{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MiKvRj5u076V"
},
"source": [
"## **ABOUT**\n",
"This example illustrates the 2D multi-slice, Spin Echo (SE) acquisition using the `pypulseq` library. This sequence is typically used for T2 weighted imaging. A 2D Fourier transform can be used to reconstruct images from this acquisition. Read more about SE [here](http://mriquestions.com/se-vs-multi-se-vs-fse.html).\n",
"\n",
"**Contact**: For issues, write to ks3621@columbia.edu\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "UgqzEwle2xCd"
},
"source": [
"## **IMPORT PACKAGES**"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "3X7UsV832B6j"
},
"outputs": [],
"source": [
"from math import pi\n",
"\n",
"import numpy as np\n",
"import pickle\n",
"import math\n",
"\n",
"from pypulseq.Sequence.sequence import Sequence\n",
"from pypulseq.calc_duration import calc_duration\n",
"from pypulseq.make_adc import make_adc\n",
"from pypulseq.make_delay import make_delay\n",
"from pypulseq.make_sinc_pulse import make_sinc_pulse\n",
"from pypulseq.make_trapezoid import make_trapezoid\n",
"from pypulseq.opts import Opts"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "UQ4AWw9l4et_"
},
"source": [
"## **USER INPUTS**\n",
"\n",
"These parameters are typically on the user interface of the scanner computer console "
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"namespace(t_BW_product_ex=3.8,\n",
" t_BW_product_ref=4.2,\n",
" t_ex=0.002048,\n",
" t_ref=0.00256,\n",
" dG=0.00025,\n",
" sl_nb=1.0,\n",
" sl_thkn=0.004,\n",
" sl_gap=100.0,\n",
" FoV_f=0.2,\n",
" FoV_ph=0.2,\n",
" Nf=16.0,\n",
" Np=16.0,\n",
" BW_pixel=500.0,\n",
" TE=0.015000000000000001,\n",
" TR=0.4,\n",
" FA=90.0,\n",
" gamma=42576000.0,\n",
" G_amp_max=1609372.8)"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('C:\\MRI_seq_files_mess\\SE_pulsequence_parameters.pickle', 'rb') as f:\n",
" params = pickle.load(f, fix_imports=True)\n",
"params.gamma = 42.576e6 #TODO add to GUI\n",
"params.G_amp_max = 37.8*1e-3*params.gamma #TODO add to GUI\n",
"params"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ssnNwiQH4q_0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User inputs setup\n"
]
}
],
"source": [
"params.nsa = 1 # Number of averages\n",
"n_slices = 1 #params.sl_nb # Number of slices\n",
"Nx = 16\n",
"Ny = 16\n",
"fov = 220e-3 # mm\n",
"slice_thickness = params.sl_thkn # s\n",
"slice_gap = params.sl_gap # s\n",
"rf_flip = params.FA # degrees\n",
"rf_offset = 0\n",
"print('User inputs setup')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PeYeI0V45ZfD"
},
"source": [
"## **SYSTEM LIMITS**\n",
"Set the hardware limits and initialize sequence object"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "XHs1LT965kqg"
},
"outputs": [],
"source": [
"dG = 256e-6 #TODO: get from GUI!\n",
"rf_raster_time = 1e-6\n",
"grad_raster_time = 10e-6\n",
"\n",
"# Set system limits\n",
"system = Opts(\n",
" max_grad=37.8,\n",
" grad_unit=\"mT/m\",\n",
" max_slew=121,\n",
" slew_unit=\"T/m/s\",\n",
" rf_ringdown_time=20e-6, \n",
" rf_dead_time=100e-6, \n",
" adc_dead_time=10e-6, \n",
" rf_raster_time = rf_raster_time, \n",
" grad_raster_time = grad_raster_time, \n",
" block_duration_raster = grad_raster_time, \n",
")\n",
"seq = Sequence(system)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ee-xBrpa7Zyn"
},
"source": [
"## **TIME CONSTANTS**"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "u2dW2nRf7obq"
},
"outputs": [],
"source": [
"TE = 0.015 # s\n",
"TR = params.TR # s\n",
"tau = TE / 2 # s\n",
"readout_time = 1/params.BW_pixel\n",
"pre_time = 8e-4 # s"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OTw7M03g79bH"
},
"source": [
"## **RF**"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "XDZyQrbL8I3Q"
},
"outputs": [
{
"data": {
"text/plain": [
"namespace(type='rf',\n",
" signal=array([-1.40967002e-05, -1.29247919e-04, -3.65626618e-04, ...,\n",
" -3.65626618e-04, -1.29247919e-04, -1.40967002e-05]),\n",
" t=array([5.0000e-07, 1.5000e-06, 2.5000e-06, ..., 2.0455e-03, 2.0465e-03,\n",
" 2.0475e-03]),\n",
" shape_dur=0.002048,\n",
" freq_offset=0,\n",
" phase_offset=0,\n",
" dead_time=0,\n",
" ringdown_time=0,\n",
" delay=8.8e-05)"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flip90 = round(rf_flip * pi / 180, 3)\n",
"flip180 = 180 * pi / 180\n",
"rf90, gz90, _ = make_sinc_pulse(flip_angle = flip90 * math.pi / 180, system = system, duration = params.t_ex, \n",
" slice_thickness = params.sl_thkn, apodization=0.3, \n",
" time_bw_product = params.t_BW_product_ex, return_gz = True)\n",
"\n",
"rf180, gz180, _ = make_sinc_pulse(flip_angle = flip180 * math.pi / 180, system = system, duration = params.t_ref, \n",
" slice_thickness = params.sl_thkn, apodization=0.3, \n",
" time_bw_product = params.t_BW_product_ref, phase_offset=90 * pi/180, return_gz = True)\n",
"rf90.delay = round(rf90.delay,8)\n",
"rf180.delay = round(rf180.delay,8)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## **Slice selection gradients**"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [],
"source": [
"# gz_reph rephase gradient after gz90\n",
"gz_reph = make_trapezoid(channel='z', system=system, area=-gz90.area / 2,\n",
" duration = params.t_ex/2)\n",
"\n",
"# gz_spoil spoil gradients around \n",
"gz_spoil = make_trapezoid(channel='z', system=system, area=gz90.area,\n",
" duration=params.t_ref / 2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RFSHuUOG9LHK"
},
"source": [
"## **READOUT gradients & events**"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Q8p-CttI9dk9"
},
"outputs": [],
"source": [
"delta_k = 1 / fov\n",
"k_width = Nx * delta_k\n",
"# gx readout gradient\n",
"gx = make_trapezoid(channel='x', system=system, flat_area=k_width, \n",
" flat_time=readout_time)\n",
"# gx_pre readout prephase gradient\n",
"gx_pre = make_trapezoid(channel='x', system=system, flat_area=k_width / 2, \n",
" flat_time=readout_time / 2)\n",
"\n",
"gx_spoil = make_trapezoid(channel='x', system=system, area=gz90.area * 0.75,\n",
" duration=readout_time / 2)\n",
"\n",
"adc = make_adc(num_samples=Nx, duration=gx.flat_time, delay=gx.rise_time)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "o829kzm8kVFB"
},
"source": [
"## **PREPHASE AND REPHASE**"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "m5zA1bMakTVs"
},
"outputs": [],
"source": [
"phase_areas = (np.arange(Ny) - (Ny / 2)) * delta_k\n",
"#gz_reph = make_trapezoid(channel='z', system=system, area=-gz90.area / 2,\n",
" # duration=2.5e-3)\n",
"#gx_pre = make_trapezoid(channel='x', system=system, flat_area=k_width / 2, \n",
"# flat_time=readout_time / 2)\n",
"gy_pre = make_trapezoid(channel='y', system=system, area=phase_areas[-1], \n",
" duration=params.t_ex/2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5Css5esAkYHo"
},
"source": [
"## **SPOILER**"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "R1DOmoKKkawr"
},
"outputs": [],
"source": [
"#gz_spoil = make_trapezoid(channel='z', system=system, area=gz90.area,\n",
"# duration=readout_time / 2)\n",
"#gz_spoil"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3F5JUpE9-4lo"
},
"source": [
"## **DELAYS**\n",
"Echo time (TE) and repetition time (TR). Here, TE is broken down into `delay1` and `delay2`."
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "aOKRJclb_mDQ"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"delay_1: namespace(type='delay', delay=0.005457)\n",
"delay_2: namespace(type='delay', delay=0.005457)\n",
"delay_TR: namespace(type='delay', delay=0.381901)\n"
]
}
],
"source": [
"delay1 = tau - calc_duration(rf90) / 2 - calc_duration(gx_pre)\n",
"delay1 -= calc_duration(gz_spoil) - calc_duration(rf180) / 2\n",
"delay1 = make_delay(delay1)\n",
"delay2 = tau - calc_duration(rf180) / 2 - calc_duration(gz_spoil)\n",
"delay2 -= calc_duration(gx) / 2\n",
"delay2 = make_delay(delay2)\n",
"delay_TR = TR - calc_duration(rf90) / 2 - calc_duration(gx) / 2 - TE\n",
"delay_TR -= calc_duration(gy_pre)\n",
"delay_TR = make_delay(delay_TR)\n",
"print(f'delay_1: {delay1}')\n",
"print(f'delay_2: {delay1}')\n",
"print(f'delay_TR: {delay_TR}')\n",
"\n",
"delay1 = np.ceil((delay1/seq.grad_raster_time)*seq.grad_raster_time)\n",
"delay2 = np.ceil((delay2/seq.grad_raster_time)*seq.grad_raster_time)\n",
"delay_TR = np.ceil((delay_TR/seq.grad_raster_time)*seq.grad_raster_time)\n",
"\n",
"delay1 = round(delay1,8)\n",
"delay2 = round(delay2,8)\n",
"delay_TR = round(delay_TR,8)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "6Dq4wT-UAEOR"
},
"source": [
"## **CONSTRUCT SEQUENCE**\n",
"Construct sequence for one phase encode and multiple slices"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "B8ZmVkkrAXnK"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"namespace(type='rf', signal=array([-1.40967002e-05, -1.29247919e-04, -3.65626618e-04, ...,\n",
" -3.65626618e-04, -1.29247919e-04, -1.40967002e-05]), t=array([5.0000e-07, 1.5000e-06, 2.5000e-06, ..., 2.0455e-03, 2.0465e-03,\n",
" 2.0475e-03]), shape_dur=0.002048, freq_offset=-23193359.375, phase_offset=0, dead_time=0, ringdown_time=0, delay=8.8e-05)\n"
]
}
],
"source": [
"# Prepare RF offsets. This is required for multi-slice acquisition\n",
"\n",
"phase_areas = (np.arange(Ny) - (Ny / 2)) * delta_k\n",
"\n",
"delta_z = n_slices * slice_gap\n",
"z = np.linspace((-delta_z / 2), (delta_z / 2), n_slices) + rf_offset\n",
"\n",
"for k in range(params.nsa): # Averages\n",
" for j in range(n_slices): # Slices\n",
" # Apply RF offsets\n",
" freq_offset = gz90.amplitude * z[j]\n",
" rf90.freq_offset = freq_offset\n",
" print(rf90)\n",
"\n",
" freq_offset = gz180.amplitude * z[j]\n",
" rf180.freq_offset = freq_offset\n",
"\n",
" for i in range(Ny): # Phase encodes\n",
" seq.add_block(rf90, gz90)\n",
" gy_pre = make_trapezoid(channel='y', system=system, \n",
" area=phase_areas[-i -1], duration = params.t_ex/2)\n",
" seq.add_block(gx_pre, gy_pre, gz_reph)\n",
" seq.add_block(delay1)\n",
" seq.add_block(gz_spoil)\n",
" seq.add_block(rf180, gz180)\n",
" seq.add_block(gz_spoil)\n",
" seq.add_block(delay2)\n",
" seq.add_block(gx_spoil) \n",
" seq.add_block(gx, adc)\n",
" seq.add_block(gx_spoil)\n",
" gy_pre = make_trapezoid(channel='y', system=system, \n",
" area=-phase_areas[-j -1], duration = params.t_ex/2)\n",
" seq.add_block(gy_pre, gz_spoil)\n",
" seq.add_block(delay_TR)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "l-YP9djBJCpC"
},
"source": [
"## **PLOTTING TIMNG DIAGRAM**"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "d_iCUR4nfoH9"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"seq.plot(time_range=(0.000, 0.030))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fYNgdWc_KiK7"
},
"source": [
"## **GENERATING `.SEQ` FILE**\n",
"Uncomment the code in the cell below to generate a `.seq` file and download locally."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "6iN0aeuuqKRe"
},
"outputs": [],
"source": [
"seq.write('C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t1_se\\\\t1_SE_matrx16x16.seq') # Save to disk\n",
"# from google.colab import files\n",
"# files.download('t2_se_pypulseq_colab.seq') # Download locally\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4Q0b5w-lKtfP"
},
"outputs": [
{
"data": {
"text/plain": [
"(,\n",
" 'C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t1_se/t1_SE_matrix16x16.xml')"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from py2jemris.seq2xml import seq2xml\n",
"seq2xml(seq, seq_name='t1_SE_matrix16x16', out_folder='C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t1_se')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "write_t2_se.ipynb",
"private_outputs": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}