Accurate and fast two-step phase shifting algorithm based on principle component analysis and Lissajous ellipse fitting with random phase shift and no pre-filtering

To achieve high measurement accuracy with less computational time-in-phase shifting interferometry, a random phase-shifting algorithm based on principal component analysis and Lissajous ellipse fitting (PCA& LEF) is proposed. It doesn't need pre-filtering and can obtain relatively accurate phase distribution with only two phase shifted interferograms and less computational time and is suitable for different background intensity, modulation amplitude distributions and noises. Moreover, it can obtain absolutely accurate result when the background intensity and modulation amplitude are perfect and can partly suppress the effect of imperfect background intensity and modulation amplitude. Last but not least, it removes the restriction that PCA needs more than three interferograms with welldistributed phase shifts to subtract relatively accurate mean. The simulations and experiments verify the correctness and feasibility of PCA& LEF. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing AgreementNational Natural Science Foundation of China (NSFC) [11304034]; Department of Science and Technology of Jilin Province [20190701018GH]; Education Department of Jilin Province [JJKH20190691KJ]; State Key Laboratory of Applied OpticsOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

DeDA: Deep Directed Accumulator

Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. These rim+ lesions exhibit a geometrically simple structure, where gradients at the lesion edge are radially oriented and a greater magnitude of gradients is observed in contrast to rim- (non rim+) lesions. However, recent studies have shown that the identification performance of such lesions remains unsatisfied due to the limited amount of data and high class imbalance. In this paper, we propose a simple yet effective image processing operation, deep directed accumulator (DeDA), that provides a new perspective for injecting domain-specific inductive biases (priors) into neural networks for rim+ lesion identification. Given a feature map and a set of sampling grids, DeDA creates and quantizes an accumulator space into finite intervals, and accumulates feature values accordingly. This DeDA operation is a generalized discrete Radon transform and can also be regarded as a symmetric operation to the grid sampling within the forward-backward neural network framework, the process of which is order-agnostic, and can be efficiently implemented with the native CUDA programming. Experimental results on a dataset with 177 rim+ and 3986 rim- lesions show that 10.1% of improvement in a partial (false positive rate<0.1) area under the receiver operating characteristic curve (pROC AUC) and 10.2% of improvement in an area under the precision recall curve (PR AUC) can be achieved respectively comparing to other state-of-the-art methods. The source code is available online at https://github.com/tinymilky/DeDAComment: 18 pages, 3 Tables and 4 figure

Fiber bundle imaging resolution enhancement using deep learning

We propose a deep learning based method to estimate high-resolution images from multiple fiber bundle images. Our approach first aligns raw fiber bundle image sequences with a motion estimation neural network and then applies a 3D convolution neural network to learn a mapping from aligned fiber bundle image sequences to their ground truth images. Evaluations on lens tissue samples and a 1951 USAF resolution target suggest that our proposed method can significantly improve spatial resolution for fiber bundle imaging systems.National Institute of Biomedical Imaging and Bioengineering (NIBIB) [R21EB022378]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Spatially Covariant Lesion Segmentation

Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks. In this paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve the computational efficiency and meantime maintain or increase accuracy for lesion segmentation. SCP relaxes the spatial invariance constraint imposed by convolutional operations and optimizes an underlying implicit function that maps image coordinates to network weights, the parameters of which are obtained along with the backbone network training and later used for generating network weights to capture spatially covariant contextual information. We demonstrate the effectiveness and efficiency of the proposed SCP using two lesion segmentation tasks from different imaging modalities: white matter hyperintensity segmentation in magnetic resonance imaging and liver tumor segmentation in contrast-enhanced abdominal computerized tomography. The network using SCP has achieved 23.8%, 64.9% and 74.7% reduction in GPU memory usage, FLOPs, and network size with similar or better accuracy for lesion segmentation.Comment: 9 pages, 7 figures, and 2 table

Structures of Sortase B from Staphylococcus aureus and Bacillus anthracis Reveal Catalytic Amino Acid Triad in the Active Site

Surface proteins attached by sortases to the cell wall envelope of bacterial pathogens play important roles during infection. Sorting and attachment of these proteins is directed by C-terminal signals. Sortase B of S. aureus recognizes a motif NPQTN, cleaves the polypeptide after the Thr residue, and attaches the protein to pentaglycine cross-bridges. Sortase B of B. anthracis is thought to recognize the NPKTG motif, and attaches surface proteins to m-diaminopimelic acid cross-bridges. We have determined crystal structure of sortase B from B. anthracis and S. aureus at 1.6 and 2.0 Å resolutions, respectively. These structures show a β-barrel fold with α-helical elements on its outside, a structure thus far exclusive to the sortase family. A putative active site located on the edge of the β-barrel is comprised of a Cys-His-Asp catalytic triad and presumably faces the bacterial cell surface. A putative binding site for the sorting signal is located nearby

Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

The interferometry technique is corn commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 27 pi ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.China Scholarship Council (CSC) [201704910730]; National Science Foundation (NSF) [1455630]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation

Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture global contextual information for performance enhancement. However, the large size of 3D volume images poses a great computational challenge to traditional attention methods. In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images. The main idea is that we apply tensor folding and unfolding operations with four permutations to build four small sub-affinity matrices to approximate the original affinity matrix. Through four consecutive sub-attention modules of FA, each element in the feature tensor can aggregate spatial-channel information from all other elements. Compared to traditional attention methods, with moderate improvement of accuracy, FA can substantially reduce the computational complexity and GPU memory consumption. We demonstrate the superiority of our method on two challenging tasks for 3D MIR and MIS, which are quantitative susceptibility mapping and multiple sclerosis lesion segmentation.Comment: 9 pages, 7 figure

New testing and calculation method for determination viscoelasticity of optical glass

Viscoelastic properties of glass within molding temperatures, such as shear relaxation modulus and bulk relaxation modulus, are key factors to build successful numerical model, predict forming process, and determine optimal process parameters for precision glass molding. However, traditional uniaxial compression creep tests with large strains are very limited in obtaining high-accuracy viscoelastic data of glass, due to the declining compressive stress caused by the increasing cross-sectional area of specimen in testing process. Besides, existing calculation method has limitation in transforming creep data to viscoelasticity data, especially when Poisson's ratio is unknown at molding temperature, which further induces a block to characterize viscoelastic parameter. This study proposes a systematic acquisition method tbr high-precision viscoelastic data, including creep testing, viscoelasticity calculation, and finite element verification. A minimal uniaxial creep testing (MUCT) method based on thermo-mechanical analysis (TMA) instrument is first built to obtain ideal and accurate creep data, by keeping compressive stress as a constant. A new calculation method on viscoelasticity determination is then proposed to derive shear relaxation modulus without the need of knowing bulk modulus or Poisson's ratio, which, compared with traditional method, extends the application range of viscoelasticity calculation. After determination, the obtained viscoelastic data are further incorporated into a numerical simulation model of MUCT to verify the accuracy of the determined viscoelasticity. Base on the great consistence between simulated and measured results (uniaxial creep displacement), the proposed systematic acquisition method can be used as a high accuracy viscoelasticity determination method.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]