Abstract: The invention relates to a therapy planning device for planning a therapy to be applied to tissue of a subject (2). A tissue parameter distribution, which has been generated based on a magnetic resonance fingerprint scan of the tissue, and a therapy goal distribution, which defines a distribution of at least one parameter being indicative of a desired effect of the therapy, are provided. A machine learning module (13), which has been trained to output at least one therapy application parameter defining the application of the therapy based on an input tissue parameter distribution and an input therapy goal distribution, is used for planning the therapy by determining the at least one therapy application parameter based on the provided tissue parameter distribution and the provided therapy goal distribution. This allows for a consideration of the actual quantitative tissue parameter distribution of the patient, thereby improving planning quality.

Abstract: An approach for determining a set of one or more values for a target cardiac parameter of a subject. A cardiovascular model produces at least one output value set (for a respective at least one property of cardiac function) by processing model parameters. One or more measured value sets, corresponding to non-invasively measurable properties of cardiac function, are processed with corresponding output value sets to modify the model parameters. This modification continues until some predetermined criteria is met, such that the model more closely matches a true representation of the subject's cardiac function. The set of one or more values is then derived from the output of the cardiovascular model and/or from the model parameters of the cardiovascular model.

Abstract: A mechanism for adding or updating a heat distribution model stored in a heat distribution model database. A heat distribution model is usable to determine a heat distribution about an ablation device when it is operated, and can be usable to derive thermal profiles in the vicinity of the ablation device. The mechanism comprises obtaining information about fixed properties of the ablation device, generating a heat distribution model based on the fixed properties, and modifying the heat distribution model if, when used, a generated thermal profile is inaccurate.

Abstract: A computer-implemented method of providing optimized values of treatment parameters of a thermal ablation device for treating a region of interest within a subject, is provided. The method includes: iteratively adjusting initial values of the treatment parameters based on a difference between the predicted effect of the treatment parameters on the region of interest predicted by a relatively less computationally-expensive model, and a desired effect of the treatment parameters on the region of interest, to provide the optimized values of the treatment parameters; intermittently inputting the adjusted values of the treatment parameters into a relatively more computationally-expensive model; and updating the relatively less computationally-expensive model, and/or a mapping between the values of the parameters inputted into the models, such that the predicted effects of both models on the region of interest more closely match one another.

Abstract: The invention relates to a therapy planning device for planning a therapy to be applied to tissue of a subject (2). A tissue parameter distribution, which has been generated based on a magnetic resonance fingerprint scan of the tissue, and a therapy goal distribution, which defines a distribution of at least one parameter being indicative of a desired effect of the therapy, are provided. A machine learning module (13), which has been trained to output at least one therapy application parameter defining the application of the therapy based on an input tissue parameter distribution and an input therapy goal distribution, is used for planning the therapy by determining the at least one therapy application parameter based on the provided tissue parameter distribution and the provided therapy goal distribution. This allows for a consideration of the actual quantitative tissue parameter distriution of the patient, thereby improving planning quality.

Abstract: A patch pocket folding apparatus (1) comprises: a template (2), moving means (3) of the template (2), a folding body (4), against which the template (2) pushes a fabric (T), folding blades (5), a folding unit (G) disposed under the folding body (4); the folding unit (G) is composed of a set of strips (6) made of an easily workable rigid material that are fixed under the folding body (4) and define an internal corresponding border having the same profile as the external border of the template (2).