Abstract: A mechanism for generating and providing one or more quality measures of a mapping function (for mapping electrical responses of an electrode to a predicted position of that electrode within an anatomical cavity) to a user, such as a clinician. Each quality measure predicts or indicates a quality of a mapping function with respect to a particular part of an anatomical cavity, for instance, indicating a predicted accuracy of the mapping function for predicting a position of an electrode located within a particular portion of the anatomical cavity. A user-perceptible output is provided that indicates the quality measure for the user.

Abstract: A mechanism for determining a shape of an interventional device. A mapping function is used to predict the position of an electrode, mounted on the interventional device, as the interventional device is moved within an anatomical cavity—to thereby form a sequence of predicted positions. The sequence of predicted positions is then processed to predict a shape of the interventional device.

Abstract: A mechanism for adjusting the direction of one or more crossing electric fields that have been induced by a set of electrodes positioned on a patient, without necessitating movement of any of the electrodes. This is achieved through the use of one or more electrode patches, which effectively allow an electric field generator to control a position of a source/sink of an electric field, to thereby control a direction of the electric field.

Abstract: A mechanism for generating a mapping function for mapping measurements taken at an electrode within crossing electric fields to a position or positions within a multidimensional co-ordinate system. The values for coefficients of the mapping function are generated using a first machine-learning algorithm, which receives, as input, example measurements (or responses) of the electrode and provides, as output, values for the coefficients for the mapping function. There is proposed a method and processing system for carrying out this mechanism.

Abstract: The invention relates to a method for generating a synthetic elastography image (18), the method comprising the steps of (a) receiving a B-mode ultrasound image (5) of a region of interest; (b) generating a synthetic elastography image (18) of the region of interest by applying a trained artificial neural network (16) to the B-mode ultrasound image (5). The invention also relates to a method for training an artificial neural network (16)5 useful in generating synthetic elastography images, and a related computer program and system.