{ "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": 101, "metadata": { "colab": {}, "colab_type": "code", "id": "3X7UsV832B6j" }, "outputs": [], "source": [ "from math import pi\n", "\n", "import numpy as np\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\n", "from pypulseq.calc_rf_center import calc_rf_center" ] }, { "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": 102, "metadata": { "colab": {}, "colab_type": "code", "id": "ssnNwiQH4q_0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "User inputs setup\n" ] } ], "source": [ "nsa = 1 # Number of averages\n", "n_slices = 1 # Number of slices\n", "Nx = 16\n", "Ny = 16\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')" ] }, { "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": 103, "metadata": { "colab": {}, "colab_type": "code", "id": "XHs1LT965kqg" }, "outputs": [], "source": [ "system = Opts(max_grad=32, grad_unit='mT/m', max_slew=130, slew_unit='T/m/s', \n", " grad_raster_time=10e-6, rf_ringdown_time=10e-6, \n", " rf_dead_time=100e-6)\n", "seq = Sequence(system)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ee-xBrpa7Zyn" }, "source": [ "## **TIME CONSTANTS**" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "colab": {}, "colab_type": "code", "id": "u2dW2nRf7obq" }, "outputs": [], "source": [ "TE = 100e-3 # s\n", "TR = 3 # 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": 105, "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": 106, "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": 107, "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": 108, "metadata": { "colab": {}, "colab_type": "code", "id": "R1DOmoKKkawr" }, "outputs": [], "source": [ "gz_spoil = make_trapezoid(channel='z', system=system, area=gz90.area * 4,\n", " duration=pre_time * 4)" ] }, { "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": 109, "metadata": { "colab": {}, "colab_type": "code", "id": "aOKRJclb_mDQ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "delay_1: namespace(type='delay', delay=0.04283000000000001)\n", "delay_2: namespace(type='delay', delay=0.04283000000000001)\n", "delay_TR: namespace(type='delay', delay=2.8927400000000003)\n" ] } ], "source": [ "delay1 = tau - calc_duration(rf90) / 2 - calc_duration(gx_pre)\n", "delay1 -= calc_duration(gz_spoil) - calc_duration(rf180) / 2\n", "delay1 = np.ceil(delay1/seq.grad_raster_time)*seq.grad_raster_time\n", "delay1 = make_delay(delay1)\n", "\n", "delay2 = tau - calc_duration(rf180) / 2 - calc_duration(gz_spoil)\n", "delay2 -= calc_duration(gx) / 2\n", "delay2 = np.ceil(delay2/seq.grad_raster_time)*seq.grad_raster_time\n", "delay2 = make_delay(delay2)\n", "\n", "delay_TR = TR - calc_duration(rf90) / 2 - calc_duration(gx) / 2 - TE\n", "delay_TR -= calc_duration(gy_pre)\n", "delay_TR = np.ceil(delay_TR/seq.grad_raster_time)*seq.grad_raster_time\n", "delay_TR = make_delay(delay_TR)\n", "\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": 110, "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(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[-i -1], duration=2e-3)\n", " seq.add_block(gy_pre, gz_spoil)\n", " seq.add_block(delay_TR)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29.545454545454547" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phase_areas[-1 -1]" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "# Prepare RF offsets. This is required for multi-slice acquisition\n", "rf90_phase = np.pi / 2\n", "rf180_phase = 0\n", "\n", "delta_z = n_slices * slice_gap\n", "z = np.linspace((-delta_z / 2), (delta_z / 2), n_slices) + rf_offset\n", "phase_areas = (np.arange(Ny) - (Ny / 2)) * delta_k\n", "for k in range(nsa): # Averages\n", " for j in range(n_slices): # Slices\n", " rf90.freq_offset = (gz180.amplitude * slice_thickness * (j - (n_slices - 1) / 2))\n", " rf180.freq_offset = (gz180.amplitude * slice_thickness * (j - (n_slices - 1) / 2))\n", " rf90.phase_offset = (rf90_phase - 2 * np.pi * rf90.freq_offset * calc_rf_center(rf90)[0])\n", " rf180.phase_offset = (rf180_phase - 2 * np.pi * rf180.freq_offset * calc_rf_center(rf180)[0])\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[-i -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": 115, "metadata": { "colab": {}, "colab_type": "code", "id": "d_iCUR4nfoH9" }, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seq.plot(time_range=(0, 0.125))" ] }, { "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": 116, "metadata": { "colab": {}, "colab_type": "code", "id": "6iN0aeuuqKRe" }, "outputs": [], "source": [ " seq.write('C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t2_SE\\\\t2_SE_matrx16x16.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": 117, "metadata": { "colab": {}, "colab_type": "code", "id": "4Q0b5w-lKtfP" }, "outputs": [ { "data": { "text/plain": [ "(,\n", " 'C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t2_SE/t2_SE_matrix16x16.xml')" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from py2jemris.seq2xml import seq2xml\n", "seq2xml(seq, seq_name='t2_SE_matrix16x16', out_folder='C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t2_SE')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "namespace(type='rf',\n", " signal=array([-9.52099400e-09, -2.57194516e-07, -1.19130264e-06, ...,\n", " -1.19130264e-06, -2.57194516e-07, -9.52099400e-09]),\n", " t=array([5.0000e-07, 1.5000e-06, 2.5000e-06, ..., 3.9975e-03, 3.9985e-03,\n", " 3.9995e-03]),\n", " shape_dur=0.004,\n", " freq_offset=-1500.0,\n", " phase_offset=0,\n", " dead_time=0.0001,\n", " ringdown_time=1e-05,\n", " delay=0.00010000000000000002)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf90\n", " " ] }, { "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 }