Johns Hopkins University

Mathias Unberath

Jan-Nico Zaech

Nassir Navab

Javad Fotouhi

Wenhao Gu

Kinjal Shah

A device may receive an X-ray image captured by a C-arm CBCT device at a particular position defined by a six-degree of freedom pose relative to an anatomy, and may process the X-ray image, with a machine learning model, to determine a predicted quality of next possible X-ray images provided by the C-arm CBCT device. The device may utilize the machine learning model, to identify a particular X-ray image, of the next possible X-ray images, with a greatest predicted quality and to update the six-degree of freedom pose based on the particular X-ray image. The device may provide, to the C-arm CBCT device, data that identifies the updated six-degree of freedom pose to cause the C-arm CBCT device to adjust to a new position based on the updated six-degree of freedom pose.

Machine learning model to adjust C-arm cone-beam computed tomography device trajectories

Provided herein are methods of determining a cognitive load of a sensorized device user in certain aspects. Provided herein are also methods of adjusting an interface between a user and a sensorized device in some aspects. Related devices, systems, and computer program products are also provided.

Methods, systems, and related aspects for determining a cognitive load of a sensorized device user

A device may receive, from an imaging device, a two-dimensional image of a patient being operated on by a user, where the two-dimensional image captures a portion of the patient, and where the portion of the patient is provided between a focal point of an imaging source of the imaging device and a detector plane of the imaging device. The device may translate the two-dimensional image along a frustum of the imaging source, and may generate one or more images in a three-dimensional space based on translating the two-dimensional image along the frustum of the imaging source. The device may provide the one or more images in the three-dimensional space to an augmented reality device associated with the user.

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Baltimore, MD, United States of America

Mathias Unberath