634 research outputs found
A proposed search for dark-matter axions in the 0.6-16 micro-eV range
A proposed experiment is described to search for dark matter axions in the mass range 0.6 to 16 micro-eV. The method is based on the Primakoff conversion of axions into monochromatic microwave photons inside a tunable microwave cavity in a large volume high field magnet, as described by Sikivie. This proposal capitalizes on the availability of two Axicell magnets from the decommissioned Mirror Fusion Test Facility (MFTF-B) fusion machine at LLNL. Assuming a local dark matter density in axions of rho = 0.3 GeV/cu cm, the axion would be found or ruled out at the 97 pct. c.l. in the above mass range in 48 months
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Proceeding of the exotic nuclei symposium
This report contains viewgraphs of papers from the proceedings of the Erotic Nuclei Symposium
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Deep-learning-based registration methods emerged as a fast alternative to
conventional registration methods. However, these methods often still cannot
achieve the same performance as conventional registration methods because they
are either limited to small deformation or they fail to handle a superposition
of large and small deformations without producing implausible deformation
fields with foldings inside.
In this paper, we identify important strategies of conventional registration
methods for lung registration and successfully developed the deep-learning
counterpart. We employ a Gaussian-pyramid-based multilevel framework that can
solve the image registration optimization in a coarse-to-fine fashion.
Furthermore, we prevent foldings of the deformation field and restrict the
determinant of the Jacobian to physiologically meaningful values by combining a
volume change penalty with a curvature regularizer in the loss function.
Keypoint correspondences are integrated to focus on the alignment of smaller
structures.
We perform an extensive evaluation to assess the accuracy, the robustness,
the plausibility of the estimated deformation fields, and the transferability
of our registration approach. We show that it achieves state-of-the-art results
on the COPDGene dataset compared to conventional registration method with much
shorter execution time. In our experiments on the DIRLab exhale to inhale lung
registration, we demonstrate substantial improvements (TRE below mm) over
other deep learning methods. Our algorithm is publicly available at
https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/
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