Smart 3D PACS workflow by learning

1. A method to automatically apply advanced three-dimensional manipulation to an image exam via a hanging protocol for clinical image display, said method comprising:

detecting selection of a new image exam for display by a user;

automatically identifying at least one of a) a previously learned hanging protocol saved for the user and b) a saved hanging protocol associated with a prior image exam corresponding to the new image exam;

when at least one of a) and b) is identified, applying the saved hanging protocol identified in at least one of a) and b) to the new image exam, the saved hanging protocol from at least one of a) and b) including three-dimensional manipulation to be automatically applied to the new image exam as part of the hanging protocol configuration for display and

facilitating display of the new image exam based on the saved hanging protocol; and

triggering, based on a failure to identify a) and b), a hanging protocol learning session for machine learning of the hanging protocol configuration from the user.

2. The method of claim 1 , wherein the three-dimension manipulation comprises at least one of multi-planar reconstruction and maximum intensity projection.

3. The method of claim 1 , further comprising monitoring, in the learning session, user workflow including three-dimensional manipulation of image data from the new image exam and accepting, using a processor, user input to record/teach at least a portion of the workflow for repeat setup.

4. The method of claim 1 , wherein the saved hanging protocol is learned based on images from a first patient and applied to images from a second patient.

5. The method of claim 1 , further comprising facilitating user modification of a saved workflow element in a saved hanging protocol.

6. The method of claim 1 , wherein the three-dimensional manipulation includes registering pixel values from a fixed image and an interpolated moving image based on one or more transform parameters.

7. A non-transitory computer-readable storage medium including a set of instructions for execution by a processor, the instructions, when executed, to implement a method to automatically apply advanced three-dimensional manipulation to an image exam via a hanging protocol for clinical image display, said method comprising:

detecting selection of a new image exam for display by a user;

automatically identifying at least one of a) a previously learned hanging protocol saved for the user and b) a saved hanging protocol associated with a prior image exam corresponding to the new image exam;

when at least one of a) and b) is identified, applying the saved hanging protocol identified in at least one of a) and b) to the new image exam, the saved hanging protocol from at least one of a) and b) including three-dimensional manipulation to be automatically applied to the new image exam as part of the hanging protocol configuration for display and

facilitating display of the new image exam based on the saved hanging protocol; and

triggering, based on a failure to identify a) and b), a hanging protocol learning session for machine learning of the hanging protocol configuration from the user.

8. The computer-readable storage medium of claim 7 , wherein the three-dimension manipulation comprises at least one of multi-planar reconstruction and maximum intensity projection.

9. The computer-readable storage medium of claim 7 , wherein the method further comprises monitoring, in the learning session, user workflow including three-dimensional manipulation of image data from the new image exam and accepting, using a processor, user input to record at least a portion of the workflow for repeat setup.

10. The computer-readable storage medium of claim 7 , wherein the saved hanging protocol is learned based on images from a first patient and applied to images from a second patient.

11. The computer-readable storage medium of claim 7 , wherein the method further comprises facilitating user modification of a saved workflow element in a saved hanging protocol.

12. The computer-readable storage medium of claim 7 , wherein the three-dimensional manipulation includes registering pixel values from a fixed image and an interpolated moving image based on one or more transform parameters.

13. A machine learning hanging protocol analysis system comprising:

a memory to store instructions; and

a processor to execute the instructions which configure the processor to implement:

an image processor to process image data to provide three-dimensional image manipulation in conjunction with a hanging protocol; and

a learning engine to capture and store the hanging protocol including three-dimensional image manipulation configured by a monitored user,

the image processor to automatically apply three-dimensional manipulation to an image exam via the hanging protocol for clinical image display at least in part by:

detecting selection of a new image exam for display by a user;

automatically identifying at least one of a) a previously learned hanging protocol saved for the user and b) a saved hanging protocol associated with a prior image exam corresponding to the new image exam;

when at least one of a) and b) is identified, applying the saved hanging protocol identified in at least one of a) and b) to the new image exam, the saved hanging protocol from at least one of a) and b) including three-dimensional manipulation to be automatically applied to the new image exam as part of the hanging protocol configuration for display and

facilitating display of the new image exam based on the saved hanging protocol; and

triggering, based on a failure to identify a) and b), a hanging protocol learning session for machine learning of the hanging protocol configuration from the user.

14. The system of claim 13 , wherein the three-dimension manipulation comprises at least one of multi-planar reconstruction and maximum intensity projection.

15. The system of claim 13 , wherein the saved hanging protocol is to be learned based on images from a first patient and applied to images from a second patient.

16. The system of claim 13 , wherein the image processor further comprises:

a metric generator to receive pixel data from a fixed image and a moving image and generate a fitness value for registration between the fixed image and the moving image;

an interpolator to interpolate pixel data from the moving image and to provide the interpolated moving image pixel data to the metric generator;

an optimizer to generate one or more transform parameters based on the fitness value; and

a transformer to generate one or more spatial mapping points for the moving image based on the one or more transform parameters.