2,263 research outputs found
Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain
In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 mm or smaller but degrades at 2 mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.Published versio
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction
Dimensionality reduction is an essential technique for multi-way large-scale
data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to
its high representation ability and flexibility. However, the traditional TR
decomposition algorithms suffer from high computational cost when facing
large-scale data. In this paper, taking advantages of the recently proposed
tensor random projection method, we propose two TR decomposition algorithms. By
employing random projection on every mode of the large-scale tensor, the TR
decomposition can be processed at a much smaller scale. The simulation
experiment shows that the proposed algorithms are times faster than
traditional algorithms without loss of accuracy, and our algorithms show
superior performance in deep learning dataset compression and hyperspectral
image reconstruction experiments compared to other randomized algorithms.Comment: ICASSP submissio
Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields
In dieser Arbeit werden spektral kodierte multispektrale Lichtfelder untersucht, wie sie von einer Lichtfeldkamera mit einem spektral kodierten Mikrolinsenarray aufgenommen werden. Für die Rekonstruktion der kodierten Lichtfelder werden zwei Methoden entwickelt, eine basierend auf den Prinzipien des Compressed Sensing sowie eine Deep Learning Methode. Anhand neuartiger synthetischer und realer Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail evaluiert
Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields
In this work, spatio-spectrally coded multispectral light fields, as taken by a light field camera with a spectrally coded microlens array, are investigated. For the reconstruction of the coded light fields, two methods, one based on the principles of compressed sensing and one deep learning approach, are developed. Using novel synthetic as well as a real-world datasets, the proposed reconstruction approaches are evaluated in detail
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