Generating a 3D dataset containing a simulated surgical device

1. A method of generating simulated volumetric datasets comprising:

generating a 3D coordinate system;

generating a simulated 3D dataset comprising a set of voxels wherein each voxel within said set of voxels has an assigned data unit within said 3D coordinate system comprising:

generating a sub-volume, wherein the sub-volume includes a representation of a surgical device;

inserting said sub-volume into said 3D coordinate system;

performing a discrimination process on said simulated 3D dataset wherein said discrimination process determines whether said simulated 3D dataset appears real or appears fake; and

modifying voxel(s) within said simulated 3D dataset when said discrimination process determines that said simulated 3D dataset appears fake.

2. The method of claim 1 further comprising wherein said generating a simulated 3D dataset further comprises at least one of the group consisting of: an x-ray; an ultrasound; a mammogram; a CT scan; an MRI scan; a PET scan; a SPECT scan; and, a DTS scan.

3. The method claim 1 further comprising performing analysis of a set of real medical imaging examinations to generate metrics.

4. The method of claim 3 wherein said performing analysis comprises at least one of the group consisting of: calculating a mean; calculating a median; calculating a mode; calculating a standard deviation; calculating a variance; calculating a histogram type analysis; and, performing advanced statistical methods to characterize the data.

5. The method of claim 3 further comprising wherein said discrimination process of said simulated 3D dataset compares metrics of said simulated 3D dataset with metrics of the set of real medical imaging examinations.

6. The method of claim 5 further comprising wherein said simulated dataset is modified under conditions wherein the metrics of said simulated dataset differ from the metrics of said real imaging examinations.

7. The method of claim 1 further comprising wherein generating a simulated 3D dataset utilizes a layer-by-layer approach.

8. The method of claim 7 further comprising wherein said inserting said sub-volume comprises at least one of the group consisting of:

insertion of real sub-volumes from actual data into said simulated 3D dataset;

insertion of simulated 3D sub-volumes into said simulated 3D dataset; and

insertion of simulated 3D sub-volumes into actual patient examinations.

9. The method of claim 1 further comprising wherein performing a discrimination process on said simulated 3D dataset utilizes at least one of the group consisting of:

AI analysis of reformatted images from multiple contiguous slices; and

AI analysis of processed images including maximum intensity projection images and volume rendering images.

10. The method of claim 1 further comprising wherein said simulated 3D dataset comprises at least one of the group consisting of:

a single anatomic structure;

an age-specific normal examination;

a gender-specific examination; and

varying pathologic conditions.

11. The method of claim 10 further comprising wherein a difficulty level is established associated with some particular pathology for testing purposes wherein said difficulty level is related to at least one of the group consisting of: a size of a lesion; a conspicuity of lesion; a presence of imaging artifacts; and, a frequency of disease.

12. The method of claim 1 further comprising wherein said simulated 3D dataset comprises at least one of the group consisting of: a simulation of pathologic growth occurs over time; and, a simulation of a changing imaging feature.

13. The method of claim 1 further comprising wherein said simulated 3D dataset is used for medical education in a time-step interactive process performing the steps of:

selecting demographics of a patient;

selecting a pathology;

selecting a medical imaging examination; and

modifying said medical imaging examination to simulate said pathology.

14. The method of claim 13 further comprising wherein said pathology comprises one of the group consisting of: tumor(s); infection(s); traumatic injury(ies); and, other known diseases.

15. The method of claim 1 further comprising wherein a user provides feedback to denote the fake appearing region through use of a 3D volume cursor.

16. A method comprising:

using a generative adversarial network to create a first image slice and insert a representation of a surgical device into said first image slice;

performing a discrimination process to determine if said first image slice appears real;

if said first image slice appears real, using a generative adversarial network to create at least one additional image slice wherein said at least one additional image slice is contiguous with said first image slice;

performing the discrimination process to determine if the at least one additional image slice appears real;

if the at least one additional image slice appears real:

arranging said first image slice and said at least one additional image slice to generate a volume; and

reconstructing a reformat from said volume wherein said reformat contains at least a portion of said first slice and at least a portion of said at least one additional image slice; and

performing a discrimination process on said reformat.

17. The method of claim 16 further comprising wherein said reformat comprises at least one of the group consisting of: an axial image; a sagittal image; a coronal image; and, an oblique image.

18. A method comprising:

using an artificial intelligence (AI) process to create a first image slice wherein said first image slice comprises a representation of a surgical device;

performing a discrimination process to determine if said first image slice appears real;

if said first image slice appears real, using an AI process to create at least one additional image slice wherein said at least one additional image slice is contiguous with said first image slice;

performing the discrimination process to determine if the at least one additional image slice appears real;

if the at least one additional image slice appears real:

arranging said first image slice and said at least one additional image slice to generate a volume; and

reconstructing a reformat from a said volume wherein said reformat contains at least a portion of said first slice and at least a portion of said at least one additional image slice; and

performing a discrimination process on said reformat.

19. The method of claim 18 further comprising wherein said reformat comprises at least one of the group consisting of: an axial image; a sagittal image; a coronal image; and, an oblique image.

20. The method of claim 18 further comprising wherein a user provides feedback to denote the fake appearing region through use of a 3D volume cursor.