changeset 0:fe569aad237c draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/diffusion_signal_reconstruction.py	Sun Nov 05 09:30:58 2017 -0500
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+import argparse
+import shutil
+
+from dipy.data import fetch_sherbrooke_3shell
+from dipy.data import fetch_stanford_hardi
+from dipy.data import get_sphere
+from dipy.data import read_sherbrooke_3shell
+from dipy.data import read_stanford_hardi
+from dipy.reconst import dti
+from dipy.reconst.dti import color_fa
+from dipy.reconst.dti import fractional_anisotropy
+from dipy.segment.mask import median_otsu
+from dipy.viz import fvtk
+from matplotlib import pyplot
+
+import nibabel
+import numpy
+
+# http://nipy.org/dipy/examples_built/reconst_dti.html#example-reconst-dti
+parser = argparse.ArgumentParser()
+parser.add_argument('--drmi_dataset', dest='drmi_dataset', help='Input dataset')
+parser.add_argument('--output_nifti1_fa', dest='output_nifti1_fa', help='Output fractional anisotropy Nifti1 dataset')
+parser.add_argument('--output_nifti1_evecs', dest='output_nifti1_fevecs', help='Output eigen vectors Nifti1 dataset')
+parser.add_argument('--output_nifti1_md', dest='output_nifti1_md', help='Output mean diffusivity Nifti1 dataset')
+parser.add_argument('--output_nifti1_rgb', dest='output_nifti1_rgb', help='Output RGB-map Nifti1 dataset')
+parser.add_argument('--output_png_ellipsoids', dest='output_png_ellipsoids', help='Output ellipsoids PNG dataset')
+parser.add_argument('--output_png_odfs', dest='output_png_odfs', help='Output orientation distribution functions PNG dataset')
+parser.add_argument('--output_png_middle_axial_slice', dest='output_png_middle_axial_slice', help='Output middle axial slice PNG dataset')
+
+args = parser.parse_args()
+
+# Get input data.
+input_dir = args.drmi_dataset
+if input_dir == 'sherbrooke_3shell':
+    fetch_sherbrooke_3shell()
+    img, gtab = read_sherbrooke_3shell()
+elif input_dir == 'stanford_hardi':
+    fetch_stanford_hardi()
+    img, gtab = read_stanford_hardi()
+
+data = img.get_data()
+maskdata, mask = median_otsu(data, 3, 1, True, vol_idx=range(10, 50), dilate=2)
+
+axial_middle = data.shape[2] // 2
+pyplot.subplot(1, 2, 1).set_axis_off()
+pyplot.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
+pyplot.subplot(1, 2, 2).set_axis_off()
+pyplot.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
+pyplot.savefig('middle_axial.png', bbox_inches='tight')
+shutil.move('middle_axial.png', args.output_png_middle_axial_slice)
+
+tenmodel = dti.TensorModel(gtab)
+tenfit = tenmodel.fit(maskdata)
+
+fa = fractional_anisotropy(tenfit.evals)
+fa[numpy.isnan(fa)] = 0
+fa_img = nibabel.Nifti1Image(fa.astype(numpy.float32), img.affine)
+nibabel.save(fa_img, 'output_fa.nii')
+shutil.move('output_fa.nii', args.output_nifti1_fa)
+
+evecs_img = nibabel.Nifti1Image(tenfit.evecs.astype(numpy.float32), img.affine)
+nibabel.save(evecs_img, 'output_evecs.nii')
+shutil.move('output_evecs.nii', args.output_nifti1_evecs)
+
+md1 = dti.mean_diffusivity(tenfit.evals)
+nibabel.save(nibabel.Nifti1Image(md1.astype(numpy.float32), img.affine), 'output_md.nii')
+shutil.move('output_md.nii', args.output_nifti1_md)
+
+fa = numpy.clip(fa, 0, 1)
+rgb = color_fa(fa, tenfit.evecs)
+nibabel.save(nibabel.Nifti1Image(numpy.array(255 * rgb, 'uint8'), img.affine), 'output_rgb.nii')
+shutil.move('output_rgb.nii', args.output_nifti1_rgb)
+
+sphere = get_sphere('symmetric724')
+ren = fvtk.ren()
+
+evals = tenfit.evals[13:43, 44:74, 28:29]
+evecs = tenfit.evecs[13:43, 44:74, 28:29]
+cfa = rgb[13:43, 44:74, 28:29]
+cfa /= cfa.max()
+fvtk.add(ren, fvtk.tensor(evals, evecs, cfa, sphere))
+fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids.png', size=(600, 600))
+shutil.move('tensor_ellipsoids.png', args.output_png_ellipsoids)
+
+fvtk.clear(ren)
+
+tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
+fvtk.add(ren, fvtk.sphere_funcs(tensor_odfs, sphere, colormap=None))
+fvtk.record(ren, n_frames=1, out_path='tensor_odfs.png', size=(600, 600))
+shutil.move('tensor_odfs.png', args.output_png_odfs)

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/diffusion_signal_reconstruction.xml	Sun Nov 05 09:30:58 2017 -0500
@@ -0,0 +1,49 @@
+<tool id="diffusion_signal_reconstruction" name="Reconstruct diffusion signal" version="0.13.0">
+    <description>with Tensor model</description>
+    <requirements>
+        <requirement type="package" version="0.13.0">dipy</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+python '$__tool_directory__/diffusion_signal_reconstruction.py'
+--drmi_dataset $drmi_dataset
+--output_nifti1_evecs '$output_nifti1_evecs'
+--output_nifti1_fa '$output_nifti1_fa'
+--output_nifti1_md '$output_nifti1_md'
+--output_nifti1_rgb '$output_nifti1_rgb'
+--output_png_ellipsoids '$output_png_ellipsoids'
+--output_png_middle_axial_slice '$output_png_middle_axial_slice'
+--output_png_odfs '$output_png_odfs'
+    ]]></command>
+    <inputs>
+        <param name="drmi_dataset" type="select" label="dRMI dataset">
+            <option value="stanford_hardi" selected="true">stanford_hardi</option>
+            <option value="sherbrooke_3shell">sherbrooke_3shell</option>
+        </param>
+    </inputs>
+    <outputs>
+        <data name="output_png_odfs" format="png" label="${tool.name}: orientation distribution functions" />
+        <data name="output_png_ellipsoids" format="png" label="${tool.name}: colored ellipsoids" />
+        <data name="output_png_middle_axial_slice" format="png" label="${tool.name}: middle axial slice without (left) and with (right) diffusion weighting" />
+        <data name="output_nifti1_fa" format="nifti1" label="${tool.name}: fractional anisotropye" />
+        <data name="output_nifti1_evecs" format="nifti1" label="${tool.name}: eigen vectorse" />
+        <data name="output_nifti1_evecs" format="nifti1" label="${tool.name}: mean diffusivitye" />
+        <data name="output_nifti1_rgb" format="nifti1" label="${tool.name}: RGB-mape" />
+    </outputs>
+    <tests>
+        <test>
+        </test>
+    </tests>
+    <help>
+**What it does**
+
+The diffusion tensor model is a model that describes the diffusion within a voxel. It demonstrates
+the utility of diffusion MRI in characterizing the micro-structure of white matter tissue and of the
+biophysical properties of tissue, inferred from local diffusion properties.
+-----
+
+**Options**
+
+    </help>
+    <citations>
+    </citations>
+</tool>