2_low_R2[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "#axs[1, 2].set_title(vol2title + ' Diff')\n",
    "axs[1, 2].set_xticks([])\n",
    "axs[1, 2].set_yticks([])\n",
    "axs[1, 2].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[1, 2].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol2_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol2_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[0, 3].imshow(np.abs(vol3_high_R2[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "axs[0, 3].set_title(vol3title, fontsize=10, fontweight=font_lw)\n",
    "axs[0, 3].set_xticks([])\n",
    "axs[0, 3].set_yticks([])\n",
    "axs[0, 3].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[0, 3].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol3_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol3_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[1, 3].imshow(np.abs(vol3_low_R2[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "#axs[1, 3].set_title(vol3title + ' Diff')\n",
    "axs[1, 3].set_xticks([])\n",
    "axs[1, 3].set_yticks([])\n",
    "axs[1, 3].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[1, 3].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol3_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol3_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[0, 4].imshow(np.abs(vol4_high_R2[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "axs[0, 4].set_title(vol4title, fontsize=10, fontweight=font_lw)\n",
    "axs[0, 4].set_xticks([])\n",
    "axs[0, 4].set_yticks([])\n",
    "axs[0, 4].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[0, 4].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol4_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol4_high_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[1, 4].imshow(np.abs(vol4_low_R2[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R2)\n",
    "#axs[1, 4].set_title(vol4title + ' Diff')\n",
    "axs[1, 4].set_xticks([])\n",
    "axs[1, 4].set_yticks([])\n",
    "axs[1, 4].set_aspect(asp_ratio)\n",
    "im.set_clim(0, (1.05*max_GT_R2))\n",
    "axs[1, 4].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol4_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol4_low_R2[:,:,:]),np.abs(GT_R2[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "\n",
    "im = axs[1,0].imshow(np.abs(mask_R2.T),cmap='gray')\n",
    "axs[1, 0].set_xticks([])\n",
    "axs[1, 0].set_yticks([])\n",
    "axs[1, 0].set_xlabel('PE1')\n",
    "axs[1, 0].set_ylabel('PE2')\n",
    "axs[1, 0].set_title('Mask',fontsize=10, fontweight=font_lw)\n",
    "#axs[1, 0].axis('off')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "max_GT_R4 = np.amax(np.abs(GT_R4[:,:,slc]))\n",
    "\n",
    "im = axs[2, 0].imshow(np.abs(GT_R4[:,:,slc].T), cmap= 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "\n",
    "axs[2, 0].set_xlabel('PE1')\n",
    "axs[2, 0].set_ylabel('RO')\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "\n",
    "im = axs[2, 1].imshow(np.abs(vol1_high_R4[:,:,slc].T), cmap = 'gray', vmin = 0,vmax = 1.05*max_GT_R4)\n",
    "axs[2, 1].set_title(vol1title,fontsize=10,fontweight=font_lw)\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[2, 1].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol1_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol1_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "axs[2, 1].set_ylabel('SNR '+str(noise_dB_zero[-1])+' dB',fontsize=10, fontweight=font_lw)\n",
    "\n",
    "im = axs[3, 1].imshow(np.abs(vol1_low_R4[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "#axs[3, 1].set_title(vol1title + ' Diff')\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[3, 1].set_xlabel('SSIM ='+str(round(ssim(np.abs(vol1_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE ='+str(round(nrmse(np.abs(vol1_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "axs[3, 1].set_ylabel('SNR '+str(noise_dB_zero[0])+' dB',fontsize=10, fontweight=font_lw)\n",
    "\n",
    "\n",
    "im = axs[2, 2].imshow(np.abs(vol2_high_R4[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "axs[2, 2].set_title(vol2title,fontsize=10,fontweight=font_lw)\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[2, 2].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol2_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol2_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[3, 2].imshow(np.abs(vol2_low_R4[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "#axs[3, 2].set_title(vol2title + ' Diff')\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[3, 2].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol2_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol2_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[2, 3].imshow(np.abs(vol3_high_R4[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "axs[2, 3].set_title(vol3title,fontsize=10,fontweight=font_lw)\n",
    "\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[2, 3].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol3_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol3_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[3, 3].imshow(np.abs(vol3_low_R4[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "#axs[3, 3].set_title(vol3title + ' Diff')\n",
    "\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[3, 3].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol3_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol3_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[2, 4].imshow(np.abs(vol4_high_R4[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "axs[2, 4].set_title(vol4title,fontsize=10,fontweight=font_lw)\n",
    "\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[2, 4].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol4_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol4_high_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "im = axs[3, 4].imshow(np.abs(vol4_low_R4[:,:,slc].T), cmap = 'gray', vmin = 0, vmax = 1.05*max_GT_R4)\n",
    "#axs[3, 4].set_title(vol4title + ' Diff')\n",
    "\n",
    "im.set_clim(0, (1.05*max_GT_R4))\n",
    "axs[3, 4].set_xlabel('SSIM = '+str(round(ssim(np.abs(vol4_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),2))\n",
    "                     +'\\n NRMSE = '+str(round(nrmse(np.abs(vol4_low_R4[:,:,:]),np.abs(GT_R4[:,:,:])),3)),fontsize=7)\n",
    "\n",
    "\n",
    "im = axs[3,0].imshow(np.abs(mask_R4.T),cmap='gray')\n",
    "axs[3, 0].set_xlabel('PE1')\n",
    "axs[3, 0].set_ylabel('PE2')\n",
    "axs[3, 0].set_title('Mask',fontsize=10,fontweight=font_lw)\n",
    "\n",
    "\n",
    "\n",
    "img_scale = 1.6\n",
    "\n",
    "for j in range(4):\n",
    "    for k in range(5):\n",
    "        l, b, w, h = axs[j,k].get_position().bounds\n",
    "        axs[j,k].set_position([l,b,w*img_scale,h*img_scale])\n",
    "        if j>1:\n",
    "            axs[j,k].set_position([l,b-0.15,w*img_scale,h*img_scale])\n",
    "\n",
    "col0shift=-0.05\n",
    "col_delta=-0.013\n",
    "\n",
    "axs[0, 0].set_title('Reference',fontsize=10, fontweight=font_lw)\n",
    "axs[2, 0].set_title('Reference',fontsize=10,fontweight=font_lw)\n",
    "\n",
    "l, b, w, h = axs[0,0].get_position().bounds\n",
    "axs[0, 0].set_position([l+col0shift+col_delta,b,w,h])\n",
    "\n",
    "l, b, w, h = axs[1,0].get_position().bounds\n",
    "axs[1, 0].set_position([l+col0shift,b,w,h])\n",
    "\n",
    "l, b, w2, h2 = axs[1,0].get_position().bounds\n",
    "axs[1,0].set_position([l,b,w2/img_scale,h2/img_scale])\n",
    "\n",
    "l, b, w, h = axs[2, 0].get_position().bounds\n",
    "axs[2, 0].set_position([l+col0shift+col_delta,b,w,h])\n",
    "\n",
    "l, b, w2, h2 = axs[3,0].get_position().bounds\n",
    "axs[3,0].set_position([l,b,w2/img_scale,h2/img_scale])\n",
    "\n",
    "l, b, w, h = axs[3,0].get_position().bounds\n",
    "axs[3, 0].set_position([l+col0shift,b,w,h])\n",
    "\n",
    "axs[1, 1].set_title('(a) $R = 2$ undersampling',x=-1.8,y=2.8,fontweight=font_lw)\n",
    "axs[3, 1].set_title('(b) $R = 4$ undersampling',x=-1.8,y=2.8,fontweight=font_lw)\n",
    "\n",
    "for ax in axs.flatten():\n",
    "\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_aspect(asp_ratio)\n",
    "    ax.tick_params(color='k')\n",
    "    if ax not in [axs[1,0],axs[3,0]]:\n",
    "        for spine in ax.spines.values():\n",
    "            spine.set_edgecolor('k')\n",
    "        ax.set_xlim([10,65])\n",
    "        ax.set_ylim([59,5])\n",
    "\n",
    "for ax in [axs[1,0],axs[3,0]]:\n",
    "    #for spine in ax.spines.values():\n",
    "    #    spine.set_edgecolor('w')\n",
    "    ax.set_xticks([0,75])\n",
    "    ax.set_yticks([0,25])\n",
    "    #ax.set_xlim([])\n",
    "    #ax.set_ylim([])\n",
    "\n",
    "#figimgs.savefig('export_figs/fastmri_imgs_compare_snr_R.pdf',bbox_inches='tight',dpi=300)"
   ]
  }
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