Outotec Oyj

Instrumentarium Corporation

Ge Healthcare Finland Oy

Rocsole Ltd

Outotec (finland) Oy

Jari Kaipio

Ville Kolehmainen

Samuli Siltanen

Anssi Lehikoinen

Matti Lassas

Marko Vauhkonen

Martti Kalke

Juha Reunanen

Arto Voutilainen

Erkki Somersalo

Antti Nissinen

Jouni Hartikainen

A method for determining a quantity of interest in a target comprises: providing simulated statistics of a deviation, caused by a boundary distortion in observations of the physical quantity; providing an approximate mathematical model of observations of the physical quantity, the approximate mathematical model defining the physical quantity to be dependent on the quantity of interest in the target domain, and on a deviation a boundary distortion causes in the observations, said deviation being determined to behave in accordance with the simulated statistics; receiving measured values of the physical quantity; determining an observation difference between the measured values of the physical quantity and corresponding observations according to the approximate mathematical model, and adjusting the approximate mathematical model to reduce the observation difference; and determining an estimate of the quantity of interest in the target domain on the basis of the adjusted approximate mathematical model.

Method for determining a quantity of interest in a target domain, apparatus, and computer program

The present invention describes a method, apparatus and computer program for detecting the locations of boundaries between different materials in a desired measurement volume. The apparatus uses at least one measuring probe, the electrodes of which are characterized in that they together form an assembly that differs from a straight line. In addition, a volume at a further distance from the assembly can also be observed by the assembly, so that measurements can be conducted remotely and, on the other hand, the apparatus stays unbroken in diverse measurement situations. By using the EIT-measurement and applying for example a so-called 1D-&#x3c3;-method or methods of machine learning, electrical conductivity distributions in the measured volume are detected. As a result, the locations of the possible boundaries between different materials or for example thicknesses of different material layers are detected.

Probe indicating intermaterial boundaries

This invention relates to a medical X-ray device arrangement for producing three-dimensional information of an object in a medical X-ray imaging, the medical X-ray device arrangement comprising an X-ray source for X-radiating the object from at least two different directions and a detector for detecting the X-radiation to form projection data of the object. The medical X-ray device () arrangement comprises means () for utilizing said projection data in a regularized reconstruction method based on solving the non-linear evolution equation&#x2202;&#x3c6;()=&#x2212;()*(((&#x3c6;()))&#x2212;)+&#x3b1;&#x3c6;()to produce three-dimensional information of the object, x representing the coordinates inside the object under imaging, t representing time, &#x3c6; representing a level set function indicating the boundary of the object, h representing a positive-valued function, m representing projection data, the operator A representing the X-radiation attenuation model, A* representing the backprojection operator of A, &#x3b1; representing a positive number, P representing high pass filtering, and f representing a cut-off function returning zero for non-positive arguments and positive values for positive arguments.

Method and arrangement for three-dimensional medical X-ray imaging

The invention relates to a medical X-ray devicearrangement for producing three-dimensional information of an objectin a medical X-ray imaging medical X-ray device arrangement comprising an X-ray sourcefor X-radiating the object from different directions and a detectorfor detecting the X-radiation to form projection data of the object. The medical X-ray devicearrangement comprises: to produce three-dimensional information of the object, the prior distribution p(x) representing mathematical modelling of the object, the object image vector x, which comprise values of the X-ray attenuation coefficient inside the object, m representing projection data, the likelihood distribution p(m|x) representing the X-radiation attenuation model between the object image vector x and projection data m, p(m) being a normalization constant and the posteriori distribution p(x|m) representing the three-dimensional information of the object

Method and arrangement for medical X-ray imaging and reconstruction from sparse data

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Auckland, New Zealand

Jari Kaipio