Siemens Healthcare Gmbh

Centre Hospitalier Universitaire Vaudois

Siemens Healthineers Ag

Jérôme Yerly

Tobias Kober

Davide Piccini

Robin Demesmaeker

Jérôme Chaptinel

Peter Speier

Matthias Stuber

A method and system are provided for detecting a position of a periodically moving organ in a MRI examination. MR images of an examining person including a periodically moving organ are provided over a plurality of periodic cycles of the periodically moving organ. Based on the provided MR images, a pixel frequency is associated with each pixel of the MR images. Using the associated pixel frequencies and the positions of the pixels within the MR images, the position and the frequency of the periodically moving organ are determined.

Detection of position and frequency of a periodically moving organ in an MRI examination

The disclosure relates to a method for the interactive acquisition of data from an object under investigation by a magnetic resonance system. The data is acquired from the object under investigation with the magnetic resonance system and images are automatically reconstructed and displayed in real time based on the data. A time interval is determined during which a predetermined condition is met in the images. Quality images are automatically reconstructed based on the data acquired within the time interval. The temporal resolution during reconstruction of the quality images is higher than the temporal resolution during reconstruction of the images.

Interactive data acquisition and reconstruction by a magnetic resonance system

In order to reduce the time and effort required to generate high-quality image reconstructions, a machine-trained neural network may assign a quality score to an image at each iteration of a reconstruction. The neural network may confirm that the iterative reconstruction process increases image quality as each iteration converges to the solution of an optimization problem. The image quality score generated by the neural network may drive the reconstruction toward better image quality by contributing to a regularization term of a cost function minimized by the optimization problem. The neural network may allow for multiple reconstruction of image data to be performed rapidly and for the highest image quality reconstruction to be identified. Additionally, the neural network may provide exit criteria of the iterative reconstruction or may contribute to the optimization problem.

Machine learning in iterative image reconstruction

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Charmey, Switzerland

Jérôme Yerly