42,924 research outputs found
A Bayesian approach for energy-based estimation of acoustic aberrations in high intensity focused ultrasound treatment
High intensity focused ultrasound is a non-invasive method for treatment of
diseased tissue that uses a beam of ultrasound to generate heat within a small
volume. A common challenge in application of this technique is that
heterogeneity of the biological medium can defocus the ultrasound beam. Here we
reduce the problem of refocusing the beam to the inverse problem of estimating
the acoustic aberration due to the biological tissue from acoustic radiative
force imaging data. We solve this inverse problem using a Bayesian framework
with a hierarchical prior and solve the inverse problem using a
Metropolis-within-Gibbs algorithm. The framework is tested using both synthetic
and experimental datasets. We demonstrate that our approach has the ability to
estimate the aberrations using small datasets, as little as 32 sonication
tests, which can lead to significant speedup in the treatment process.
Furthermore, our approach is compatible with a wide range of sonication tests
and can be applied to other energy-based measurement techniques
PEAR: PEriodic And fixed Rank separation for fast fMRI
In functional MRI (fMRI), faster acquisition via undersampling of data can
improve the spatial-temporal resolution trade-off and increase statistical
robustness through increased degrees-of-freedom. High quality reconstruction of
fMRI data from undersampled measurements requires proper modeling of the data.
We present an fMRI reconstruction approach based on modeling the fMRI signal as
a sum of periodic and fixed rank components, for improved reconstruction from
undersampled measurements. We decompose the fMRI signal into a component which
a has fixed rank and a component consisting of a sum of periodic signals which
is sparse in the temporal Fourier domain. Data reconstruction is performed by
solving a constrained problem that enforces a fixed, moderate rank on one of
the components, and a limited number of temporal frequencies on the other. Our
approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI.
Experimental results include purely synthetic simulation, a simulation with
real timecourses and retrospective undersampling of a real fMRI dataset.
Evaluation was performed both quantitatively and visually versus ground truth,
comparing PEAR to two additional recent methods for fMRI reconstruction from
undersampled measurements. Results demonstrate PEAR's improvement in estimating
the timecourses and activation maps versus the methods compared against at
acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real
data). PEAR results in reconstruction with higher fidelity than when using a
fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have
shown that splitting the functional information between the components leads to
better modeling of fMRI, over state-of-the-art methods
Detecting chaos in particle accelerators through the frequency map analysis method
The motion of beams in particle accelerators is dominated by a plethora of
non-linear effects which can enhance chaotic motion and limit their
performance. The application of advanced non-linear dynamics methods for
detecting and correcting these effects and thereby increasing the region of
beam stability plays an essential role during the accelerator design phase but
also their operation. After describing the nature of non-linear effects and
their impact on performance parameters of different particle accelerator
categories, the theory of non-linear particle motion is outlined. The recent
developments on the methods employed for the analysis of chaotic beam motion
are detailed. In particular, the ability of the frequency map analysis method
to detect chaotic motion and guide the correction of non-linear effects is
demonstrated in particle tracking simulations but also experimental data.Comment: Submitted for publication in Chaos, Focus Issue: Chaos Detection
Methods and Predictabilit
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