Siemens Healthcare Gmbh

Ecole Polytechnique Federale De Lausanne

Onward Medical N.v.

Centre Hospitalier Universitaire Vaudois

Siemens Healthineers Ag

Robin Demesmaeker

Tobias Kober

Gregoire Courtine

Vincent Delattre

Grégoire Courtine

Joachim Von Zitzewitz

Davide Piccini

Jocelyne Bloch

Edoardo Paoles

Miroslav Caban

Damien Ganty

Jérôme Yerly

Jordan Squair

Leonie Asboth

Henri Lorach

Tomislav Milekovic

Jérôme Chaptinel

Andreas Rowald

Salif Komi

Fabien Wagner

Lead paddle

The present disclosure provides devices, systems, and methods for spinal cord stimulation. In some exemplary embodiments, a lead is provided. In one exemplary embodiment, the lead includes a paddle and an electrode array distributed in the paddle. The electrode array includes a first column of electrodes and a second column of electrodes. The first column of electrodes and the second column of electrodes are symmetric about the midline of the paddle. The first column of electrodes includes a first electrode, a second electrode, and a third electrode. The second column of electrodes includes a fourth electrode corresponding to the first electrode, a fifth electrode corresponding to the second electrode, and a sixth electrode corresponding to the third electrode. A vertical inter-electrode distance between the first electrode and the second electrode is larger than that between the second electrode and the third electrode.

Devices and systems for electrical stimulation and methods of use

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

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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Morges, Switzerland

Robin Demesmaeker