SYSTEMS AND METHODS FOR CONTRAST FLOW MODELING WITH DEEP LEARNING

1 . A system, comprising:

an x-ray source that emits a beam of x-rays towards a subject to be imaged;

a detector that receives the x-rays attenuated by the subject;

a data acquisition system (DAS) operably connected to the detector; and

a computing device operably connected to the DAS and configured with executable instructions in non-transitory memory that when executed cause the computing device to:

generate a first estimated time to perform a diagnostic scan of the subject based on demographic information and clinical information of the patient; and

control the x-ray source and the detector to perform the diagnostic scan of the subject at the first estimated time responsive to a first confidence level of the first estimated time above a threshold.

2 . The system of claim 1 , wherein the computing device is further configured with executable instructions in non-transitory memory that when executed cause the computing device to:

control the x-ray source and the detector to perform a monitoring scan of a region of interest (ROI) of the subject responsive to the first confidence level below the threshold, the monitoring scan comprising a low-dose, short-duration scan relative to the diagnostic scan;

generate a second estimated time to perform the diagnostic scan of the subject based on projection data acquired during the monitoring scan; and

control the x-ray source and the detector to perform the diagnostic scan of the subject at the second estimated time responsive to a second confidence level of the second estimated time above the threshold.

3 . The system of claim 2 , wherein the computing device is configured with a first deep learning model and a second deep learning model, wherein the first deep learning model generates the first estimated time and the second deep learning model generates the second estimated time.

4 . The system of claim 3 , wherein the computing device is further configured with executable instructions in non-transitory memory that when executed cause the computing device to:

reconstruct an image from data acquired during the diagnostic scan;

receive, via an operator console communicatively coupled to the computing device, an indication of image quality for the image; and

update one or more of the first deep learning model and the second deep learning model based on the indication of image quality.

5 . The system of claim 1 , wherein the first estimated time comprises a timing prediction of peak contrast enhancement in a region of interest (ROI) of the subject.