702 research outputs found
MRI Super-Resolution using Multi-Channel Total Variation
This paper presents a generative model for super-resolution in routine
clinical magnetic resonance images (MRI), of arbitrary orientation and
contrast. The model recasts the recovery of high resolution images as an
inverse problem, in which a forward model simulates the slice-select profile of
the MR scanner. The paper introduces a prior based on multi-channel total
variation for MRI super-resolution. Bias-variance trade-off is handled by
estimating hyper-parameters from the low resolution input scans. The model was
validated on a large database of brain images. The validation showed that the
model can improve brain segmentation, that it can recover anatomical
information between images of different MR contrasts, and that it generalises
well to the large variability present in MR images of different subjects. The
implementation is freely available at https://github.com/brudfors/spm_superre
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific
parameters -- such as the apparent transverse relaxation rate R2*, the
longitudinal relaxation rate R1 and the magnetisation transfer saturation --
that can be compared across sites and scanners and carry important information
about the underlying microstructure. The multi-parameter mapping (MPM) protocol
takes advantage of multi-echo acquisitions with variable flip angles to extract
these parameters in a clinically acceptable scan time. In this context,
ESTATICS performs a joint loglinear fit of multiple echo series to extract R2*
and multiple extrapolated intercepts, thereby improving robustness to motion
and decreasing the variance of the estimators. In this paper, we extend this
model in two ways: (1) by introducing a joint total variation (JTV) prior on
the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a
posteriori} estimate. We evaluated the proposed algorithm by predicting
left-out echoes in a rich single-subject dataset. In this validation, we
outperformed other state-of-the-art methods and additionally showed that the
proposed approach greatly reduces the variance of the estimated maps, without
introducing bias.Comment: 11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI
202
Conservation Agriculture as Practised in Ghana
This case study presents the status of conservation agriculture in Ghana. It is one in a series of eight case studies about conservation agriculture in Africa, which were developed within the framework of a collaboration between CIRAD (French Agricultural Research Centre for International Development), FAO (Food and Agriculture Organization of the United Nations), RELMA-in-ICRAF (Regional Land Management Unit of the World Agroforestry Centre) and ACT (African Conservation Tillage Network)
Factorisation-Based Image Labelling
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts
An Algorithm for Learning Shape and Appearance Models without Annotations
This paper presents a framework for automatically learning shape and
appearance models for medical (and certain other) images. It is based on the
idea that having a more accurate shape and appearance model leads to more
accurate image registration, which in turn leads to a more accurate shape and
appearance model. This leads naturally to an iterative scheme, which is based
on a probabilistic generative model that is fit using Gauss-Newton updates
within an EM-like framework. It was developed with the aim of enabling
distributed privacy-preserving analysis of brain image data, such that shared
information (shape and appearance basis functions) may be passed across sites,
whereas latent variables that encode individual images remain secure within
each site. These latent variables are proposed as features for
privacy-preserving data mining applications.
The approach is demonstrated qualitatively on the KDEF dataset of 2D face
images, showing that it can align images that traditionally require shape and
appearance models trained using manually annotated data (manually defined
landmarks etc.). It is applied to MNIST dataset of handwritten digits to show
its potential for machine learning applications, particularly when training
data is limited. The model is able to handle ``missing data'', which allows it
to be cross-validated according to how well it can predict left-out voxels. The
suitability of the derived features for classifying individuals into patient
groups was assessed by applying it to a dataset of over 1,900 segmented
T1-weighted MR images, which included images from the COBRE and ABIDE datasets.Comment: 61 pages, 16 figures (some downsampled by a factor of 4), submitted
to MedI
Principles of genome evolution in the Drosophila melanogaster species group.
That closely related species often differ by chromosomal inversions was discovered by Sturtevant and Plunkett in 1926. Our knowledge of how these inversions originate is still very limited, although a prevailing view is that they are facilitated by ectopic recombination events between inverted repetitive sequences. The availability of genome sequences of related species now allows us to study in detail the mechanisms that generate interspecific inversions. We have analyzed the breakpoint regions of the 29 inversions that differentiate the chromosomes of Drosophila melanogaster and two closely related species, D. simulans and D. yakuba, and reconstructed the molecular events that underlie their origin. Experimental and computational analysis revealed that the breakpoint regions of 59% of the inversions (17/29) are associated with inverted duplications of genes or other nonrepetitive sequences. In only two cases do we find evidence for inverted repetitive sequences in inversion breakpoints. We propose that the presence of inverted duplications associated with inversion breakpoint regions is the result of staggered breaks, either isochromatid or chromatid, and that this, rather than ectopic exchange between inverted repetitive sequences, is the prevalent mechanism for the generation of inversions in the melanogaster species group. Outgroup analysis also revealed evidence for widespread breakpoint recycling. Lastly, we have found that expression domains in D. melanogaster may be disrupted in D. yakuba, bringing into question their potential adaptive significance
Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation
This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM). but with a more efficient optimisation scheme - both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data. (C) 2011 Elsevier Inc. All rights reserved
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