diff --git a/.idea/SEPAL.iml b/.idea/SEPAL.iml
index d49a298..4606b2f 100644
--- a/.idea/SEPAL.iml
+++ b/.idea/SEPAL.iml
@@ -11,5 +11,6 @@
+
\ No newline at end of file
diff --git a/.idea/other.xml b/.idea/other.xml
new file mode 100644
index 0000000..640fd80
--- /dev/null
+++ b/.idea/other.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/demo/demo_aif_module.ipynb b/demo/demo_aif_module.ipynb
index 8fcecee..4a23fa7 100644
--- a/demo/demo_aif_module.ipynb
+++ b/demo/demo_aif_module.ipynb
@@ -3,7 +3,11 @@
{
"cell_type": "markdown",
"id": "eb332c7d-4589-47da-81d0-e2697ee70254",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"## AIF module demo"
]
@@ -11,7 +15,11 @@
{
"cell_type": "markdown",
"id": "4276273d-31f5-4625-bfc8-9018b7cd984d",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"### Import modules"
]
@@ -21,7 +29,10 @@
"execution_count": 2,
"id": "42671502-6096-4107-8c21-3f876b950a66",
"metadata": {
- "tags": []
+ "tags": [],
+ "pycharm": {
+ "name": "#%%\n"
+ }
},
"outputs": [
{
@@ -46,7 +57,11 @@
{
"cell_type": "markdown",
"id": "0c2eae34-da30-465b-9b23-3dbadd7dc0d8",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"### The AIF Class\n",
"AIF objects define an arterial input function. This can either be a population average function or an AIF based on individual patient measurements."
@@ -55,7 +70,11 @@
{
"cell_type": "markdown",
"id": "reported-projector",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"### Classic Parker AIF\n",
"Create a Parker AIF object of the Parker subclass of AIF:"
@@ -65,7 +84,11 @@
"cell_type": "code",
"execution_count": 3,
"id": "4f59a6a6-69b0-4f28-a4a1-03b0b846f6e8",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [],
"source": [
"parker_aif = aifs.Parker(hct=0.42)"
@@ -74,7 +97,11 @@
{
"cell_type": "markdown",
"id": "f9a0f8e5-ddd6-4164-a4df-7f50a3af82eb",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"We can use the c_ap method to get concentration at arbitrary time points:"
]
@@ -83,7 +110,11 @@
"cell_type": "code",
"execution_count": 4,
"id": "f65ca4cc-58d3-422a-822b-6c52cd9328c9",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [
{
"data": {
@@ -110,7 +141,11 @@
{
"cell_type": "markdown",
"id": "electoral-tamil",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"### Patient-specific AIF\n",
"Now we create an individual AIF object based on a series of time-concentration data points. We use the PatientSpecific subclass of AIF."
@@ -120,7 +155,11 @@
"cell_type": "code",
"execution_count": 5,
"id": "2d0a1c5b-7972-4be9-aa1b-9029e5645685",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [],
"source": [
"t_patient = np.array([19.810000,59.430000,99.050000,138.670000,178.290000,217.910000,257.530000,297.150000,336.770000,376.390000,416.010000,455.630000,495.250000,534.870000,574.490000,614.110000,653.730000,693.350000,732.970000,772.590000,812.210000,851.830000,891.450000,931.070000,970.690000,1010.310000,1049.930000,1089.550000,1129.170000,1168.790000,1208.410000,1248.030000])\n",
@@ -132,7 +171,11 @@
{
"cell_type": "markdown",
"id": "9a03a459-93d7-4779-9709-6389bf265024",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"Again, using the c_ap method we can get concentration at arbitrary time points. This is achieved using interpolation function."
]
@@ -141,7 +184,11 @@
"cell_type": "code",
"execution_count": 6,
"id": "c9d16286-d0df-4ab8-b918-4486652200f0",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [
{
"data": {
@@ -176,7 +223,11 @@
{
"cell_type": "markdown",
"id": "d30c7325-3453-4e38-a869-7439ed256437",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"### Other standard AIF functions\n",
"The following function is described in Manning et al., Magn Reson Med. 2021;86:1888–1903. \n",
@@ -188,7 +239,11 @@
"cell_type": "code",
"execution_count": 7,
"id": "e7274fdd-ef51-40fd-8ce9-d1b2820aa37e",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [],
"source": [
"manning_fast_aif = aifs.ManningFast(hct=0.42, t_start=3*39.62)"
@@ -197,7 +252,11 @@
{
"cell_type": "markdown",
"id": "83da4ffa-24c2-4e24-b1ec-30ed89a823c2",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"Manning et al. also reports an AIF for *slow* contrast injections in the same patient population, based on patient measurements:"
]
@@ -206,7 +265,11 @@
"cell_type": "code",
"execution_count": 8,
"id": "a9cd6fa3-3347-4c4d-9cc4-da0d2d027c69",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [],
"source": [
"manning_slow_aif = aifs.ManningSlow()"
@@ -215,7 +278,11 @@
{
"cell_type": "markdown",
"id": "f3d27241-b579-4b38-9ecd-1658184e6253",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"A similar population average function (for Bolus injection) was reported in Heye et al., NeuroImage 2016;125:446-455:"
]
@@ -224,7 +291,11 @@
"cell_type": "code",
"execution_count": 9,
"id": "a3fb3120-0b2c-420b-9e1f-0625ab9a4fff",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [],
"source": [
"heye_aif = aifs.Heye(hct=0.45, t_start=3*39.62)"
@@ -233,7 +304,11 @@
{
"cell_type": "markdown",
"id": "65d7c712-3ef0-4f87-a7b1-2dfa03c0e45d",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"Plot the above AIFs (and the original Parker model) for comparison:"
]
@@ -242,7 +317,11 @@
"cell_type": "code",
"execution_count": 11,
"id": "f7d24797-32ac-467d-8ea8-66b24079fa49",
- "metadata": {},
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
"outputs": [
{
"data": {
@@ -293,4 +372,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/demo/demo_conc_to_enh.ipynb b/demo/demo_conc_to_enh.ipynb
deleted file mode 100644
index 0d467fc..0000000
--- a/demo/demo_conc_to_enh.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "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": [
- "[,\n",
- " ]"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "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
-}
diff --git a/demo/demo_enh_to_conc.ipynb b/demo/demo_enh_to_conc.ipynb
new file mode 100644
index 0000000..230cf48
--- /dev/null
+++ b/demo/demo_enh_to_conc.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Enhancement to concentration\n",
+ "Demonstrate 2 classes for converting enhancement to concentration using analytical and numerical approaches.\n",
+ "### Imports"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3b2d4aa7-5b11-44fc-ad48-069caaeab47a",
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "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, aifs\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Setup\n",
+ "First define some parameters.\n",
+ "Then define concentration-relaxation and relaxation-signal relationships."
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "9439e538-cdd8-4849-bedf-1ead532f0811",
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "r1, r2 = 5.0, 0 # /s/mM\n",
+ "tr, fa, te = 3.4e-3, 15, 1.7e-3 # s/deg\n",
+ "t10 = 2 # s\n",
+ "k_fa = 1.0\n",
+ "c_to_r_model = relaxivity.CRLinear(r1, r2)\n",
+ "signal_model = signal_models.SPGR(tr, fa, te)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Calculate and plot the predicted enhancement for a range of concentrations."
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "conc_range = np.arange(-0.5, 12, 0.005) # mM\n",
+ "enh = dce_fit.conc_to_enh(conc_range, t10, k_fa, c_to_r_model, signal_model)\n",
+ "plt.plot(conc_range, enh)\n",
+ "plt.xlabel('C (mM)')\n",
+ "plt.ylabel('enhancement (%)');"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Enhancement to concentration conversion\n",
+ "Now take these enhancments and go backwards to estimate concentration.\n",
+ "First use the general method to convert enhancement to concentration.\n",
+ "This uses a numerical method to estimate concentration.\n",
+ "Accounts for pulse sequence and T2* dephasing."
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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) # EnhToConc (numerical) object\n",
+ "C_t_num = e_to_c_num.proc(enh, t10, k_fa=k_fa) # mM\n",
+ "plt.plot(enh, C_t_num)\n",
+ "plt.xlabel('enhancement (%)')\n",
+ "plt.ylabel('C (mM)');"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now use the analytical method.\n",
+ "This assumes the SPGR pulse sequence.\n",
+ "It does not account for T2* dephasing."
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "e_to_c_ana = dce_fit.EnhToConcSPGR(tr, fa, r1) # EnhToConcSPGR object\n",
+ "C_t_ana = e_to_c_ana.proc(enh, t10, k_fa=k_fa) # mM\n",
+ "plt.plot(enh, C_t_ana)\n",
+ "plt.xlabel('enhancement (%)')\n",
+ "plt.ylabel('C (mM)');"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Plot difference between estimated and true concentrations"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(enh, C_t_num - conc_range, 'k-', label='numerical (general)')\n",
+ "plt.plot(enh, C_t_ana - conc_range, 'b-', label='analytical (SPGR)');\n",
+ "plt.legend()\n",
+ "plt.xlabel('enhancement (%)')\n",
+ "plt.ylabel('C error (mM)');"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ }
+ ],
+ "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
+}
\ No newline at end of file
diff --git a/src/dce_fit.py b/src/dce_fit.py
index 4db5286..d4a8e53 100644
--- a/src/dce_fit.py
+++ b/src/dce_fit.py
@@ -63,7 +63,7 @@ def proc(self, s):
s_pre = np.mean(s[self.base_idx])
if s_pre <= 0:
raise ArithmeticError('Baseline signal is zero or negative.')
- enh = np.empty(s.shape, dtype=np.float32)
+ enh = np.empty(s.shape, dtype=np.float64)
enh[:] = 100. * ((s - s_pre) / s_pre) if s_pre > 0 else np.nan
return enh