Abstract: A computer-implemented method and system train a model of imaging examinations based on data representing a plurality of historical imaging examinations, and generate, using the model of imaging examinations and a current schedule of imaging appointments, a schedule recommendation. The schedule recommendation recommends an imaging appointment within the current schedule of imaging appointments. The current schedule of imaging appointments may be updated based on the schedule recommendation, such as by adding the recommended imaging appointment to the current schedule or by modifying an existing imaging appointment in the current schedule. The schedule recommendation may be generated in response to a request or dynamically.

Abstract: A computer-implemented method uses a plurality of input examination data sets, created by performing a plurality of imaging examinations of at least one patient on at least one scanner, to learn a model of imaging protocols. The model may learn imaging protocols by capturing common features across the plurality of input examination data sets . The method may regroup examination data sets, within the plurality of input examination data sets, with common features under a common protocol tag, and learning the model may include generating a plurality of protocol tags. The model may be updated over time based on new input examination data sets.

Abstract: A system or method for improving quality in projection and tomographic x-ray, which includes a depth sensing device to measure a depth of at least one body part of a patient from the depth sensing device and a control unit to calculate a thickness and/or circumference of the body part using the depth information. The calculated thickness and circumference information is used to determine an optimal level of x-ray exposure for the body part. The system or method also includes a camera to identify the body part that needs to be examined and to detect any motion of the identified body part.