Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems

The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we present a flexible framework to extend model-based learning directly to nonuniform meshes, by interpreting the mesh as a graph and formulating our network architectures using graph convolutional neural networks. This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh. We present results for Electrical Impedance Tomography, a severely ill-posed nonlinear inverse problem that is frequently solved via optimization-based methods, where the forward problem is solved by finite element methods. Results for absolute EIT imaging are compared to standard iterative methods as well as a graph residual network. We show that the GCNM has strong generalizability to different domain shapes and meshes, out of distribution data as well as experimental data, from purely simulated training data and without transfer training

On Learned Operator Correction in Inverse Problems

We discuss the possibility of learning a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularized reconstructions. This paper discusses the conceptual difficulty of learning such a forward model correction and proceeds to present a possible solution as a forward-adjoint correction that explicitly corrects in both data and solution spaces. We then derive conditions under which solutions to the variational problem with a learned correction converge to solutions obtained with the correct operator. The proposed approach is evaluated on an application to limited view photoacoustic tomography and compared to the established framework of the Bayesian approximation error method

Лексическое наполнение современных газет российских немцев как реализация этнической функции языка

The purpose of our study was to determine the expression of the pro-apoptotic BAX protein in relation to the mutational status of BAX and p53 (as transcriptional activator of the BAX gene) in benign and malignant thyroid tissue. In 47 patients with thyroid tumours (14 follicular and 3 papillary carcinomas, 14 adenomas and 16 goitres), the DNA was screened for mutations of BAX (exon 1-6) and p53 (exon 5-8) by single-strand conformation polymorphism polymerase chain reaction (SSCP-PCR). Furthermore, the protein expression of BAX, p53 and p21 (which is also increased transcriptionally by p53) was investigated by immunohistochemistry. Surprisingly, we observed elevated BAX levels in patients with thyroid carcinomas compared with patients with adenomas (unpaired t-test: p<0.05) or with goitres (p<0.02). This is in clear contrast to other carcinomas where BAX is frequently inactivated which correlates to a poor prognosis (Sturm et al., 1999). There were no significant differences of the BAX levels between goitres or the adenomas. In the SSCP-PCR analysis, no BAX mutations were detectable. P53 mutation analysis by SSCP-PCR did not reveal any functional p53 mutations in the patients with carcinomas, adenomas or goitres. Nevertheless, patients with carcinomas showed an overexpression (preferentially cytoplasmic) of p53 protein compared with patients with benign tumours (p<0.05). The absence of p53 mutations suggests that the overexpressed p53 is wild type. This is in line with the expression profile of BAX and p21, which showed a higher protein expression in these p53 positive tumours (p<0.05 in the carcinomas compared with the non-malignant lesions). Consequently, the overexpressed p53 might be a correlate for dysregulation without loss of function. This, in turn, might be a reason for the good outcome of some patients with thyroid cancer

Using Key Performance Indicators for traffic management and Intelligent Transport Systems as a prediction tool

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. Focussing on pollution reduction, a tool (CONDUITS-DST) integrating the respective KPIs with microsimulation modelling is developed. The paper describes the integration process, including the model chosen for calculating the emissions levels of a number of scenarios, presents the results of the application to a case study in the city of Brussels, and outlines future developments targeted at broadening the integration of the KPIs into decision-makin

Using Key Performance Indicators for multi-criteria traffic management strategic decisions

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. The first stage of the extension focused on pollution reduction, and a novel decision support tool (CONDUITS_DST) integrating the respective KPI with micro-simulation modelling was developed. Case studies executed in Brussels and Zurich demonstrated the usability and viability of the tool. This paper takes the development one step further and reports on the extension of the approach, which moves from single-criterion to a multi-criteria decision support tool through the inclusion of the KPI on traffic efficiency, again based onmicro-simulation modelling outputs

Development and testing of a predictive traffic safety evaluation tool for road traffic management and ITS impact assessment

In recent research the CONDUITS performance evaluation framework for traffic management and Intelligent Transport Systems (ITS) was developed, consisting of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion. Follow-up work has concentrated on integrating the developed CONDUITS KPIs with microscopic traffic simulation. The outcome has been a predictive evaluation tool for traffic management and ITS, called CONDUITS_DST, in which two of the four KPI categories have been integrated to date: pollution and traffic efficiency. The objective of the present study is to further extend the predictive evaluation framework to include the theme of traffic safety. Contributing to the development of the CONDUITS_DST traffic safety module, the paper identifies and proposes relevant models and metrics linking traffic characteristics with road safety impacts. In doing so, it enables the extraction of the necessary input data for each of the three CONDUITS KPIs for traffic safety (accidents, direct impacts, and indirect impacts) directly from microscopic traffic simulation models. The proposed models and metrics are tested in conjunction with the relevant CONDUITS KPIs for safety using data from simulation models before and after the implementation of a bus priority signalling system in Brussels. Testing takes place both at the network level, but also at the level of individual links, and the results show that the framework is able to capture the expected safety impacts adequately well, paving the way towards its implementation is the traffic safety module of CONDUITS_DST

Convolutional Neural Network for Material Decomposition in Spectral CT Scans

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain.In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10 5 photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100

Deep Learning for Instrumented Ultrasonic Tracking: From synthetic training data to in vivo application

Instrumented ultrasonic tracking is used to improve needle localisation during ultrasound guidance of minimally-invasive percutaneous procedures. Here, it is implemented with transmitted ultrasound pulses from a clinical ultrasound imaging probe that are detected by a fibre-optic hydrophone integrated into a needle. The detected transmissions are then reconstructed to form the tracking image. Two challenges are considered with the current implementation of ultrasonic tracking. First, tracking transmissions are interleaved with the acquisition of B-mode images and thus, the effective B-mode frame rate is reduced. Second, it is challenging to achieve an accurate localisation of the needle tip when the signal-to-noise ratio is low. To address these challenges, we present a framework based on a convolutional neural network (CNN) to maintain spatial resolution with fewer tracking transmissions and to enhance signal quality. A major component of the framework included the generation of realistic synthetic training data. The trained network was applied to unseen synthetic data and experimental in vivo tracking data. The performance of needle localisation was investigated when reconstruction was performed with fewer (up to eight-fold) tracking transmissions. CNN-based processing of conventional reconstructions showed that the axial and lateral spatial resolution could be improved even with an eight-fold reduction in tracking transmissions. The framework presented in this study will significantly improve the performance of ultrasonic tracking, leading to faster image acquisition rates and increased localisation accuracy