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Anisotropy

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On the application of hybrid deep 3D convolutional neural network algorithms for predicting the micromechanics of brain white matter.

Computer methods and programs in biomedicine
BACKGROUND: Material characterization of brain white matter (BWM) is difficult due to the anisotropy inherent to the three-dimensional microstructure and the various interactions between heterogeneous brain-tissue (axon, myelin, and glia). Developing...

Machine learning reveals correlations between brain age and mechanics.

Acta biomaterialia
Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and di...

Overcoming the preferred-orientation problem in cryo-EM with self-supervised deep learning.

Nature methods
While advances in single-particle cryo-EM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the 'preferred' orientation problem) remains a complication for most specimens. Existing ...

Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging.

Magnetic resonance imaging
PURPOSE: The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted...

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing.

ACS nano
Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magne...

Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield.

PloS one
Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to n...

A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers.

Magnetic resonance imaging
BACKGROUND: The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening rout...

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning.

Scientific reports
Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusi...

Constitutive neural networks for main pulmonary arteries: discovering the undiscovered.

Biomechanics and modeling in mechanobiology
Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross proce...

Adaptive Vectorial Restoration from Dynamic Speckle Patterns Through Biological Scattering Media Based on Deep Learning.

Sensors (Basel, Switzerland)
Imaging technologies based on vector optical fields hold significant potential in the biomedical field, particularly for non-invasive scattering imaging of anisotropic biological tissues. However, the dynamic and anisotropic nature of biological tiss...