Abstract: 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.

Abstract: 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.

Abstract: A computer platform is provided for computer assisted navigation during surgery. The computer platform includes at least one processor that is operative to transform pre-operative images of a patient obtained from a first imaging modality to an estimate of the pre-operative images of the patient in a second imaging modality that is different than the first imaging modality. The at least one processor is further operative to register the estimate of the pre-operative images of the patient in the second imaging modality to intra-operative navigable images or data of the patient.

Abstract: A method of determining a three-dimensional motion of a movable ultrasound probe (10) is described. The method is carried out during acquisition of an ultrasound image of a volume portion (2) by the ultrasound probe. The method comprises receiving a stream of ultrasound image data (20) from the ultrasound probe (10) while the ultrasound probe is moved along the volume portion (2); inputting at least a sub-set of the ultrasound image data (20, 40) representing a plurality of ultrasound image frames (22) into a machine-learning module (50), wherein the machine learning module (50) has been trained to determine the relative three-dimensional motion between ultrasound image frames (22); and determining, by the machine-learning module (50), a three-dimensional motion indicator (60) indicating the relative three-dimensional motion between the ultrasound image frames.

Abstract: Disclosed herein is a surgical system for surgical registration of patient bones to a surgical plan. As part of its ability to perform the surgical registration, the system is configured to process an ultrasound image of patient bones, the ultrasound image including a bone surface for each of the patient bones. The system includes a computing device including a processing device and a computer-readable medium with one or more executable instructions stored thereon. The processing device is configured to execute the one or more executable instructions. The one or more executable instructions: i) detects the bone surface of each of the patient bones in the ultrasound image; and ii) segregates a first point cloud of ultrasound image pixels associated with the bone surface of each of the patient bones.

Abstract: A method of determining a three-dimensional motion of a movable ultrasound probe (10) is described. The method is carried out during acquisition of an ultrasound image of a volume portion (2) by the ultrasound probe. The method comprises receiving a stream of ultrasound image data (20) from the ultrasound probe (10) while the ultrasound probe is moved along the volume portion (2); inputting at least a sub-set of the ultrasound image data (20, 40) representing a plurality of ultrasound image frames (22) into a machine-learning module (50), wherein the machine learning module (50) has been trained to determine the relative three-dimensional motion between ultrasound image frames (22); and determining, by the machine-learning module (50), a three-dimensional motion indicator (60) indicating the relative three-dimensional motion between the ultrasound image frames.

Abstract: 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.