FIELD

This disclosure relates generally to medical imaging systems, and more specifically to controls for imaging scanners.

BACKGROUND

To reduce unnecessary radiation, medical scanning equipment should limit the regions of the patient that are scanned. Because of the variation in patient body shapes, body sizes, clothing, and the like, a technician operating a medical imaging scanner may be faced with the difficult task of trying to determine, roughly, the hidden location of an internal organ or region of interest in a patient. The location of an internal organ can be determined by the technician from low-dose topogram images, which result in extra radiation of the patients. The technician then manually positions the patient such that the region of interest is optimally positioned with respect to the scanner. The manual patient positioning process is time-consuming and costly.

SUMMARY

In some embodiments, a method for controlling a scanner comprises: sensing an outer surface of a body of a subject to collect body surface data; using machine learning to predict a surface of an internal organ of the subject based on the body surface data; and controlling the scanner based on the predicted surface of the internal organ.

In some embodiments, a system for controlling a scanner comprises: a depth sensor for sensing an outer surface of a body of a subject to collect body surface data; a processor coupled to the scanner; and a non-transitory, machine readable storage medium coupled to the processor and encoded with program instructions for: using machine learning to predict a surface of an internal organ of the subject based on the body surface data; and controlling the scanner based on the predicted surface of the internal organ.

In some embodiments, a non-transitory, machine readable storage medium encoded with program instructions for controlling a scanner, such that when a processor executes the program instructions, the processor performs a method comprising: receiving body surface data representing an outer surface of a body of a subject from a depth sensor; using machine learning to predict a surface of an internal organ of the subject based on the body surface data; and controlling the scanner based on the predicted surface of the internal organ.

