Autonomous image acquisition start-stop managing system

A method, where each of the steps of the method are implemented by one or more processors for evaluating an autonomous scan procedure for controlling an autonomous imaging apparatus to acquire an image of a patient, the method comprising:

• a) receiving a data input indicative of an event in an autonomous image acquisition workflow and a plurality of sets of workflow steps, each having a plurality of segments, that define phases within the autonomous image acquisition workflow for the event,
• wherein the data input indicative of an event is received from a pre-trained machine learning algorithm that outputs the event based on a determined probability indicating whether the event is likely to happen;
• b) selecting for the data input, one or more workflow step rule engine units from a plurality of workflow step rule engine units to determine a set of readiness indices of the autonomous imaging apparatus, each of the one or more workflow step rule engine units defining rules for a specific segment of the autonomous image acquisition workflow,
• wherein each workflow step rule engine unit is associated with a respective set of rules;
• wherein each rule corresponds to a specific workflow step in the autonomous image acquisition workflow, and workflow steps between different rules are different;
• and
• wherein each of the one or more selected workflow step rule engine units is configured to execute, in response to the data input, one or more rules of the respective rule set to generate a respective readiness index indicative of a state of being prepared for a particular step in the autonomous image acquisition workflow;
• c) determining an action of the autonomous imaging apparatus based on the set of readiness indices;
• and
• d) controlling, by the one or more processors, the autonomous imaging apparatus to perform the determined action when acquiring the image of the patient.

A system comprising:

• at least one processor;
• and
• a non-transitory memory storing instructions that, when executed by the at least one processor, cause the at least one processor to;
• receive a data input indicative of an event in an autonomous image acquisition workflow and a plurality of sets of workflow steps, each having a plurality of segments, that define phases within the autonomous image acquisition workflow for the event,
• wherein the data input indicative of an event is received from a pre-trained machine learning algorithm that outputs the event based on a determined probability indicating whether the event is likely to happen;
• select, for the data input, one or more workflow step rule engines from a plurality of workflow step rule engines to determine a set of readiness indices of the autonomous imaging apparatus, each of the one or more workflow step rule engine units defining rules for a specific segment of the autonomous image acquisition workflow,
• wherein each workflow step rule engine is associated with a respective set of rules,
• wherein each of the one or more selected workflow step rule engines is configured to execute, in response to the data input, one or more rules of the respective set of rules to generate a respective readiness index indicative of a state of being prepared for a particular step in the autonomous image acquisition workflow, and
• wherein each rule corresponds to a specific workflow step in the autonomous image acquisition workflow, and workflow steps between different rules are different;
• determine an action of the autonomous imaging apparatus based on the set of readiness indices;
• and
• output the determined action to the autonomous imaging apparatus and control the autonomous imaging apparatus to perform the determined action in acquiring an image of a patient.