Abstract: A computer-implemented method for medical measurement reconstruction may comprise: receiving a measurement acquisition signal; based on the received measurement acquisition signal, creating a plurality of representations of the measurement acquisition signal, wherein each of the plurality of representations relates to a different aspect of the measurement acquisition signal; modifying one or more of the plurality of representations; and generating an output signal including the modified one or more of the plurality of representations.

Abstract: A computer-implemented method for medical measurement reconstruction may comprise obtaining a first reconstruction of at least one representation of at least one set of medical measurements; presenting the first reconstruction or information about the first reconstruction to a reviewer; receiving an input from the reviewer relating to the first reconstruction or the information about the first reconstruction; processing the received input; and generating a second, modified reconstruction based on the received input.

Abstract: A computer-implemented method for medical measurement reconstruction may comprise obtaining a first reconstruction of at least one representation of at least one set of medical measurements; presenting the first reconstruction or information about the first reconstruction to a reviewer; receiving an input from the reviewer relating to the first reconstruction or the information about the first reconstruction; processing the received input; and generating a second, modified reconstruction based on the received input.

Abstract: A computer-implemented method for medical measurement reconstruction may comprise: receiving a measurement acquisition signal; based on the received measurement acquisition signal, creating a plurality of representations of the measurement acquisition signal, wherein each of the plurality of representations relates to a different aspect of the measurement acquisition signal; modifying one or more of the plurality of representations; and generating an output signal including the modified one or more of the plurality of representations.

Abstract: Imaging methods, imaging apparatus and computer program products are disclosed. An imaging method comprises: receiving image data of a 3-dimensional object; and allocating a confidence level to at least a portion of an image frame of the image data using a machine-learning algorithm, the confidence level indicating a likelihood of that image frame having a specified element imaged on a specified plane through the 3-dimensional object. In this way, particular elements when imaged in a desired way can be identified from image data of the 3-dimensional object.

Abstract: A method (1) is described for training a machine learning model (2) to receive as input a time-resolved three-dimensional model (4) of a heart or a portion of a heart, and to output (3) a predicted time-to-event or a measure of risk for an adverse cardiac event. The method includes receiving a training set (5). The training set (5) includes a number of time-resolved three-dimensional models (41, . . . , 4N) of a heart or a portion of a heart. The training set (5) also includes, for each time-resolved three-dimensional model (41, . . . , 4N), corresponding outcome data (71, . . . , 7N) associated with the time-resolved three-dimensional model (41, . . . , 4N). The method (1) of training a machine learning model (2) also includes, using the training set (5) as input, training the machine learning model (2) to recognise latent representations (12) of cardiac motion which are predictive of an adverse cardiac event.

Abstract: Imaging methods, imaging apparatus and computer program products are disclosed. An imaging method comprises: receiving image data of a 3-dimensional object; and allocating a confidence level to at least a portion of an image frame of the image data using a machine-learning algorithm, the confidence level indicating a likelihood of that image frame having a specified element imaged on a specified plane through the 3-dimensional object. In this way, particular elements when imaged in a desired way can be identified from image data of the 3-dimensional object.

Abstract: The present invention provides a method of modelling the interaction between different biomarkers or other patient measurements and their progression along the trajectory of a neurodegenerative disease, the method comprising the steps of: a) Obtaining longitudinal biomarker profiles from multiple subjects; b) Storing the longitudinal biomarker profiles in a first memory; c) Using a computer system to perform temporal alignment of the longitudinal biomarker profiles; d) Identifying longitudinal biomarker parameters indicative of a subject at a target neurodegenerative disease stage; e) Applying the longitudinal biomarker parameters to each longitudinal biomarker profile and subject in the memory to define respective parametric models; f) defining a biomarker signature for each subject by combining two or more longitudinal biomarker profiles for that subject; and g) forming a global disease model by combining individual parametric models and biomarker signatures.

Abstract: One embodiment of the invention provides a computer-implemented method of annotating images in a data set comprising a first set of images which initially have annotations and a second set of images which initially do not have annotations. The method comprises the steps of determining spatial correspondences for all pairs of images in the data set, wherein each spatial correspondence represents a mapping of a location in a first image to a corresponding location in a second image. For each mapping, the method further comprises calculating a connection strength between the location in the first image and the corresponding location in the second image.

Abstract: A method of processing image data for registration of multiple images to a common reference space comprising the step of computing a mapping between each image and a common reference image comprising an average of all of the images.

Abstract: For the classification of images, a classification measure is computed by registering a set of images to a reference image and performing linear discriminant analysis on the set of images using a conditioned within-class scatter matrix. The classification measure may be used for classifying images, as well as for visualising between-class differences for two or more classes of images.