82,195 research outputs found
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
Band gap prediction for large organic crystal structures with machine learning
Machine-learning models are capable of capturing the structure-property
relationship from a dataset of computationally demanding ab initio
calculations. Over the past two years, the Organic Materials Database (OMDB)
has hosted a growing number of calculated electronic properties of previously
synthesized organic crystal structures. The complexity of the organic crystals
contained within the OMDB, which have on average 82 atoms per unit cell, makes
this database a challenging platform for machine learning applications. In this
paper, the focus is on predicting the band gap which represents one of the
basic properties of a crystalline materials. With this aim, a consistent
dataset of 12 500 crystal structures and their corresponding DFT band gap are
released, freely available for download at https://omdb.mathub.io/dataset. An
ensemble of two state-of-the-art models reach a mean absolute error (MAE) of
0.388 eV, which corresponds to a percentage error of 13% for an average band
gap of 3.05 eV. Finally, the trained models are employed to predict the band
gap for 260 092 materials contained within the Crystallography Open Database
(COD) and made available online so that the predictions can be obtained for any
arbitrary crystal structure uploaded by a user.Comment: 10 pages, 6 figure
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