Crossing-preserving contextual enhancement

This demo presents an example of crossing-preserving contextual enhancement of FOD/ODF fields [1], implementing the contextual PDE framework of [2] for processing HARDI data. The aim is to enhance the alignment of elongated structures in the data such that crossing/junctions are maintained while reducing noise and small incoherent structures. This is achieved via a hypo-elliptic 2nd order PDE in the domain of coupled positions and orientations \(\mathbb{R}^3 \rtimes S^2\). This domain carries a non-flat geometrical differential structure that allows including a notion of alignment between neighboring points.

Let \(({\bf y},{\bf n}) \in \mathbb{R}^3 times S^2\) where \({\bf y} \in \mathbb{R}^{3}\) denotes the spatial part, and \({\bf n} \in S^2\) the angular part. Let \(W:\mathbb{R}^3\rtimes S^2\times \mathbb{R}^{+} \to \mathbb{R}\) be the function representing the evolution of FOD/ODF field. Then, the contextual PDE with evolution time \(t\geq 0\) is given by:

\[\begin{cases} \frac{\partial}{\partial t} W({\bf y},{\bf n},t) &= ((D^{33}({\bf n} \cdot \nabla)^2 + D^{44} \Delta_{S^2})W)({\bf y},{\bf n},t) \ W({\bf y},{\bf n},0) &= U({\bf y},{\bf n}) \end{cases},\]

where:

• \(D^{33}>0\) is the coefficient for the spatial smoothing (which goes only in the direction of \(n\));

• \(D^{44}>0\) is the coefficient for the angular smoothing (here \(\Delta_{S^2}\) denotes the Laplace-Beltrami operator on the sphere \(S^2\));

• \(U:\mathbb{R}^3\rtimes S^2 \to \mathbb{R}\) is the initial condition given by the noisy FOD/ODF’s field.

This equation is solved via a shift-twist convolution (denoted by \(\ast_{\mathbb{R}^3\rtimes S^2}\)) with its corresponding kernel \(P_t:\mathbb{R}^3\rtimes S^2 \to \mathbb{R}^+\):

\[W({\bf y},{\bf n},t) = (P_t \ast_{\mathbb{R}^3 \rtimes S^2} U) ({\bf y},{\bf n}) = \int_{\mathbb{R}^3} \int_{S^2} P_t (R^T_{{\bf n}^\prime}({\bf y}-{\bf y}^\prime), R^T_{{\bf n}^\prime} {\bf n} ) U({\bf y}^\prime, {\bf n}^\prime)\]

Here, \(R_{\bf n}\) is any 3D rotation that maps the vector \((0,0,1)\) onto \({\bf n}\).

Note that the shift-twist convolution differs from a Euclidean convolution and takes into account the non-flat structure of the space \(\mathbb{R}^3\rtimes S^2\).

The kernel \(P_t\) has a stochastic interpretation [3]. It can be seen as the limiting distribution obtained by accumulating random walks of particles in the position/orientation domain, where in each step the particles can (randomly) move forward/backward along their current orientation, and (randomly) change their orientation. This is an extension to the 3D case of the process for contour enhancement of 2D images.

The random motion of particles (a) and its corresponding probability map (b) in 2D. The 3D kernel is shown on the right. Adapted from [2].

In practice, as the exact analytical formulas for the kernel \(P_t\) are unknown, we use the approximation given in [4].

import numpy as np

from dipy.core.gradients import gradient_table
from dipy.data import default_sphere, get_fnames
from dipy.denoise.enhancement_kernel import EnhancementKernel
from dipy.denoise.shift_twist_convolution import convolve
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti_data
from dipy.reconst.csdeconv import (
    ConstrainedSphericalDeconvModel,
    auto_response_ssst,
    odf_sh_to_sharp,
)
from dipy.reconst.shm import sf_to_sh, sh_to_sf
from dipy.segment.mask import median_otsu
from dipy.sims.voxel import add_noise
from dipy.viz import actor, window

The enhancement is evaluated on the Stanford HARDI dataset (150 orientations, b=2000 \(s/mm^2\)) where Rician noise is added. Constrained spherical deconvolution is used to model the fiber orientations.

# Read data
hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames(name="stanford_hardi")
data = load_nifti_data(hardi_fname)
bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
gtab = gradient_table(bvals, bvecs=bvecs)

# Add Rician noise
b0_slice = data[:, :, :, 1]
b0_mask, mask = median_otsu(b0_slice)
rng = np.random.default_rng(1)
data_noisy = add_noise(
    data, 10.0, np.mean(b0_slice[mask]), noise_type="rician", rng=rng
)

# Select a small part of it.
padding = 3  # Include a larger region to avoid boundary effects
data_small = data[25 - padding : 40 + padding, 65 - padding : 80 + padding, 35:42]
data_noisy_small = data_noisy[
    25 - padding : 40 + padding, 65 - padding : 80 + padding, 35:42
]

Enables/disables interactive visualization

interactive = False

Fit an initial model to the data, in this case Constrained Spherical Deconvolution is used.

# Perform CSD on the original data
response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
csd_model_orig = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit_orig = csd_model_orig.fit(data_small)
csd_shm_orig = csd_fit_orig.shm_coeff

# Perform CSD on the original data + noise
response, ratio = auto_response_ssst(gtab, data_noisy, roi_radii=10, fa_thr=0.7)
csd_model_noisy = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit_noisy = csd_model_noisy.fit(data_noisy_small)
csd_shm_noisy = csd_fit_noisy.shm_coeff

Inspired by [5], a lookup-table is created, containing rotated versions of the kernel \(P_t\) sampled over a discrete set of orientations. In order to ensure rotationally invariant processing, the discrete orientations are required to be equally distributed over a sphere. By default, a sphere with 100 directions is used.

# Create lookup table
D33 = 1.0
D44 = 0.02
t = 1
k = EnhancementKernel(D33, D44, t)

Visualize the kernel

scene = window.Scene()

# convolve kernel with delta spike
spike = np.zeros((7, 7, 7, k.get_orientations().shape[0]), dtype=np.float64)
spike[3, 3, 3, 0] = 1
spike_shm_conv = convolve(
    sf_to_sh(spike, k.get_sphere(), sh_order_max=8), k, sh_order_max=8, test_mode=True
)

spike_sf_conv = sh_to_sf(spike_shm_conv, default_sphere, sh_order_max=8)
model_kernel = actor.odf_slicer(
    spike_sf_conv * 6, sphere=default_sphere, norm=False, scale=0.4
)
model_kernel.display(x=3)
scene.add(model_kernel)
scene.set_camera(position=(30, 0, 0), focal_point=(0, 0, 0), view_up=(0, 0, 1))
window.record(scene=scene, out_path="kernel.png", size=(900, 900))
if interactive:
    window.show(scene)

Visualization of the contour enhancement kernel.

Shift-twist convolution is applied on the noisy data

# Perform convolution
csd_shm_enh = convolve(csd_shm_noisy, k, sh_order_max=8)

The Sharpening Deconvolution Transform is applied to sharpen the ODF field.

# Sharpen via the Sharpening Deconvolution Transform

csd_shm_enh_sharp = odf_sh_to_sharp(
    csd_shm_enh, default_sphere, sh_order_max=8, lambda_=0.1
)

# Convert raw and enhanced data to discrete form
csd_sf_orig = sh_to_sf(csd_shm_orig, default_sphere, sh_order_max=8)
csd_sf_noisy = sh_to_sf(csd_shm_noisy, default_sphere, sh_order_max=8)
csd_sf_enh = sh_to_sf(csd_shm_enh, default_sphere, sh_order_max=8)
csd_sf_enh_sharp = sh_to_sf(csd_shm_enh_sharp, default_sphere, sh_order_max=8)

# Normalize the sharpened ODFs
csd_sf_enh_sharp *= np.amax(csd_sf_orig)
csd_sf_enh_sharp /= np.amax(csd_sf_enh_sharp) * 1.25

The end results are visualized. It can be observed that the end result after diffusion and sharpening is closer to the original noiseless dataset.

scene = window.Scene()

# original ODF field
fodf_spheres_org = actor.odf_slicer(
    csd_sf_orig, sphere=default_sphere, scale=0.4, norm=False
)
fodf_spheres_org.display(z=3)
fodf_spheres_org.SetPosition(0, 25, 0)
scene.add(fodf_spheres_org)

# ODF field with added noise
fodf_spheres = actor.odf_slicer(
    csd_sf_noisy,
    sphere=default_sphere,
    scale=0.4,
    norm=False,
)
fodf_spheres.SetPosition(0, 0, 0)
scene.add(fodf_spheres)

# Enhancement of noisy ODF field
fodf_spheres_enh = actor.odf_slicer(
    csd_sf_enh, sphere=default_sphere, scale=0.4, norm=False
)
fodf_spheres_enh.SetPosition(25, 0, 0)
scene.add(fodf_spheres_enh)

# Additional sharpening
fodf_spheres_enh_sharp = actor.odf_slicer(
    csd_sf_enh_sharp, sphere=default_sphere, scale=0.4, norm=False
)
fodf_spheres_enh_sharp.SetPosition(25, 25, 0)
scene.add(fodf_spheres_enh_sharp)

window.record(scene=scene, out_path="enhancements.png", size=(900, 900))
if interactive:
    window.show(scene)

The results after enhancements. Top-left: original noiseless data. Bottom-left: original data with added Rician noise (SNR=10). Bottom-right: After enhancement of noisy data. Top-right: After enhancement and sharpening of noisy data.

References

[1]

Stephan Meesters, Gonzalo Sanguinetti, Eleftherios Garyfallidis, Jorg Portegies, and Remco Duits1. Fast implementations of contextual PDE’s for HARDI data processing in DIPY. In ISMRM 24th Annual Meeting & Exhibition SMRT 25th Annual Meeting, volume. Singapore, May 2016. International Society for Magnetic Resonance in Medicine (ISMRM).

[2] (1,2)

J. M. Jorg M. Portegies, Rutger H. J. Fick, Gonzalo Sanguinetti, Stephan P. L. Meesters, Gabriel Girard, and Remco Duits. Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution. PLOS ONE, 10(10):1–33, October 2015. URL: https://doi.org/10.1371/journal.pone.0138122, doi:10.1371/journal.pone.0138122.

[3]

Remco Duits and Erik Franken. Left-invariant diffusions on the space of positions and orientations and their application to crossing-preserving smoothing of hardi images. International Journal of Computer Vision, 92(3):231–264, 2011. URL: https://doi.org/10.1007/s11263-010-0332-z, doi:10.1007/s11263-010-0332-z.

[4]

Jorg Portegies, Gonzalo Sanguinetti, Stephan Meesters, and Remco Duits. New Approximation of a Scale Space Kernel on SE(3) and Applications in Neuroimaging. In Jean-François Aujol, Mila Nikolova, and Nicolas Papadakis, editors, 5th International Conference on Scale Space and Variational Methods in Computer Vision, 40–52. Cham, 2015. Springer International Publishing.

[5]

Paulo Rodrigues, Remco Duits, Bart M. ter Haar Romeny, and Anna Vilanova. Accelerated Diffusion Operators for Enhancing DW-MRI. In Dirk Bartz, Charl Botha, Joachim Hornegger, Raghu Machiraju, Alexander Wiebel, and Bernhard Preim, editors, Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association, 2010. doi:https://doi.org/10.2312/VCBM/VCBM10/049-056.