Abstract: The present invention relates to an apparatus and method for clinical decision support to identify patients at high risk of thrombosis based on a combination of clinical risk factors and molecular markers, e.g., protein concentrations. These clinical risk factors and molecular markers are combined in a machine learning based algorithm which returns an output value, relating to an estimated risk of a thrombosis event in the future.

Abstract: The present invention provides for a clinical decision support system which makes use of a numerical model which dynamically describes a blood dilution of a blood circulation. Based on measured coagulation data and maybe other patient information, loss of hemostatic balance is predicted based on calculations of certain protein concentrations. Additionally, a calculation arrangement translates at least some of the calculated values of concentrations of human blood proteins into a risk value which risk value describes a risk of clotting and/or embolism and/or bleeding. A time development of that risk value is displayed to the user.

Abstract: A method for estimating concentrations or concentration changes of particular biological markers, e.g. proteins, includes performing estimations by providing a mathematical model with data from measurements of other biological markers, e.g. other proteins. Thus, instead of measuring the concentration of particular proteins, this concentration can be estimated using the model. The estimated proteins can be used together with other clinical data in another model which is able to provide information or estimations of a patient's disease, for example.

Abstract: A method within dynamic molecular imaging comprising dynamically estimating a first parameter (?(x)) and a second parameter (k(x)) of an activity function describing the bio distribution of an administered tracer, is disclosed.

Abstract: An imaging system (10) comprises a data device (30), which controls radiation data acquisition from a subject positioned in an examination region (18) for an examination. A rebinning processor (40) bins the acquired data periodically into a histogram (42). A transform (70) transforms the histogram (42) into individual independent or uncorrelated components, each component including a signal content and a noise content. A stopping determining device (52) compares an aspect of at least one selected component to a predetermined threshold (TH) and, based on the comparison, terminates the data acquisition.

Abstract: The invention relates to a method for estimating concentrations or concentration changes of particular biological markers, e.g. proteins. The estimations are performed by providing a mathematical model with data from measurements of other biological markers, e.g. other proteins. Thus, instead of measuring those particular proteins they can be estimated using the model. The estimated proteins can be used together with other clinical data in another model which is able to provide information or estimations of, for example, a patient's disease.

Abstract: A method within dynamic molecular imaging comprising dynamically estimating a first parameter (?(x)) and a second parameter (k(x)) of an activity function describing the bio distribution of an administered tracer, is disclosed.

Abstract: A system and method for iterative reconstruction with user interaction in data-driven, adaptive mesh generation for reconstruction of model parameters from imaging data is disclosed. The method includes reading input (110, 115) from a user and checking reconstructed parameters (130) for convergence after each iteration. A required computation time is estimated (130) after each iteration based on a current mesh grid and expected number of iterations and the mesh grid is updated (140). An on-line representation of the reconstructed parameters and an adapted mesh grid is displayed during the reconstruction (170) and a next iteration of the reconstruction is based on the adapted mesh grid (145).

Abstract: An imaging system (10) comprises a data device (30), which controls radiation data acquisition from a subject positioned in an examination region (18) for an examination. A rebinning processor (40) bins the acquired data periodically into a histogram (42). A transform (70) transforms the histogram (42) into individual independent or uncorrelated components, each component including a signal content and a noise content. A stopping determining device (52) compares an aspect of at least one selected component to a predetermined threshold (TH) and, based on the comparison, terminates the data acquisition.