scholarly journals Shearlet-based regularized reconstruction in region-of-interest computed tomography

2018 ◽  
Vol 13 (4) ◽  
pp. 34
Author(s):  
T.A. Bubba ◽  
D. Labate ◽  
G. Zanghirati ◽  
S. Bonettini
Keyword(s):  
Optimization Problem ◽  
Ad Hoc ◽  
Iterative Scheme ◽  
Tomographic Reconstruction ◽  
Region Of Interest ◽  
Projection Data ◽  
Reconstruction Problem ◽  
Convex Optimization Problem ◽  
Scanning Time ◽  
Novel Approach

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, bothad hocanalytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.

scholarly journals A New Algorithm for the Common Solutions of a Generalized Variational Inequality System and a Nonlinear Operator Equation in Banach Spaces

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1944
Author(s):  
Yuanheng Wang ◽  
Cancan Li ◽  
Lirong Lu
Keyword(s):  
Variational Inequality ◽  
Banach Spaces ◽  
Optimization Problem ◽  
Nonlinear Operator ◽  
Iterative Scheme ◽  
Nonlinear Operator Equation ◽  
Convex Optimization Problem ◽  
Generalized Variational Inequality ◽  
Inequality System ◽  
The Common

We study a new algorithm for the common solutions of a generalized variational inequality system and the fixed points of an asymptotically non-expansive mapping in Banach spaces. Under some specific assumptions imposed on the control parameters, some strong convergence theorems for the sequence generated by our new viscosity iterative scheme to approximate their common solutions are proved. As an application of our main results, we solve the standard constrained convex optimization problem. The results here generalize and improve some other authors’ recently corresponding results.

scholarly journals Marker for the pre-clinical development of bone substitute materials

2017 ◽  
Vol 3 (2) ◽  
pp. 711-715
Author(s):  
Michael de Wild ◽  
Simon Zimmermann ◽  
Marcel Obrecht ◽  
Michel Dard
Keyword(s):  
Bone Graft ◽  
Mechanical Stability ◽  
Clinical Performance ◽  
Ex Vivo ◽  
Region Of Interest ◽  
Defect Site ◽  
Novel Approach ◽  
Bone Substitute Materials ◽  
Clinical Experiments

AbstractThin mechanically stable Ti-cages have been developed for the in-vivo application as X-ray and histology markers for the optimized evaluation of pre-clinical performance of bone graft materials. A metallic frame defines the region of interest during histological investigations and supports the identification of the defect site. This standardization of the procedure enhances the quality of pre-clinical experiments. Different models of thin metallic frameworks were designed and produced out of titanium by additive manufacturing (Selective Laser Melting). The productibility, the mechanical stability, the handling and suitability of several frame geometries were tested during surgery in artificial and in ex-vivo bone before a series of cages was preclinically investigated in the female Göttingen minipigs model. With our novel approach, a flexible process was established that can be adapted to the requirements of any specific animal model and bone graft testing.

scholarly journals Inverse reinforcement learning in contextual MDPs

2021 ◽  
Author(s):  
Stav Belogolovsky ◽  
Philip Korsunsky ◽  
Shie Mannor ◽  
Chen Tessler ◽  
Tom Zahavy
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problem ◽  
Decision Processes ◽  
Inverse Reinforcement Learning ◽  
Convex Optimization Problem ◽  
Reward Function ◽  
Dynamic Treatment Regime ◽  
Markov Decision ◽  
Dynamic Treatment ◽  
Recorded Data

AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the reward mapping, such that the agent will act optimally even when encountering previously unseen contexts, also known as zero-shot transfer. We formulate this problem as a non-differential convex optimization problem and propose a novel algorithm to compute its subgradients. Based on this scheme, we analyze several methods both theoretically, where we compare the sample complexity and scalability, and empirically. Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts). Specifically, we present empirical experiments in a dynamic treatment regime, where the goal is to learn a reward function which explains the behavior of expert physicians based on recorded data of them treating patients diagnosed with sepsis.

scholarly journals Dense U-Net for Limited Angle Tomography of Sound Pressure Fields

2021 ◽  
Vol 11 (10) ◽  
pp. 4570
Author(s):  
Oliver Rothkamm ◽  
Johannes Gürtler ◽  
Jürgen Czarske ◽  
Robert Kuschmierz
Keyword(s):  
Measurement Errors ◽  
Sound Pressure ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Projection Data ◽  
Quantitative Measurements ◽  
Pressure Fields ◽  
The Neural Network ◽  
Bias Flow ◽  
3D Information

Tomographic reconstruction allows for the recovery of 3D information from 2D projection data. This commonly requires a full angular scan of the specimen. Angular restrictions that exist, especially in technical processes, result in reconstruction artifacts and unknown systematic measurement errors. We investigate the use of neural networks for extrapolating the missing projection data from holographic sound pressure measurements. A bias flow liner was studied for active sound dampening in aviation. We employed a dense U-Net trained on synthetic data and compared reconstructions of simulated and measured data with and without extrapolation. In both cases, the neural network based approach decreases the mean and maximum measurement deviations by a factor of two. These findings can enable quantitative measurements in other applications suffering from limited angular access as well.

scholarly journals A Novel Approach for Reliable Route Discovery in Mobile Ad-Hoc Network

2015 ◽  
Vol 83 (2) ◽  
pp. 1519-1529 ◽  
Author(s):  
Shariq Mahmood Khan ◽  
R. Nilavalan ◽  
Abdulhafid F. Sallama
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Route Discovery ◽  
Novel Approach ◽  
Mobile Ad Hoc

Extended state observer-based robust non-linear integral dynamic surface control for triaxial MEMS gyroscope

Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 481-501 ◽  
Author(s):  
Mehran Hosseini-Pishrobat ◽  
Jafar Keighobadi
Keyword(s):  
Optimization Problem ◽  
Dynamic Surface Control ◽  
Extended State ◽  
Gain Function ◽  
Dynamic Surface ◽  
Convex Optimization Problem ◽  
Mems Gyroscope ◽  
Surface Control ◽  
Non Linear ◽  
Linear Integral

SUMMARYThis paper reports an extended state observer (ESO)-based robust dynamic surface control (DSC) method for triaxial MEMS gyroscope applications. An ESO with non-linear gain function is designed to estimate both velocity and disturbance vectors of the gyroscope dynamics via measured position signals. Using the sector-bounded property of the non-linear gain function, the design of an $\mathcal{L}_2$-robust ESO is phrased as a convex optimization problem in terms of linear matrix inequalities (LMIs). Next, by using the estimated velocity and disturbance, a certainty equivalence tracking controller is designed based on DSC. To achieve an improved robustness and to remove static steady-state tracking errors, new non-linear integral error surfaces are incorporated into the DSC. Based on the energy-to-peak ($\mathcal{L}_2$-$\mathcal{L}_\infty$) performance criterion, a finite number of LMIs are derived to obtain the DSC gains. In order to prevent amplification of the measurement noise in the DSC error dynamics, a multi-objective convex optimization problem, which guarantees a prescribed $\mathcal{L}_2$-$\mathcal{L}_\infty$ performance bound, is considered. Finally, the efficacy of the proposed control method is illustrated by detailed software simulations.

scholarly journals Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

2021 ◽  
Author(s):  
Chiara Maffei ◽  
Christine Lee ◽  
Michael Planich ◽  
Manisha Ramprasad ◽  
Nivedita Ravi ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Region Of Interest ◽  
Training Data ◽  
Quality Data ◽  
High Quality ◽  
Scanning Time ◽  
Human Connectome Project ◽  
High Gradients ◽  
Comprehensive Comparison

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

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