797 research outputs found
Paraxial diffusion-field retrieval
Unresolved spatially-random microstructure, in an illuminated sample, can
lead to position-dependent blur when an image of that sample is taken using an
incoherent imaging system. For a small propagation distance, between the exit
surface of the sample and the entrance surface of a position-sensitive
detector, the paraxial approximation implies that the blurring influence of the
sample may be modeled using an anomalous-diffusion field. This diffusion field
may have a scalar or tensor character, depending on whether the random
microstructure has an autocorrelation function that is rotationally isotropic
or anisotropic, respectively. Partial differential equations are written down
and then solved, in a closed-form manner, for several variants of the inverse
problem of diffusion-field retrieval given suitable intensity images. Both
uniform-illumination and structured-illumination schemes are considered. Links
are made, between the recovered diffusion field and certain statistical
properties of the unresolved microstructure. The developed theory -- which may
be viewed as a crudely parallel form of small-angle scattering under the
Guinier approximation -- is applicable to a range of paraxial radiation and
matter fields, such as visible light, x rays, neutrons, and electrons
Estimating uncertainty in multiple fibre reconstructions
Diffusion magnetic resonance imaging (MRI) is a technique that allows us to probe
the microstructure of materials. The standard technique in diffusion MRI is diffusion
tensor imaging (DTI). However, DTI can only model a single fibre orientation and
fails in regions of complex microstructure. Multiple-fibre algorithms aim to overcome
this limitation of DTI, but there remain many questions about which multiple-fibre
algorithms are most promising and how best to exploit them in tractography.
This work focuses on exploring the potential of multiple-fibre reconstructions and
preparing them for transfer to the clinical arena. We provide a standardised framework
for comparing multiple-fibre algorithms and use it for a robust comparison of standard
algorithms, such as persistent angular structure (PAS) MRI, spherical deconvolution
(SD), maximum entropy SD (MESD), constrained SD (CSD) and QBall. An output
of this framework is the parameter settings of the algorithms that maximise the consistency
of reconstructions. We show that non-linear algorithms, and CSD in particular,
provide the most consistent reconstructions.
Next, we investigate features of the reconstructions that can be exploited to improve
tractography. We show that the peak shapes of multiple-fibre reconstructions
can be used to predict anisotropy in the uncertainty of fibre-orientation estimates. We
design an experiment that exploits this information in the probabilistic index of connectivity
(PICo) tractography algorithm. We then compare PICo tractography results
using information about peak shape and sharpness to estimate uncertainty with PICo
results using only the peak sharpness to estimate uncertainty and show structured differences.
The final contribution of this work is a robust algorithm for calibrating PICo
that overcomes some of the limitations of the original algorithm. We finish with some
early exploratory work that aims to estimate the distribution of fibre-orientations in a
voxel using features of the reconstruction
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Representing graphs as sets of node embeddings in certain curved Riemannian
manifolds has recently gained momentum in machine learning due to their
desirable geometric inductive biases, e.g., hierarchical structures benefit
from hyperbolic geometry. However, going beyond embedding spaces of constant
sectional curvature, while potentially more representationally powerful, proves
to be challenging as one can easily lose the appeal of computationally
tractable tools such as geodesic distances or Riemannian gradients. Here, we
explore computationally efficient matrix manifolds, showcasing how to learn and
optimize graph embeddings in these Riemannian spaces. Empirically, we
demonstrate consistent improvements over Euclidean geometry while often
outperforming hyperbolic and elliptical embeddings based on various metrics
that capture different graph properties. Our results serve as new evidence for
the benefits of non-Euclidean embeddings in machine learning pipelines.Comment: Submitted to the Thirty-fourth Conference on Neural Information
Processing System
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