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

A device, comprising:

• one or more memories;
• and
• one or more processors, communicatively coupled to the one or more memories, configured to:
• receive an X-ray image captured by a C-arm cone-beam computed tomography (CBCT) device at a particular position defined by a six-degree of freedom pose relative to an anatomy;
• 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;
• utilize the machine learning model, to identify a particular X-ray image, of the next possible X-ray images, with a greatest predicted quality;
• utilize the machine learning model to update the six-degree of freedom pose based on the particular X-ray image and to generate an updated six-degree of freedom pose;
• and
• provide, to the C-arm CBCT device, data that identifies the updated six-degree of freedom pose,
• wherein the data that identifies the updated six-degree of freedom pose cause the C-arm CBCT device to:
• adjust to a new position based on the updated six-degree of freedom pose, and
• acquire a new X-ray image at the new position.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• train a machine learning model based on a first dataset and a second dataset and to generate a trained machine learning model;
• receive an X-ray image captured by a C-arm cone-beam computed tomography (CBCT) device at a particular position defined by a six-degree of freedom pose relative to an anatomy;
• process the X-ray image, with the trained machine learning model, to determine a predicted quality of next possible X-ray images provided by the C-arm CBCT device;
• utilize the trained machine learning model to identify a particular X-ray image, of the next possible X-ray images, with a greatest predicted quality;
• utilize the trained machine learning model to update the six-degree of freedom pose based on the particular X-ray image and to generate an updated six-degree of freedom pose;
• and
• provide, to the C-arm CBCT device, data that identifies the updated six-degree of freedom pose,
• wherein the data that identifies the updated six-degree of freedom pose cause the C-arm CBCT device to adjust to a new position based on the updated six-degree of freedom pose.