Patient anatomy and task specific automatic exposure control in computed tomography

A method, comprising:

• training, by a system operatively coupled to a processor, a first deep learning network to determine expected organ doses absorbed by one or more organs of an anatomical region under different automatic exposure control patterns of a computed tomography scan based on scout images captured of the anatomical region for different patients;
• training, by the system, a second deep learning network to determine expected measures of image quality in target regions and background regions of computed tomography scan images captured under the different automatic exposure control patterns based on the scout images;
• and
• employing, by the system, the first deep learning network and the second deep learning network to determine optimal automatic exposure control patterns of the different automatic exposure control patterns for performance of the computed tomography scan for the different patients based on the scout images.

A machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

• training a first deep learning network to determine expected organ doses absorbed by one or more organs of an anatomical region under different automatic exposure control patterns of a computed tomography scan based on scout images captured of the anatomical region for different patients;
• training a second deep learning network to determine expected measures of image quality in target regions and background regions of computed tomography scan images captured under the different automatic exposure control patterns based on the scout images;
• and
• employing the first deep learning network and the second deep learning network to determine optimal automatic exposure control patterns of the different automatic exposure control patterns for performance of the computed tomography scan for the different patients based on the scout images.