Tomographic image reconstruction using artificial intelligence (AI) engines

1. A method for a computer system to perform tomographic image reconstruction using an artificial intelligence (AI) engine, wherein the method comprises:

obtaining two-dimensional (2D) projection data associated with a target object and acquired using an imaging system;

processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers by performing the following:

generating 2D feature data by processing the 2D projection data using the multiple first processing layers that are associated with first weight data and operate in a 2D projection space;

reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and

generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers that are associated with second weight data and operate in a 3D volume space.

2. The method of claim 1 , wherein generating the second 3D feature volume data comprises:

generating the second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers that are trained to perform encoding on the first 3D feature volume data.

3. The method of claim 1 , wherein generating the 2D feature data comprises:

generating the 2D feature data by processing the 2D projection data using the multiple first processing layers that are trained to perform pre-processing on the 2D projection data in the 2D projection space.

4. The method of claim 3 , wherein generating the 2D feature data comprises:

generating the 2D feature data by processing the 2D projection data using the multiple first processing layers that are trained to apply one or more convolution filters on the 2D projection data.

5. The method of claim 1 , wherein the method further comprises:

obtaining training data that includes (a) training 2D projection data and (b) training 3D feature volume data; and

training the multiple first processing layers and the multiple second processing layers together, with the back-projection module interposed in between, to transform (a) the training 2D projection data and (b) the training 3D feature volume data and to learn the respective first weight data and second weight data.

6. The method of claim 1 , wherein the method further comprises:

processing the second 3D feature volume data generated by the AI engine, being a first AI engine, using a second AI engine that is trained to perform tomographic image analysis to transform the second 3D feature volume data to analysis output data.

7. The method of claim 6 , wherein the method further comprises:

obtaining training data that includes (a) training 2D projection data and (b) training analysis output data; and

training the first AI engine and the second AI engine together to perform integrated tomographic image reconstruction and analysis to transform (a) the training 2D projection data to (b) the training analysis output data.

8. A non-transitory computer-readable storage medium that includes a set of instructions which, in response to execution by a processor of a computer system, cause the processor to perform a method of tomographic image reconstruction using an artificial intelligence (AI) engine, wherein the method comprises:

obtaining two-dimensional (2D) projection data associated with a target object and acquired using an imaging system;

processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers by performing the following:

generating 2D feature data by processing the 2D projection data using the multiple first processing layers that are associated with first weight data and operate in a 2D projection space;

reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and

generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers that are associated with second weight data and operate in a 3D volume space.

9. The non-transitory computer-readable storage medium of claim 8 , wherein generating the second 3D feature volume data comprises:

generating the second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers that are trained to perform encoding on the first 3D feature volume data.

10. The non-transitory computer-readable storage medium of claim 8 , wherein generating the 2D feature data comprises:

generating the 2D feature data by processing the 2D projection data using the multiple first processing layers that are trained to perform pre-processing on the 2D projection data in the 2D projection space.

11. The non-transitory computer-readable storage medium of claim 10 , wherein generating the 2D feature data comprises:

generating the 2D feature data by processing the 2D projection data using the multiple first processing layers that are trained to apply one or more convolution filters on the 2D projection data.

12. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises:

obtaining training data that includes (a) training 2D projection data and (b) training 3D feature volume data; and

training the multiple first processing layers and the multiple second processing layers together, with the back-projection module interposed in between, to transform (a) the training 2D projection data and (b) the training 3D feature volume data and to learn the respective first weight data and second weight data.

13. The non-transitory computer-readable storage medium of claim 8 , wherein the method further comprises:

processing the second 3D feature volume data generated by the AI engine, being a first AI engine, using a second AI engine that is trained to perform tomographic image analysis to transform the second 3D feature volume data to analysis output data.

14. The non-transitory computer-readable storage medium of claim 13 , wherein the method further comprises:

obtaining training data that includes (a) training 2D projection data and (b) training analysis output data; and

training the first AI engine and the second AI engine together to perform integrated tomographic image reconstruction and analysis to transform (a) the training 2D projection data to (b) the training analysis output data.

15. A computer system configured to perform tomographic image reconstruction using an artificial intelligence (AI) engine, wherein the computer system comprises: a processor and a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to:

obtain two-dimensional (2D) projection data associated with a target object and acquired using an imaging system;

process the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers by performing the following:

generate 2D feature data by processing the 2D projection data using the multiple first processing layers that are associated with first weight data and operate in a 2D projection space;

reconstruct first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and

generate second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers that are associated with second weight data and operate in a 3D volume space.

16. The computer system of claim 15 , wherein the instructions for generating the second 3D feature volume data cause the processor to:

generate the second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers that are trained to perform encoding on the first 3D feature volume data.

17. The computer system of claim 15 , wherein the instructions for generating the 2D feature data cause the processor to:

generate the 2D feature data by processing the 2D projection data using the multiple first processing layers that are trained to perform pre-processing on the 2D projection data in the 2D projection space.

18. The computer system of claim 15 , wherein the instructions for generating the 2D feature data cause the processor to:

generate the 2D feature data by processing the 2D projection data using the multiple first processing layers that are trained to apply one or more convolution filters on the 2D projection data.

19. The computer system of claim 15 , wherein the instructions further cause the processor to:

obtain training data that includes (a) training 2D projection data and (b) training 3D feature volume data; and

train the multiple first processing layers and the multiple second processing layers together, with the back-projection module interposed in between, to transform (a) the training 2D projection data and (b) the training 3D feature volume data and to learn the respective first weight data and second weight data.

20. The computer system of claim 15 , wherein the instructions further cause the processor to:

process the second 3D feature volume data generated by the AI engine, being a first AI engine, using a second AI engine that is trained to perform tomographic image analysis to transform the second 3D feature volume data to analysis output data.

21. The computer system of claim 20 , wherein the instructions further cause the processor to:

obtain training data that includes (a) training 2D projection data and (b) training analysis output data; and

train the first AI engine and the second AI engine together to perform integrated tomographic image reconstruction and analysis to transform (a) the training 2D projection data to (b) the training analysis output data.