{
"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": "Y98YDJr215fa"
},
"source": [
"## **INSTALL** `pypulseq`"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ogKNAZH3TmgA"
},
"outputs": [],
"source": [
"!pip install git+https://github.com/imr-framework/pypulseq.git@dev"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "UgqzEwle2xCd"
},
"source": [
"## **IMPORT PACKAGES**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "3X7UsV832B6j"
},
"outputs": [],
"source": [
"from math import pi\n",
"\n",
"import numpy as np\n",
"import pickle\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": 4,
"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=3.0,\n",
" sl_thkn=0.004,\n",
" sl_gap=100.0,\n",
" FoV_f=0.2,\n",
" FoV_ph=0.2,\n",
" Nf=128.0,\n",
" Np=128.0,\n",
" BW_pixel=500.0,\n",
" TE=0.015000000000000001,\n",
" TR=0.5,\n",
" FA=90.0)"
]
},
"execution_count": 4,
"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"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ssnNwiQH4q_0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User inputs setup\n"
]
},
{
"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=3.0,\n",
" sl_thkn=0.004,\n",
" sl_gap=100.0,\n",
" FoV_f=0.2,\n",
" FoV_ph=0.2,\n",
" Nf=128.0,\n",
" Np=128.0,\n",
" BW_pixel=500.0,\n",
" TE=0.015000000000000001,\n",
" TR=0.5,\n",
" FA=90.0,\n",
" nsa=1)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"params.nsa = 1 # Number of averages\n",
"n_slices = 3 # Number of slices\n",
"Nx = 128\n",
"Ny = 128\n",
"fov = 220e-3 # mm\n",
"slice_thickness = 5e-3 # s\n",
"slice_gap = 15e-3 # s\n",
"rf_flip = 90 # 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": 6,
"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=125,\n",
" slew_unit=\"T/m/s\",\n",
" rf_ringdown_time=100e-6, \n",
" rf_dead_time=100e-6, \n",
" adc_dead_time=12e-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": 7,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "u2dW2nRf7obq"
},
"outputs": [],
"source": [
"TE = params.TE # s\n",
"TR = params.TR # s\n",
"tau = TE / 2 # s\n",
"readout_time = 6.4e-3\n",
"pre_time = 8e-4 # s"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OTw7M03g79bH"
},
"source": [
"## **RF**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "XDZyQrbL8I3Q"
},
"outputs": [],
"source": [
"flip90 = round(rf_flip * pi / 180, 3)\n",
"flip180 = 180 * pi / 180\n",
"rf90, gz90, _ = make_sinc_pulse(flip_angle=flip90, system=system, duration=4e-3, \n",
" slice_thickness=slice_thickness, apodization=0.5, \n",
" time_bw_product=4, return_gz = True)\n",
"rf180, gz180, _ = make_sinc_pulse(flip_angle=flip180, system=system, \n",
" duration=2.5e-3, \n",
" slice_thickness=slice_thickness, \n",
" apodization=0.5, \n",
" time_bw_product=4, phase_offset=90 * pi/180, return_gz = True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RFSHuUOG9LHK"
},
"source": [
"## **READOUT**\n",
"Readout gradients and related events"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Q8p-CttI9dk9"
},
"outputs": [],
"source": [
"delta_k = 1 / fov\n",
"k_width = Nx * delta_k\n",
"gx = make_trapezoid(channel='x', system=system, flat_area=k_width, \n",
" flat_time=readout_time)\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": 10,
"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=2e-3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5Css5esAkYHo"
},
"source": [
"## **SPOILER**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "R1DOmoKKkawr"
},
"outputs": [
{
"data": {
"text/plain": [
"namespace(type='trap',\n",
" channel='z',\n",
" amplitude=1080936.4548494983,\n",
" rise_time=0.00021,\n",
" flat_time=0.00278,\n",
" fall_time=0.00021,\n",
" area=3232.0,\n",
" flat_area=3005.0033444816054,\n",
" delay=0,\n",
" first=0,\n",
" last=0)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gz_spoil = make_trapezoid(channel='z', system=system, area=gz90.area * 4,\n",
" duration=pre_time * 4)\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": 12,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "aOKRJclb_mDQ"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"delay_1: namespace(type='delay', delay=0.06280999999999999)\n",
"delay_2: namespace(type='delay', delay=0.06280999999999999)\n",
"delay_TR: namespace(type='delay', delay=0.35268)\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}')"
]
},
{
"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": 14,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "B8ZmVkkrAXnK"
},
"outputs": [],
"source": [
"# Prepare RF offsets. This is required for multi-slice acquisition\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",
"\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=2e-3)\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, adc)\n",
" gy_pre = make_trapezoid(channel='y', system=system, \n",
" area=-phase_areas[-j -1], duration=2e-3)\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": 15,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "d_iCUR4nfoH9"
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"seq.plot(time_range=(0, 0.20))"
]
},
{
"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": 143,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "6iN0aeuuqKRe"
},
"outputs": [],
"source": [
"seq.write('t2_se_pypulseq_colab.seq') # Save to disk\n",
"# from google.colab import files\n",
"# files.download('t2_se_pypulseq_colab.seq') # Download locally"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4Q0b5w-lKtfP"
},
"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
}