.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "generated/gallery/01-course/05_testing_on_the_virtual_scanner.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_generated_gallery_01-course_05_testing_on_the_virtual_scanner.py: ======================================== 5. Testing on the virtual scanner ======================================== This lesson is optional: it extends the course rather than completing it. The virtual scanner stands in for the scanner and nothing else, so a sequence and a reconstruction plugin can be tested before a scanner is involved. It acquires in two ways: along the played trajectory from an analytic phantom, which leaves relaxation out, or by a Bloch simulation of every block the cache plays. The conversion itself is checked by comparing the gradients a sequence asks for with those its cache plays. The models are described in :doc:`/explanations/virtual-scanner`. **Learning objectives** - Acquire a phantom with :func:`~pulserver.virtual.acquire` and simulate it on isochromats with :func:`~pulserver.virtual.simulate`, and state what each models. - Check a cache against the sequence it was converted from with :func:`~pulserver.validate.validate`. The previous lesson reconstructed a series with a plugin of its own. .. GENERATED FROM PYTHON SOURCE LINES 25-31 .. GENERATED FROM PYTHON SOURCE LINES 32-39 A sequence with T1 contrast --------------------------- The design is the 2D gradient echo at a 20 ms TR and a 30° flip angle, without dummy scans, converted into its cache. The phantom holds two ellipses of equal proton density and T2, with a T1 of 300 ms on the left and 1500 ms on the right. .. GENERATED FROM PYTHON SOURCE LINES 39-61 .. code-block:: Python import tempfile from pathlib import Path import numpy as np import pypulseqpp as pp from pypulseqpp.sequences.sequence.gre2D_sequence import gre2d from pulserver import ir, virtual from pulserver.proxy import SequenceTable system = pp.Opts(max_grad=40, grad_unit="mT/m", max_slew=150, slew_unit="T/m/s") path = Path(tempfile.mkdtemp()) / "gre2d.seq" gre2d(system, n_x=64, n_y=64, tr=0.02, flip_angle_deg=30.0, n_dummy=0).write(path) ir.convert(path, system) phantom = virtual.Phantom( [ virtual.Ellipse((-0.04, 0.0, 0.0), (0.03, 0.05), t1=0.3, t2=0.08), virtual.Ellipse((0.04, 0.0, 0.0), (0.03, 0.05), t1=1.5, t2=0.08), ] ) .. GENERATED FROM PYTHON SOURCE LINES 62-72 Two acquisitions ---------------- :func:`~pulserver.virtual.acquire` evaluates the phantom's analytic transform at the k-space location of every sample: each excitation tips the whole magnetization, whatever the flip angle and T1. The isochromats of :meth:`~pulserver.virtual.Phantom.isochromats`, here on a 1 mm grid, are played block by block by :func:`~pulserver.virtual.simulate` with the Bloch equation, so the flip angle, relaxation and the approach to steady state act on the signal. .. GENERATED FROM PYTHON SOURCE LINES 72-75 .. code-block:: Python analytic = virtual.acquire(path, phantom) simulated = virtual.simulate(path, phantom.isochromats(1e-3)) .. GENERATED FROM PYTHON SOURCE LINES 76-78 Both return one array of samples per readout in play order; the encoding counters of the sequence place them in k-space. .. GENERATED FROM PYTHON SOURCE LINES 78-108 .. code-block:: Python lines = SequenceTable.read(path).counters["LIN"] def image(readouts): kspace = np.zeros((64, readouts[0].shape[-1]), complex) kspace[lines] = np.stack([readout[0] for readout in readouts]) pixels = np.fft.fftshift(np.fft.ifft2(np.fft.ifftshift(kspace))) return np.abs(pixels[:, 32:96]) images = {"acquire": image(analytic), "simulate": image(simulated)} inside = images["acquire"] > 0.5 * images["acquire"].max() short_t1, long_t1 = inside.copy(), inside.copy() short_t1[:, 32:], long_t1[:, :32] = False, False for name, pixels in images.items(): ratio = pixels[short_t1].mean() / pixels[long_t1].mean() print(f"{name}: mean signal, short T1 over long T1, {ratio:.2f}") .. image-sg:: /generated/gallery/01-course/images/sphx_glr_05_testing_on_the_virtual_scanner_001.png :alt: acquire, simulate :srcset: /generated/gallery/01-course/images/sphx_glr_05_testing_on_the_virtual_scanner_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none acquire: mean signal, short T1 over long T1, 1.00 simulate: mean signal, short T1 over long T1, 3.28 .. GENERATED FROM PYTHON SOURCE LINES 109-122 The analytic acquisition shows two ellipses of equal signal: it tests the trajectory, the encoding and the reconstruction. The Bloch simulation shows the T1 weighting of a spoiled gradient echo at a short TR and, since the scan plays no dummy scans, the ghosts along the phase-encoding axis that the approach to steady state leaves. It tests the sequence's contrast. Conversion check ---------------- :func:`~pulserver.validate.validate` compares the gradients the sequence asks for with a second rendering. Without a recording of a scanner playing it, the rendering is the cache's own waveforms. Agreement establishes that the conversion kept the sequence; it does not establish that a scanner plays it. .. GENERATED FROM PYTHON SOURCE LINES 122-125 .. code-block:: Python from pulserver.validate import validate print(validate(path, system=system)) .. rst-class:: sphx-glr-script-out .. code-block:: none the sequence agrees with the ir waveforms gx largest difference 9.542e-06 of 38.79 (0.00%) gy largest difference 7.34e-08 of 21.35 (0.00%) gz largest difference 3.35e-10 of 35.63 (0.00%) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.150 seconds) .. _sphx_glr_download_generated_gallery_01-course_05_testing_on_the_virtual_scanner.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 05_testing_on_the_virtual_scanner.ipynb <05_testing_on_the_virtual_scanner.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 05_testing_on_the_virtual_scanner.py <05_testing_on_the_virtual_scanner.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 05_testing_on_the_virtual_scanner.zip <05_testing_on_the_virtual_scanner.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_