System and method for image-based object modeling using multiple image acquisitions or reconstructions
Systems and methods are disclosed for integrating imaging data from multiple sources to create a single, accurate model of a patient's anatomy. One method includes receiving a representation of a target object for modeling; determining one or more first anatomical parameters of the target anatomical object from at least one of one or more first images of the target anatomical object; determining one or more second anatomical parameters of the target anatomical object from at least one of one or more second images of the target anatomical object; updating the one or more first anatomical parameters based at least on the one or more second anatomical parameters; and generating a model of the target anatomical object based on the updated first anatomical parameters.
1. A computer-implemented method of processing images to produce an anatomical model, using a computer system, the method comprising:
obtaining a first image acquisition of a patient at a first point in time, the first image acquisition comprising first lumen boundary information;
obtaining a second image acquisition of the patient at a second point in time, the second image acquisition comprising second lumen boundary information;
performing a segmentation of the first image acquisition to determine a first probability;
performing a segmentation of the second image acquisition to determine a second probability;
combining the first lumen boundary information and the second lumen boundary information;
averaging the first and second probabilities; and
generating an updated volumetric model of a target anatomy of the patient by updating a lumen boundary of a volumetric model based on the combined first lumen boundary information and second lumen boundary information and based on the averaged first and second probabilities.
2. The method of claim 1 , further comprising:
receiving a first image reconstruction based on the first image acquisition of the patient,
wherein generating the updated volumetric model is further based on the first image reconstruction.
3. The method of claim 2 , further comprising:
receiving a second image reconstruction based on the second image acquisition of the patient,
wherein generating the updated volumetric model is further based on the second image reconstruction.
4. The method of claim 1 , wherein:
the first image acquisition of the patient is generated using a first imaging modality, and
the second image acquisition of the patient is generated using a second imaging modality different from the first imaging modality.
5. The method of claim 4 , wherein the first imaging modality is a computerized tomography (CT) scan, and the second imaging modality is a magnetic resonance (MR) scan.
6. The method of claim 1 , wherein the updated volumetric model further includes a parameterized representation of the patient's vasculature.
7. The method of claim 1 , wherein the averaged first and second probabilities indicate a probability that a voxel of the volumetric model belongs to the patient's vasculature.
8. The method of claim 1 , wherein:
the first probability is a probability that each voxel of the volumetric model belongs to a lumen, and,
the second probability is a probability that each voxel of the volumetric model belongs to the lumen.
9. A system of processing images to produce an anatomical model, the system comprising:
a data storage device storing instructions for processing images to produce an anatomical model; and
a processor configured to execute the instructions to perform a method including:
obtaining a first image acquisition of a patient at a first point in time, the first image acquisition comprising first lumen boundary information;
obtaining a second image acquisition of the patient at a second point in time, the second image acquisition comprising second lumen boundary information;
performing a segmentation of the first image acquisition to determine a first probability;
performing a segmentation of the second image acquisition to determine a second probability;
combining the first lumen boundary information and the second lumen boundary information;
averaging the first and second probabilities; and
generating an updated volumetric model of a target anatomy of the patient by updating a lumen boundary of a volumetric model based on the combined first lumen boundary information and second lumen boundary information and based on the averaged first and second probabilities.
10. The system of claim 9 , wherein the processor is further configured for:
receiving a first image reconstruction based on the first image acquisition of the patient,
wherein generating the updated volumetric model is further based on the first image reconstruction.
11. The system of claim 10 , wherein the processor is further configured for:
receiving a second image reconstruction based on the second image acquisition of the patient,
wherein generating the updated volumetric model is further based on the second image reconstruction.
12. The system of claim 9 , wherein:
the first image acquisition of the patient is generated using a first imaging modality, and
the second image acquisition of the patient is generated using a second imaging modality different from the first imaging modality.
13. The system of claim 12 , wherein the first imaging modality is a CT scan and the second imaging modality is an MR scan.
14. The system of claim 9 , wherein the updated volumetric model further includes a parameterized representation of the patient's vasculature.
15. The system of claim 9 , wherein the averaged first and second probabilities indicate a probability that a voxel of the volumetric model belongs to the patient's vasculature.
16. The system of claim 9 , wherein:
the first probability is a probability that each voxel of the volumetric model belongs to a lumen, and,
the second probability is a probability that each voxel of the volumetric model belongs to the lumen.
17. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for_executing a method for processing images to produce an anatomical model, the method comprising:
obtaining a first image acquisition of a patient at a first point in time, the first image acquisition comprising first lumen boundary information;
obtaining a second image acquisition of the patient at a second point in time, the second image acquisition comprising second lumen boundary information;
performing a segmentation of the first image acquisition to determine a first probability;
performing a segmentation of the second image acquisition to determine a second probability;
combining the first lumen boundary information and the second lumen boundary information;
averaging the first and second probabilities; and
generating an updated volumetric model of a target anatomy of the patient by updating a lumen boundary of a volumetric model based on the combined first lumen boundary information and second lumen boundary information and based on the averaged first and second probabilities.
18. The non-transitory computer readable medium of claim 17 , the method further comprising:
receiving a first image reconstruction based on the first image acquisition of the patient,
wherein generating the updated volumetric model is further based on the first image reconstruction.
19. The non-transitory computer readable medium of claim 18 , the method further comprising:
receiving a second image reconstruction based on the second image acquisition of the patient,
wherein generating the updated volumetric model is further based on the second image reconstruction.
20. The non-transitory computer readable medium of claim 17 , wherein the first image acquisition of the patient and the second image acquisition of the patient are generated using two different imaging modalities, wherein one of the imaging modalities is a CT scan.