Predicting respiratory motion for real-time tumour tracking in radiotherapy

Purpose. Radiation therapy is a local treatment aimed at cells in and around a tumor. The goal of this study is to develop an algorithmic solution for predicting the position of a target in 3D in real time, aiming for the short fixed calibration time for each patient at the beginning of the procedure. Accurate predictions of lung tumor motion are expected to improve the precision of radiation treatment by controlling the position of a couch or a beam in order to compensate for respiratory motion during radiation treatment. Methods. For developing the algorithmic solution, data mining techniques are used. A model form from the family of exponential smoothing is assumed, and the model parameters are fitted by minimizing the absolute disposition error, and the fluctuations of the prediction signal (jitter). The predictive performance is evaluated retrospectively on clinical datasets capturing different behavior (being quiet, talking, laughing), and validated in real-time on a prototype system with respiratory motion imitation. Results. An algorithmic solution for respiratory motion prediction (called ExSmi) is designed. ExSmi achieves good accuracy of prediction (error $4-9$ mm/s) with acceptable jitter values (5-7 mm/s), as tested on out-of-sample data. The datasets, the code for algorithms and the experiments are openly available for research purposes on a dedicated website. Conclusions. The developed algorithmic solution performs well to be prototyped and deployed in applications of radiotherapy

Simulation of the radiation therapy system for respiratory movement compensation

The goal of the radiation therapy is to give as much dose as possible to the target volume of tissue and avoid giving any dose to a healthy tissue. Advances of the digital control allow performing accurate plans and treatments. Unfortunately, motion compensation during the treatment remains a considerable problem. Currently, combination of the different techniques, such as gating (restricting movement of patient) and periodic emission are used to avoid damaging healthy tissue We are interested in systems that completely compensate respiratory movement (up to certain limit) and start by investigating adequacy of the existing hardware and software platform. We model a radiation therapy system consisting of a HexaPOD couch with 6-degrees movement, a tracking camera, a marker (markers) and a controller. Formal un-timed and timed models were defined, analyzed and found to be insufficient to completely determine adequacy of the system to compensate respiratory motion. We define one-dimensional hybrid model of the system using Open Modelica tool and investigate the model with simple tumor movement trajectories, and based on the results we sketch further development directions

PAFSV: A Formal Framework for Specification and Analysis of SystemVerilog

We develop a process algebraic framework PAFSV for the formal specification and analysis of IEEE 1800TM SystemVerilog designs. The formal semantics of PAFSV is defined by means of deduction rules that associate a time transition system with a PAFSV process. A set of properties of PAFSV is presented for a notion of bisimilarity. PAFSV may be regarded as the formal language of a significant subset of IEEE 1800TM SystemVerilog. To show that PAFSV is useful for the formal specification and analysis of IEEE 1800TM SystemVerilog designs, we illustrate the use of PAFSV with a multiplexer, a synchronous reset D flip-flop and an arbiter

A process-algebraic approach to hybrid systems

Process algebra is a theoretical framework for the modelling and analysis of the behaviour of concurrent discrete event systems that has been developed within computer science in past quarter century. It has generated a deeper understanding of the nature of concepts such as observable behaviour in the presence of nondeterminism, system composition by interconnection of concurrent component systems, and notions of behavioural equivalence of such systems. It has contributed fundamental concepts such as bisimulation, and has been successfully used in a wide range of problems and practical applications in concurrent systems. We believe that the basic tenets of process algebra are highly compatible with the behavioural approach to dynamical systems. In our contribution we present an extension of classical process algebra that is suitable for the modelling and analysis of continuous and hybrid dynamical systems. It provides a natural framework for the concurrent composition of such systems, and can deal with nondeterministic behaviour that may arise from the occurrence of internal switching events. Standard process algebraic techniques lead to the characterization of the observable behaviour of such systems as equivalence classes under some suitably adapted notion of bisimulation

ParlaMint: Comparable Corpora of European Parliamentary Data

This paper outlines the ParlaMint project from the perspective of its goals, tasks, participants, results and applications potential. The project produced language corpora from the sessions of the national parliaments of 17 countries, almost half a billion words in total. The corpora are split into COVID-related subcorpora (from November 2019) and reference corpora (to October 2019). The corpora are uniformly encoded according to the ParlaMint schema with the same Universal Dependencies linguistic annotations. Samples of the corpora and conversion scripts are available from the project’s GitHub repository. The complete corpora are openly available via the CLARIN.SI repository1 for download, and through the NoSketch Engine2 and KonText3 concordancers as well as through the Parlameter4 interface for exploration and analysis