Koninklijke Philips Corporation N.v.

Nicola Pezzotti

Elwin De Weerdt

Sergey Kastryulin

Alexey Chernyavskiy

The present disclosure relates to a method comprising: providing a trained machine learning model. The trained machine learning model is configured for reconstructing images from input data. The method comprises: receiving () a multidimensional array comprising M dimensional acquired data; determining () a subset of values of at least one K selected dimension of the array; for each value of the subset determining () a M−K dimensional array comprising the acquired data corresponding to the value, resulting in a set of M−1 dimensional arrays; inputting () the set of M−K dimensional arrays to the trained machine learning model, and receive a reconstructed image from the trained machine learning model.

Adaptive reconstruction of magnetic resonance images

A method of medical imaging including receiving k-space data that is divided into multiple k-space data groups, selecting one of the multiple k-space data groups as a reference k-space data group, and calculating spatial transform data for each of the multiple k-space data groups by inputting the multiple k-space data groups and the reference k-space data group into a transformation estimation module. The spatial transformation estimation module is configured for outputting spatial transform data descriptive of a spatial transform between a reference k-space data group and multiple k-space data groups in response to receiving the reference k-space data group and the multiple k-space data groups as input. The method further comprises reconstructing a corrected magnetic resonance image according to the magnetic resonance imaging protocol using the multiple k-space data groups and the spatial transform data for each of the multiple k-space data groups.

Growing community of inventors

Eindhoven, Netherlands

Nicola Pezzotti