Mako Surgical Corporation

Piur Imaging Gmbh

Imfusion Gmbh

Raphael Prevost

Wolfgang Wein

José Luis Moctezuma De La Barrera

Mehrdad Salehi

Robert Bauer

Julian Sprung

Systems and methods for calibrating ultrasound imaging directed towards a bone region. An ultrasound imaging device generates a first steered frame and a second steered frame, wherein the first steered frame and the second steered frame are directed towards the bone region at different angles from one another and are superimposed with one another. Parameters of each of the first steered frame and the second steered frame are applied to a cost function that outputs an estimated propagation speed of ultrasound waves to the bone region for optimizing an appearance of the first steered frame and the second steered frame. The ultrasound imaging device is calibrated based on the estimated propagation speed.

Ultrasound bone registration with learning-based segmentation and sound speed calibration

A workflow is disclosed to accurately register ultrasound imaging to co-modality imaging. The ultrasound imaging is segmented with a convolutional neural network to detect a surface of the object. The ultrasound imaging is calibrated to reflect a variation in propagation speed of the ultrasound waves through the object by minimizing a cost function that sums the differences between the first and second steered frames, and compares the first and second steered frames of the ultrasound imaging with a third frame of the ultrasound imaging that is angled between the first and second steered frames. The ultrasound imaging is temporarily calibrated with respect to a tracking coordinate system by creating a point cloud of the surface and calculating a set of projection values of the point cloud to a vector. The ultrasound imaging, segmented and calibrated, is automatically registered to the co-modality imagine.

A method of determining a three-dimensional motion of a movable ultrasound probe () is described. The method is carried out during acquisition of an ultrasound image of a volume portion () by the ultrasound probe. The method comprises receiving a stream of ultrasound image data () from the ultrasound probe () while the ultrasound probe is moved along the volume portion (); inputting at least a sub-set of the ultrasound image data () representing a plurality of ultrasound image frames () into a machine-learning module (), wherein the machine learning module () has been trained to determine the relative three-dimensional motion between ultrasound image frames (); and determining, by the machine-learning module (), a three-dimensional motion indicator () indicating the relative three-dimensional motion between the ultrasound image frames.

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Munich, Germany

Raphael Prevost