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A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration
Progressive loss of the field of vision is characteristic of a number of eye diseases
such as glaucoma which is a leading cause of irreversible blindness in the world. Recently,
there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling
the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this
method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results
reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor
positioning and navigation. By using Maxwell's equations, we derive and present
a Bayesian non-parametric probabilistic modeling approach for interpolation and
extrapolation of the magnetic field. We model the magnetic field components
jointly by imposing a Gaussian process (GP) prior on the latent scalar
potential of the magnetic field. By rewriting the GP model in terms of a
Hilbert space representation, we circumvent the computational pitfalls
associated with GP modeling and provide a computationally efficient and
physically justified modeling tool for the ambient magnetic field. The model
allows for sequential updating of the estimate and time-dependent changes in
the magnetic field. The model is shown to work well in practice in different
applications: we demonstrate mapping of the magnetic field both with an
inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
Purpose: To introduce a novel deep learning based approach for fast and
high-quality dynamic multi-coil MR reconstruction by learning a complementary
time-frequency domain network that exploits spatio-temporal correlations
simultaneously from complementary domains.
Theory and Methods: Dynamic parallel MR image reconstruction is formulated as
a multi-variable minimisation problem, where the data is regularised in
combined temporal Fourier and spatial (x-f) domain as well as in
spatio-temporal image (x-t) domain. An iterative algorithm based on variable
splitting technique is derived, which alternates among signal de-aliasing steps
in x-f and x-t spaces, a closed-form point-wise data consistency step and a
weighted coupling step. The iterative model is embedded into a deep recurrent
neural network which learns to recover the image via exploiting spatio-temporal
redundancies in complementary domains.
Results: Experiments were performed on two datasets of highly undersampled
multi-coil short-axis cardiac cine MRI scans. Results demonstrate that our
proposed method outperforms the current state-of-the-art approaches both
quantitatively and qualitatively. The proposed model can also generalise well
to data acquired from a different scanner and data with pathologies that were
not seen in the training set.
Conclusion: The work shows the benefit of reconstructing dynamic parallel MRI
in complementary time-frequency domains with deep neural networks. The method
can effectively and robustly reconstruct high-quality images from highly
undersampled dynamic multi-coil data ( and yielding 15s
and 10s scan times respectively) with fast reconstruction speed (2.8s). This
could potentially facilitate achieving fast single-breath-hold clinical 2D
cardiac cine imaging.Comment: Accepted by Magnetic Resonance in Medicin
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