5,364 research outputs found
Iterative algorithms for total variation-like reconstructions in seismic tomography
A qualitative comparison of total variation like penalties (total variation,
Huber variant of total variation, total generalized variation, ...) is made in
the context of global seismic tomography. Both penalized and constrained
formulations of seismic recovery problems are treated. A number of simple
iterative recovery algorithms applicable to these problems are described. The
convergence speed of these algorithms is compared numerically in this setting.
For the constrained formulation a new algorithm is proposed and its convergence
is proven.Comment: 28 pages, 8 figures. Corrected sign errors in formula (25
Radon needlet thresholding
We provide a new algorithm for the treatment of the noisy inversion of the
Radon transform using an appropriate thresholding technique adapted to a
well-chosen new localized basis. We establish minimax results and prove their
optimality. In particular, we prove that the procedures provided here are able
to attain minimax bounds for any loss. It s important to notice
that most of the minimax bounds obtained here are new to our knowledge. It is
also important to emphasize the adaptation properties of our procedures with
respect to the regularity (sparsity) of the object to recover and to
inhomogeneous smoothness. We perform a numerical study that is of importance
since we especially have to discuss the cubature problems and propose an
averaging procedure that is mostly in the spirit of the cycle spinning
performed for periodic signals
Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection
We propose a new model-free segmentation method, JULES, which combines recent
statistical multiresolution techniques with local deconvolution for
idealization of ion channel recordings. The multiresolution criterion takes
into account scales down to the sampling rate enabling the detection of
flickering events, i.e., events on small temporal scales, even below the filter
frequency. For such small scales the deconvolution step allows for a precise
determination of dwell times and, in particular, of amplitude levels, a task
which is not possible with common thresholding methods. This is confirmed
theoretically and in a comprehensive simulation study. In addition, JULES can
be applied as a preprocessing method for a refined hidden Markov analysis. Our
new methodolodgy allows us to show that gramicidin A flickering events have the
same amplitude as the slow gating events. JULES is available as an R function
jules in the package clampSeg
Test-retest reliability of structural brain networks from diffusion MRI
Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability
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