Merge pull request #3 from mjt320/develop

Develop

README.md

@@ -2,9 +2,9 @@
 
 **Please note: This library is also hosted in the [OSIPI DCE-DSC-MRI_CodeCollection repository](https://github.com/OSIPI/DCE-DSC-MRI_CodeCollection), where unit tests and perfusion code by other authors can also be found.**
 
-Python library for simulating and fitting DCE-MRI data. It permits arbitrary combinations of pulse sequence, pharmacokinetic model, water exchange model, etc. The code is a work-in-progress, has not been extensively tested and is not recommended or approved for use.
+Python library for simulating and fitting DCE-MRI data. It permits arbitrary combinations of pulse sequence, pharmacokinetic model, water exchange model, etc. The code is a work-in-progress, has not been extensively tested and is not recommended or approved for clinical use.
 
-Created 28 September 2020
+Created 28 September 2020  
 @authors: Michael Thrippleton  
 @email: [email protected]  
 @institution: University of Edinburgh, UK
@@ -14,24 +14,22 @@ Created 28 September 2020
 - Fit tissue concentration using pharmacokinetic model
 - Fit signal enhancement using pharmacokinetic model
 - Pharmacokinetic models: steady-state, Patlak, extended Tofts, Tofts, 2CXM, 2CUM
-- AIFs: patient-specific (measured), Parker, bi-exponential Parker
+- Patlak fitting with multiple linear regression
+- AIFs: including patient-specific (measured), Parker, bi-exponential Parker
 - Fitting free AIF time delay parameter
 - Relaxivity models: linear
 - Signal models: spoiled gradient echo
 - Water exchange models: FXL, NXL, NXL_be
 - T1 fitting using variable flip angle method, IR-SPGR and DESPOT1-HIFI
 
 ### Not yet implemented/limitations:
-- Generally untested. Not optimised for speed or robustness.
 - Additional pharmacokinetic models (add by inheriting from PkModel class)
 - Additional relaxivity models (add by inheriting from CRModel class)
 - Additional water exchange models, e.g. 3S2X, 2S1X (add by inheriting from WaterExModel class)
 - Additional signal models (add by inheriting from SignalModel class)
 - R2/R2* effects not included in fitting of enhancement curves (but is included for enhancement-to-concentration conversion)
 - Compartment-specific relaxivity parameters/models
 - Fitting free water exchange parameters
-- Special model implementations, e.g. linear and graphical versions of Patlak model
 
 ### TODO:
-- fast C calculation for SPGR with r2=0
-- inversion recovery T1 measurement
+- inversion recovery T1 measurment

demo/demo_conc_to_enh.ipynb

@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "3b2d4aa7-5b11-44fc-ad48-069caaeab47a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "sys.path.append('../src')\n",
+    "import dce_fit, relaxivity, signal_models, water_ex_models, aifs, pk_models\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "9439e538-cdd8-4849-bedf-1ead532f0811",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x22bbaed05e0>,\n",
+       " <matplotlib.lines.Line2D at 0x22bbaed0610>]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r1, r2 = 5.0, 0\n",
+    "tr, fa, te  = 3.4e-3, 15, 1.7e-3\n",
+    "t10 = 2\n",
+    "k_fa = 1.4\n",
+    "\n",
+    "enh = np.arange(3000)\n",
+    "\n",
+    "c_to_r_model = relaxivity.CRLinear(r1, r2)\n",
+    "signal_model = signal_models.SPGR(tr, fa, te)\n",
+    "e_to_c_num = dce_fit.EnhToConc(c_to_r_model, signal_model)\n",
+    "C_t_num = e_to_c_num.proc(enh, t10, k_fa)\n",
+    "\n",
+    "e_to_c_ana = dce_fit.EnhToConcSPGR(tr, fa, r1)\n",
+    "C_t_ana = e_to_c_ana.proc(enh, t10, k_fa)\n",
+    "\n",
+    "plt.plot(enh, C_t_num, 'k-',\n",
+    "         enh, C_t_ana, 'r-')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

demo/demo_fit_dce.ipynb

@@ -584,7 +584,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -598,7 +598,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,