392 research outputs found
Learning-based Ensemble Average Propagator Estimation
By capturing the anisotropic water diffusion in tissue, diffusion magnetic
resonance imaging (dMRI) provides a unique tool for noninvasively probing the
tissue microstructure and orientation in the human brain. The diffusion profile
can be described by the ensemble average propagator (EAP), which is inferred
from observed diffusion signals. However, accurate EAP estimation using the
number of diffusion gradients that is clinically practical can be challenging.
In this work, we propose a deep learning algorithm for EAP estimation, which is
named learning-based ensemble average propagator estimation (LEAPE). The EAP is
commonly represented by a basis and its associated coefficients, and here we
choose the SHORE basis and design a deep network to estimate the coefficients.
The network comprises two cascaded components. The first component is a
multiple layer perceptron (MLP) that simultaneously predicts the unknown
coefficients. However, typical training loss functions, such as mean squared
errors, may not properly represent the geometry of the possibly non-Euclidean
space of the coefficients, which in particular causes problems for the
extraction of directional information from the EAP. Therefore, to regularize
the training, in the second component we compute an auxiliary output of
approximated fiber orientation (FO) errors with the aid of a second MLP that is
trained separately. We performed experiments using dMRI data that resemble
clinically achievable -space sampling, and observed promising results
compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis
and treatment. However, variations in MRI acquisition protocols result in
different appearances of normal and diseased tissue in the images.
Convolutional neural networks (CNNs), which have shown to be successful in many
medical image analysis tasks, are typically sensitive to the variations in
imaging protocols. Therefore, in many cases, networks trained on data acquired
with one MRI protocol, do not perform satisfactorily on data acquired with
different protocols. This limits the use of models trained with large annotated
legacy datasets on a new dataset with a different domain which is often a
recurring situation in clinical settings. In this study, we aim to answer the
following central questions regarding domain adaptation in medical image
analysis: Given a fitted legacy model, 1) How much data from the new domain is
required for a decent adaptation of the original network?; and, 2) What portion
of the pre-trained model parameters should be retrained given a certain number
of the new domain training samples? To address these questions, we conducted
extensive experiments in white matter hyperintensity segmentation task. We
trained a CNN on legacy MR images of brain and evaluated the performance of the
domain-adapted network on the same task with images from a different domain. We
then compared the performance of the model to the surrogate scenarios where
either the same trained network is used or a new network is trained from
scratch on the new dataset.The domain-adapted network tuned only by two
training examples achieved a Dice score of 0.63 substantially outperforming a
similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure
Fast Optimal Transport Averaging of Neuroimaging Data
Knowing how the Human brain is anatomically and functionally organized at the
level of a group of healthy individuals or patients is the primary goal of
neuroimaging research. Yet computing an average of brain imaging data defined
over a voxel grid or a triangulation remains a challenge. Data are large, the
geometry of the brain is complex and the between subjects variability leads to
spatially or temporally non-overlapping effects of interest. To address the
problem of variability, data are commonly smoothed before group linear
averaging. In this work we build on ideas originally introduced by Kantorovich
to propose a new algorithm that can average efficiently non-normalized data
defined over arbitrary discrete domains using transportation metrics. We show
how Kantorovich means can be linked to Wasserstein barycenters in order to take
advantage of an entropic smoothing approach. It leads to a smooth convex
optimization problem and an algorithm with strong convergence guarantees. We
illustrate the versatility of this tool and its empirical behavior on
functional neuroimaging data, functional MRI and magnetoencephalography (MEG)
source estimates, defined on voxel grids and triangulations of the folded
cortical surface.Comment: Information Processing in Medical Imaging (IPMI), Jun 2015, Isle of
Skye, United Kingdom. Springer, 201
Diversity of cortico-descending projections: histological and diffusion MRI characterization in the monkey
The axonal composition of cortical projections originating in premotor, supplementary motor (SMA), primary motor (a4), somatosensory and parietal areas and descending towards the brain stem and spinal cord was characterized in the monkey with histological tract tracing, electron microscopy (EM) and diffusion MRI (dMRI). These 3 approaches provided complementary information. Histology provided accurate assessment of axonal diameters and size of synaptic boutons. dMRI revealed the topography of the projections (tractography), notably in the internal capsule. From measurements of axon diameters axonal conduction velocities were computed. Each area communicates with different diameter axons and this generates a hierarchy of conduction delays in this order: a4 (the shortest), SMA, premotor (F7), parietal, somatosensory, premotor F4 (the longest). We provide new interpretations for i) the well-known different anatomical and electrophysiological estimates of conduction velocity; ii) why conduction delays are probably an essential component of the cortical motor command; and iii) how histological and dMRI tractography can be integrated
Finsler geometry on higher order tensor fields and applications to high angular resolution diffusion imaging.
We study 3D-multidirectional images, using Finsler geometry. The application considered here is in medical image analysis, specifically in High Angular Resolution Diffusion Imaging (HARDI) (Tuch et al. in Magn. Reson. Med. 48(6):1358â1372, 2004) of the brain. The goal is to reveal the architecture of the neural fibers in brain white matter. To the variety of existing techniques, we wish to add novel approaches that exploit differential geometry and tensor calculus. In Diffusion Tensor Imaging (DTI), the diffusion of water is modeled by a symmetric positive definite second order tensor, leading naturally to a Riemannian geometric framework. A limitation is that it is based on the assumption that there exists a single dominant direction of fibers restricting the thermal motion of water molecules. Using HARDI data and higher order tensor models, we can extract multiple relevant directions, and Finsler geometry provides the natural geometric generalization appropriate for multi-fiber analysis. In this paper we provide an exact criterion to determine whether a spherical function satisfies the strong convexity criterion essential for a Finsler norm. We also show a novel fiber tracking method in Finsler setting. Our model incorporates a scale parameter, which can be beneficial in view of the noisy nature of the data. We demonstrate our methods on analytic as well as simulated and real HARDI data
Estimation of Fiber Orientations Using Neighborhood Information
Data from diffusion magnetic resonance imaging (dMRI) can be used to
reconstruct fiber tracts, for example, in muscle and white matter. Estimation
of fiber orientations (FOs) is a crucial step in the reconstruction process and
these estimates can be corrupted by noise. In this paper, a new method called
Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is
described and shown to reduce the effects of noise and improve FO estimation
performance by incorporating spatial consistency. FORNI uses a fixed tensor
basis to model the diffusion weighted signals, which has the advantage of
providing an explicit relationship between the basis vectors and the FOs. FO
spatial coherence is encouraged using weighted l1-norm regularization terms,
which contain the interaction of directional information between neighbor
voxels. Data fidelity is encouraged using a squared error between the observed
and reconstructed diffusion weighted signals. After appropriate weighting of
these competing objectives, the resulting objective function is minimized using
a block coordinate descent algorithm, and a straightforward parallelization
strategy is used to speed up processing. Experiments were performed on a
digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data
for both qualitative and quantitative evaluation. The results demonstrate that
FORNI improves the quality of FO estimation over other state of the art
algorithms.Comment: Journal paper accepted in Medical Image Analysis. 35 pages and 16
figure
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
We present DeepTract, a deep-learning framework for estimating white matter
fibers orientation and streamline tractography. We adopt a data-driven approach
for fiber reconstruction from diffusion weighted images (DWI), which does not
assume a specific diffusion model. We use a recurrent neural network for
mapping sequences of DWI values into probabilistic fiber orientation
distributions. Based on these estimations, our model facilitates both
deterministic and probabilistic streamline tractography. We quantitatively
evaluate our method using the Tractometer tool, demonstrating competitive
performance with state-of-the art classical and machine learning based
tractography algorithms. We further present qualitative results of
bundle-specific probabilistic tractography obtained using our method. The code
is publicly available at: https://github.com/itaybenou/DeepTract.git
Transcriptomic Signature of Leishmania Infected Mice Macrophages: A Metabolic Point of View
We analyzed the transcriptional signatures of mouse bone marrow-derived macrophages at different times after infection with promastigotes of the protozoan parasite Leishmania major. Ingenuity Pathway Analysis revealed that the macrophage metabolic pathways including carbohydrate and lipid metabolisms were among the most altered pathways at later time points of infection. Indeed, L. major promastiogtes induced increased mRNA levels of the glucose transporter and almost all of the genes associated with glycolysis and lactate dehydrogenase, suggesting a shift to anaerobic glycolysis. On the other hand, L. major promastigotes enhanced the expression of scavenger receptors involved in the uptake of Low-Density Lipoprotein (LDL), inhibited the expression of genes coding for proteins regulating cholesterol efflux, and induced the synthesis of triacylglycerides. These data suggested that Leishmania infection disturbs cholesterol and triglycerides homeostasis and may lead to cholesterol accumulation and foam cell formation. Using Filipin and Bodipy staining, we showed cholesterol and triglycerides accumulation in infected macrophages. Moreover, Bodipy-positive lipid droplets accumulated in close proximity to parasitophorous vacuoles, suggesting that intracellular L. major may take advantage of these organelles as high-energy substrate sources. While the effect of infection on cholesterol accumulation and lipid droplet formation was independent on parasite development, our data indicate that anaerobic glycolysis is actively induced by L. major during the establishment of infection
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