68,672 research outputs found
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks
Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part
of industrial quality control, materials science and medical imaging. The need
to acquire and process each scan in a short time naturally leads to trade-offs
between speed and reconstruction quality, creating a need for fast
reconstruction algorithms capable of creating accurate reconstructions from
limited data.
In this paper we introduce the Neural Network Feldkamp-Davis-Kress (NN-FDK)
algorithm. This algorithm adds a machine learning component to the FDK
algorithm to improve its reconstruction accuracy while maintaining its
computational efficiency. Moreover, the NN-FDK algorithm is designed such that
it has low training data requirements and is fast to train. This ensures that
the proposed algorithm can be used to improve image quality in high throughput
CT scanning settings, where FDK is currently used to keep pace with the
acquisition speed using readily available computational resources.
We compare the NN-FDK algorithm to two standard CT reconstruction algorithms
and to two popular deep neural networks trained to remove reconstruction
artifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDK
reconstruction algorithm is substantially faster in computing a reconstruction
than all the tested alternative methods except for the standard FDK algorithm
and we show it can compute accurate CCB CT reconstructions in cases of high
noise, a low number of projection angles or large cone angles. Moreover, we
show that the training time of an NN-FDK network is orders of magnitude lower
than the considered deep neural networks, with only a slight reduction in
reconstruction accuracy
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation
While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown
to have better image quality than Filtered Back Projection (FBP), its use has
been limited by its high computational cost. More recently, deep convolutional
neural networks (CNN) have shown great promise in both denoising and
reconstruction applications. In this research, we propose a fast reconstruction
algorithm, which we call Deep Learning MBIR (DL-MBIR), for approximating MBIR
using a deep residual neural network. The DL-MBIR method is trained to produce
reconstructions that approximate true MBIR images using a 16 layer residual
convolutional neural network implemented on multiple GPUs using Google
Tensorflow. In addition, we propose 2D, 2.5D and 3D variations on the DL-MBIR
method and show that the 2.5D method achieves similar quality to the fully 3D
method, but with reduced computational cost.Comment: IEEE Asilomar conference on signals systems and computers, 201
Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection
X-ray imaging is an important tool for quality control since it allows to inspect the interior of products in a non-destructive way. Conventional x-ray imaging, however, is slow and expensive. Inline x-ray inspection, on the other hand, can pave the way towards fast and individual quality control, provided that a sufficiently high throughput can be achieved at a minimal cost. To meet these criteria, an inline inspection acquisition geometry is proposed where the object moves and rotates on a conveyor belt while it passes a fixed source and detector. Moreover, for this acquisition geometry, a new neural-network-based reconstruction algorithm is introduced: the neural network Hilbert transform based filtered backprojection. The proposed algorithm is evaluated both on simulated and real inline x-ray data and has shown to generate high quality reconstructions of 400 x 400 reconstruction pixels within 200 ms, thereby meeting the high throughput criteria
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
Temperature- and Time-Dependent Dielectric Measurements and Modelling on Curing of Polymer Composites
In this book a test set for dielectric measurements at 2.45 GHz during curing of polymer composites is developed. Fast reconstruction is solved using a neural network algorithm. Modeling of the curing process at 2.45 GHz using both dielectric constant and dielectric loss factor results in more accurate model compared to low frequency modelling that only uses the loss factor. Effect of various hardeners and different amount of filler is investigated
Temperature- and Time-Dependent Dielectric Measurements and Modelling on Curing of Polymer Composites
In this book a test set for dielectric measurements at 2.45 GHz during curing of polymer composites is developed. Fast reconstruction is solved using a neural network algorithm. Modeling of the curing process at 2.45 GHz using both dielectric constant and dielectric loss factor results in more accurate model compared to low frequency modelling that only uses the loss factor. Effect of various hardeners and different amount of filler is investigated
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