DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI.

Estimating temperatures with low-cost infrared cameras using physically-constrained deep neural networks.

Low-cost thermal cameras are inaccurate (usually ±3 ) and have space-variant nonuniformity across their detectors. Both inaccuracy and nonuniformity are dependent on the ambient temperature of the camera. The goal of this work was to estimate temperatures with low-cost infrared cameras, and rectify the nonuniformity.

Simultaneous Temperature Estimation and Nonuniformity Correction From Multiple Frames.

IR cameras are widely used for temperature measurements in various applications, including agriculture, medicine, and security. Low-cost IR cameras have the immense potential to replace expensive radiometric cameras in these applications; however, low-cost microbolometer-based IR cameras are prone to spatially variant nonuniformity and to drift in temperature measurements, which limit their usability in practical scenarios. To address these limitations, we propose a novel approach for simultaneous temperature estimation and nonuniformity correction (NUC) from multiple frames captured by low-cost microbolometer-based IR cameras.

Implications of fitting a two-compartment model in single-shell diffusion MRI.

It is becoming increasingly common for studies to fit single-shell diffusion MRI data to a two-compartment model, which comprises a hindered cellular compartment and a freely diffusing isotropic compartment. These studies consistently find that the fraction of the isotropic compartment () is sensitive to white matter (WM) conditions and pathologies, although the actual biological source of changes inhas not been validated. In this work we put aside the biological interpretation ofand study the sensitivity implications of fitting single-shell data to a two-compartment model.

Deep Learning Solution of the Eigenvalue Problem for Differential Operators.

Solving the eigenvalue problem for differential operators is a common problem in many scientific fields. Classical numerical methods rely on intricate domain discretization and yield nonanalytic or nonsmooth approximations. We introduce a novel neural network-based solver for the eigenvalue problem of differential self-adjoint operators, where the eigenpairs are learned in an unsupervised end-to-end fashion.

Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH).

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain.

REPIMPACT - a prospective longitudinal multisite study on the effects of repetitive head impacts in youth soccer.

Repetitive head impacts (RHI) are common in youth athletes participating in contact sports. RHI differ from concussions; they are considered hits to the head that usually do not result in acute symptoms and are therefore also referred to as "subconcussive" head impacts. RHI occur e.

Rapid super resolution for infrared imagery.

Infrared (IR) imagery is used in agriculture for irrigation monitoring and early detection of disease in plants. The common IR cameras in this field typically have low resolution. This work offers a method to obtain the super-resolution of IR images from low-power devices to enhance plant traits.

Single- and double-Diffusion encoding MRI for studying ex vivo apparent axon diameter distribution in spinal cord white matter.

Mapping average axon diameter (AAD) and axon diameter distribution (ADD) in neuronal tissues non-invasively is a challenging task that may have a tremendous effect on our understanding of the normal and diseased central nervous system (CNS). Water diffusion is used to probe microstructure in neuronal tissues, however, the different water populations and barriers that are present in these tissues turn this into a complex task. Therefore, it is not surprising that recently we have witnessed a burst in the development of new approaches and models that attempt to obtain, non-invasively, detailed microstructural information in the CNS.

Joint estimation of unknown radiometric data, gain, and offset from thermal images.

Low cost, weight, and size microbolometer-based thermal focal plane arrays are attractive for thermal-imaging applications. Under environmental loads like those in agricultural remote sensing, these cameras tend to suffer from drift in gain and offset with time and thus require constant calibration. Our goal is to skip this step via computational imaging.

Spectral Analysis of a Non-Equilibrium Stochastic Dynamics on a General Network.

Unravelling underlying complex structures from limited resolution measurements is a known problem arising in many scientific disciplines. We study a stochastic dynamical model with a multiplicative noise. It consists of a stochastic differential equation living on a graph, similar to approaches used in population dynamics or directed polymers in random media.

Radiometric imaging by double exposure and gain calibration.

Due to low cost and small size, uncooled microbolometer-based thermal focal plane arrays are very attractive for radiometry. However, being non-cooled, they suffer from temporally and spatially dependent changes that require constant calibration. While the gain calibration can be reasonably realized by two-point correction, the offset due to internal radiation loads poses a complicated calibration scheme.

Microscopic interpretation and generalization of the Bloch-Torrey equation for diffusion magnetic resonance.

In order to bridge microscopic molecular motion with macroscopic diffusion MR signal in complex structures, we propose a general stochastic model for molecular motion in a magnetic field. The Fokker-Planck equation of this model governs the probability density function describing the diffusion-magnetization propagator. From the propagator we derive a generalized version of the Bloch-Torrey equation and the relation to the random phase approach.

The fiber-density-coreset for redundancy reduction in huge fiber-sets.

State of the art Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) protocols of white matter followed by advanced tractography techniques produce impressive reconstructions of White Matter (WM) pathways. These pathways often contain millions of trajectories (fibers). While for several applications the high number of fibers is essential, other applications (visualization, registration, some types of across-subject comparison) can achieve satisfying results using much smaller sets and may be overburdened by the computational load of the large fiber sets.

Blurred and noisy image pairs in parallel optics.

In previous works we have shown that parallel optics (PO) architecture can be used to improve the system matrix condition, which results in improving its immunity to additive noise in the image restoration process. PO is composed of a "main" system and an "auxiliary" system. Previously, we suggested the "trajectories" method to realize PO.

Microstructural information from angular double-pulsed-field-gradient NMR: From model systems to nerves.

Purpose: To evaluate the ability of angular double-pulsed-field gradient (d-PFG) MR to provide microstructural information in complex phantoms and fixed nerves.

Methods: We modeled the signal in angular d-PFG MR experiments performed on phantoms of increasing complexity where the ground truth is known a priori. After analyzing the microstructural features of such phantoms the same methodology was used to study microstructural features in fixed nerves.

Image formation of thick three-dimensional objects in differential-interference-contrast microscopy.

The differential-interference-contrast (DIC) microscope is of widespread use in life sciences as it enables noninvasive visualization of transparent objects. The goal of this work is to model the image formation process of thick three-dimensional objects in DIC microscopy. The model is based on the principles of electromagnetic wave propagation and scattering.

Modeling of the diffusion MR signal in calibrated model systems and nerves.

Diffusion NMR is a powerful tool for gleaning microstructural information on opaque systems. In this work, the signal decay in single-pulsed-field gradient diffusion NMR experiments performed on a series of phantoms of increasing complexity, where the ground truth is known a priori, was modeled and used to identify microstructural features of these complex phantoms. We were able to demonstrate that, without assuming the number of components or compartments, the modeling can identify the number of restricted components, detect their sizes with an accuracy of a fraction of a micrometer, determine their relative populations, and identify and characterize free diffusion when present in addition to the components exhibiting restricted diffusion.

Measuring small compartments with relatively weak gradients by angular double-pulsed-field-gradient NMR.

NMR diffusion-diffraction patterns observed in compartments in which restricted diffusion occurs are a useful tool for direct extraction of compartment sizes. Such diffusion-diffraction patterns may be observed when the signal intensity E(q,∆) is plotted against the wave-vector q (when q=(2π)(-1)γδG). However, the smaller the compartment sizes are, the higher are the q-values needed to observe such diffractions.

Mapping apparent eccentricity and residual ensemble anisotropy in the gray matter using angular double-pulsed-field-gradient MRI.

Conventional diffusion MRI methods are mostly capable of portraying microarchitectural elements such as fiber orientation in white matter from detection of diffusion anisotropy, which arises from the coherent organization of anisotropic compartments. Double-pulsed-field-gradient MR methods provide a means for obtaining microstructural information such as compartment shape and microscopic anisotropies even in scenarios where macroscopic organization is absent. Here, we apply angular double-pulsed-gradient-spin-echo MRI in the rat brain both ex vivo and in vivo for the first time.

Three dimension optical imaging in a high throughput layered expression system.

Layered peptide array (LPA) system enables multiplex screening of biomarkers [1-3]. One of the main problems of the LPA system is the screening of the layered-membranes stack. Nowadays, each membrane is imaged separately using conventional fluorescent imaging.

Trajectories in parallel optics.

In our previous work we showed the ability to improve the optical system's matrix condition by optical design, thereby improving its robustness to noise. It was shown that by using singular value decomposition, a target point-spread function (PSF) matrix can be defined for an auxiliary optical system, which works parallel to the original system to achieve such an improvement. In this paper, after briefly introducing the all optics implementation of the auxiliary system, we show a method to decompose the target PSF matrix.

Stereo matching with Mumford-Shah regularization and occlusion handling.

This paper addresses the problem of correspondence establishment in binocular stereo vision. We suggest a novel spatially continuous approach for stereo matching based on the variational framework. The proposed method suggests a unique regularization term based on Mumford-Shah functional for discontinuity preserving, combined with a new energy functional for occlusion handling.