13,450 research outputs found
Passively mode-locked laser using an entirely centred erbium-doped fiber
This paper describes the setup and experimental results for an entirely centred erbium-doped fiber laser with passively mode-locked output. The gain medium of the ring laser cavity configuration comprises a 3 m length of two-core optical fiber, wherein an undoped outer core region of 9.38 μm diameter surrounds a 4.00 μm diameter central core region doped with erbium ions at 400 ppm concentration. The generated stable soliton mode-locking output has a central wavelength of 1533 nm and pulses that yield an average output power of 0.33 mW with a pulse energy of 31.8 pJ. The pulse duration is 0.7 ps and the measured output repetition rate of 10.37 MHz corresponds to a 96.4 ns pulse spacing in the pulse train
Mapping hybrid functional-structural connectivity traits in the human connectome
One of the crucial questions in neuroscience is how a rich functional
repertoire of brain states relates to its underlying structural organization.
How to study the associations between these structural and functional layers is
an open problem that involves novel conceptual ways of tackling this question.
We here propose an extension of the Connectivity Independent Component Analysis
(connICA) framework, to identify joint structural-functional connectivity
traits. Here, we extend connICA to integrate structural and functional
connectomes by merging them into common hybrid connectivity patterns that
represent the connectivity fingerprint of a subject. We test this extended
approach on the 100 unrelated subjects from the Human Connectome Project. The
method is able to extract main independent structural-functional connectivity
patterns from the entire cohort that are sensitive to the realization of
different tasks. The hybrid connICA extracted two main task-sensitive hybrid
traits. The first, encompassing the within and between connections of dorsal
attentional and visual areas, as well as fronto-parietal circuits. The second,
mainly encompassing the connectivity between visual, attentional, DMN and
subcortical networks. Overall, these findings confirms the potential ofthe
hybrid connICA for the compression of structural/functional connectomes into
integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5
figure
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization
Spherical deconvolution (SD) methods are widely used to estimate the
intra-voxel white-matter fiber orientations from diffusion MRI data. However,
while some of these methods assume a zero-mean Gaussian distribution for the
underlying noise, its real distribution is known to be non-Gaussian and to
depend on the methodology used to combine multichannel signals. Indeed, the two
prevailing methods for multichannel signal combination lead to Rician and
noncentral Chi noise distributions. Here we develop a Robust and Unbiased
Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with
realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to
Rician and noncentral Chi likelihood models. To quantify the benefits of using
proper noise models, RUMBA-SD was compared with dRL-SD, a well-established
method based on the RL algorithm for Gaussian noise. Another aim of the study
was to quantify the impact of including a total variation (TV) spatial
regularization term in the estimation framework. To do this, we developed TV
spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The
evaluation was performed by comparing various quality metrics on 132
three-dimensional synthetic phantoms involving different inter-fiber angles and
volume fractions, which were contaminated with noise mimicking patterns
generated by data processing in multichannel scanners. The results demonstrate
that the inclusion of proper likelihood models leads to an increased ability to
resolve fiber crossings with smaller inter-fiber angles and to better detect
non-dominant fibers. The inclusion of TV regularization dramatically improved
the resolution power of both techniques. The above findings were also verified
in brain data
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Simulated design strategies for SPECT collimators to reduce the eddy currents induced by MRI gradient fields
Combining single photon emission computed tomography (SPECT) with magnetic resonance imaging (MRI) requires the insertion of highly conductive SPECT collimators inside the MRI scanner, resulting in an induced eddy current disturbing the combined system. We reduced the eddy currents due to the insert of a novel tungsten collimator inside transverse and longitudinal gradient coils. The collimator was produced with metal additive manufacturing, that is part of a microSPECT insert for a preclinical SPECT/MRI scanner. We characterized the induced magnetic field due to the gradient field and adapted the collimators to reduce the induced eddy currents. We modeled the x-, y-, and z-gradient coil and the different collimator designs and simulated them with FEKO, a three-dimensional method of moments / finite element methods (MoM/FEM) full-wave simulation tool. We used a time analysis approach to generate the pulsed magnetic field gradient. Simulation results show that the maximum induced field can be reduced by 50.82% in the final design bringing the maximum induced magnetic field to less than 2% of the applied gradient for all the gradient coils. The numerical model was validated with measurements and was proposed as a tool for studying the effect of a SPECT collimator within the MRI gradient coils
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