Physical deformable lung phantom with subject specific elasticity

1. A system for simulation of spatio-temporal flow and deformation in human tissue, comprising:

a processor; and

programming executable on the processor for:

acquiring radiotherapy imaging data of the tissue;

acquiring computational fluid dynamics (CFD) data corresponding to predictions of tissue displacement;

fusing the imaging data with the CFD data to obtain a fused dataset of the tissue;

wherein the fused data set comprises one or more unique radiological or elastic properties of the tissue; and

a patient-specific, physical organ phantom comprising the one or more unique radiological or elastic properties of the imaged tissue from the fused data set;

said organ phantom physically simulating a unique induced deformation of the imaged tissue; and

said organ phantom having a radiation-attenuation equivalency to the imaged tissue.

2. A system as recited in claim 1 , wherein the tissue comprises a human lung and the patient-specific organ phantom comprises a lung phantom.

3. A system as recited in claim 2 , wherein the imaging data comprises inverse estimation data of a CT scan dataset.

4. A system as recited in claim 3 , wherein the fused dataset is acquired using Tikhonov regularization.

5. A system as recited in claim 3 , wherein acquiring computational fluid dynamics (CFD) data comprises generating a flow-structure interaction model to simultaneously solve the airflow and structural characteristics of lung tissue.

6. A system as recited in claim 3 , wherein fusing the inverse estimation data with the CFD data comprises:

reconstructing 3D geometry of the lung from the CT scan dataset;

generating one or more computational meshes in the reconstructed 3D geometry of the lung;

estimating patient-specific Young's Modulus data from the CT scan datasets;

applying fluid-structure interaction (FSI) calculations to calculate one or more of air flow and deformation characteristics of the lung; and

calculating spatial lung deformation as a function of the FSI calculations and reconstructed lung geometry.

7. A system as recited in claim 2 :

said lung phantom physically simulating the unique lung airflow and cardiac induced deformation of the imaged human lung; and

said lung phantom having a radiation-attenuation equivalency to the imaged human lung.

8. A system as recited in claim 7 , further comprising:

a 3D printer;

said printer configured for generating a polymeric lung phantom.

9. A system as recited in claim 7 , wherein the lung phantom comprises one or more polymer nanocomposite materials having one or more unique elastic and radiological properties that mimic the imaged lung.

10. A method as recited in claim 9 , wherein the imaging data comprises multi-modal imaging data.

11. A system as recited in claim 1 , wherein the imaging data comprises multi-modal imaging data.

12. A method for simulation of spatio-temporal flow and deformation in human tissue, comprising:

acquiring radiotherapy imaging data of the tissue;

acquiring computational fluid dynamics (CFD) data corresponding to predictions of tissue displacement;

fusing the imaging data with the CFD data to obtain a fused dataset of the tissue;

wherein the fused data set comprises one or more unique radiological or elastic properties of the imaged human tissue; and

generating a patient-specific, physical organ phantom comprising the one or more unique radiological or elastic properties of the imaged tissue from the fused dataset

said organ phantom physically simulating a unique induced deformation of the imaged tissue; and

said organ phantom having a radiation-attenuation equivalency to the imaged tissue.

13. A method as recited in claim 12 , wherein the tissue comprises a human lung and the organ phantom comprises a lung phantom;

wherein the imaging data comprises inverse estimation data of a CT scan dataset.

14. A method as recited in claim 13 :

said lung phantom physically simulating the unique lung airflow and cardiac induced deformation of the imaged human lung; and

said lung phantom having a radiation-attenuation equivalency to the imaged human lung.

15. A method as recited in claim 14 , wherein generating a patient-specific organ phantom comprises generating a polymeric lung phantom by 3D printing one or more polymer nanocomposite materials to have the one or more unique elastic and radiological properties that mimic the imaged lung.

16. A method as recited in claim 13 , wherein acquiring computational fluid dynamics (CFD) data comprises generating a flow-structure interaction model to simultaneously solve the airflow and structural characteristics of the imaged lung tissue.

17. A method as recited in claim 13 , wherein fusing the inverse estimation data with the CFD data comprises:

reconstructing 3D geometry of the lung from the CT scan dataset;

generating one or more computational meshes in the reconstructed 3D geometry of the lung;

estimating patient-specific Young's Modulus data from the CT scan datasets;

applying fluid-structure interaction (FSI) calculations to calculate one or more of air flow and deformation characteristics of the lung; and

calculating spatial lung deformation as a function of the FSI calculations and reconstructed lung geometry.

18. A method for generating a lung phantom simulating spatio-temporal flow and deformation in a target lung tissue, comprising:

acquiring radiotherapy imaging data of the target lung tissue;

acquiring computational fluid dynamics (CFD) data corresponding to predictions of tissue displacement;

fusing the imaging data with the CFD data to obtain a fused dataset of the target lung tissue;

wherein the fused data set comprises one or more unique radiological or elastic properties of the imaged lung tissue;

generating a patient-specific polymeric lung phantom as a function of the fused data set, the polymeric lung phantom comprising the one or more unique radiological and elastic properties of the target lung tissue;

said lung phantom physically simulating a unique induced deformation of the target lung tissue; and

said lung phantom having a radiation-attenuation equivalency to the target lung tissue.

19. A method as recited in claim 18 , wherein the polymeric lung phantom comprises one or more polymer nanocomposite materials having the one or more unique elastic and radiological properties that mimic the imaged lung.

20. A system for simulation of spatio-temporal flow and deformation in human tissue, comprising:

a processor; and

programming executable on the processor for:

acquiring radiotherapy imaging data of the tissue;

acquiring computational fluid dynamics (CFD) data corresponding to predictions of tissue displacement;

fusing the imaging data with the CFD data to obtain a fused dataset of the tissue;

wherein the fused data set comprises one or more unique radiological or elastic properties of the tissue; and

a patient-specific organ phantom comprising the one or more unique radiological or elastic properties of the imaged tissue from the fused data set;

wherein the tissue comprises a human lung and the patient-specific organ phantom comprises a lung phantom;

said lung phantom physically simulating the unique lung airflow and cardiac induced deformation of the imaged human lung; and

said lung phantom having a radiation-attenuation equivalency to the imaged human lung.

21. A method for simulation of spatio-temporal flow and deformation in human tissue, comprising:

acquiring radiotherapy imaging data of the tissue;

acquiring computational fluid dynamics (CFD) data corresponding to predictions of tissue displacement;

fusing the imaging data with the CFD data to obtain a fused dataset of the tissue;

wherein the fused data set comprises one or more unique radiological or elastic properties of the imaged human tissue; and

generating a patient-specific organ phantom comprising the one or more unique radiological or elastic properties of the imaged tissue from the fused dataset;

wherein the tissue comprises a human lung and the organ phantom comprises a lung phantom;

wherein the imaging data comprises inverse estimation data of a CT scan dataset;

said lung phantom physically simulating the unique lung airflow and cardiac induced deformation of the imaged human lung; and

said lung phantom having a radiation-attenuation equivalency to the imaged human lung.