Difference between revisions of "Projects:MGH-HeadAndNeck-RT"
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Proton therapy is used to deliver accurate doses of radiation to people undergoing cancer treatment. At the beginning of treatment, a personalized plan describing the amount of radiation and location to which it must be delivered is created for the patient. However, over the course of the treatment, which lasts weeks, a person's anatomy is likely to change. Adaptive radiotherapy aims to improve fractionated radiotherapy by re-optimizing the radiation treatment plan for each session. To update the plan, the CT images acquired during a treatment session are registered to the treatment plan and doses of radiation to be delivered are re-calculated accordingly. A correct segmentation is a pre-requisite for this dose re-optimization and is the starting point for this project. | Proton therapy is used to deliver accurate doses of radiation to people undergoing cancer treatment. At the beginning of treatment, a personalized plan describing the amount of radiation and location to which it must be delivered is created for the patient. However, over the course of the treatment, which lasts weeks, a person's anatomy is likely to change. Adaptive radiotherapy aims to improve fractionated radiotherapy by re-optimizing the radiation treatment plan for each session. To update the plan, the CT images acquired during a treatment session are registered to the treatment plan and doses of radiation to be delivered are re-calculated accordingly. A correct segmentation is a pre-requisite for this dose re-optimization and is the starting point for this project. | ||
| − | = | + | = Description = |
Initial experiments show that bony structures such as the mandible can be segmented accurately with a graph cut based algorithm. However, for soft tissue such as the brain stem, the intensity profile does not contain sufficient information for reasonably accurate segmentation. To deal with "soft boundaries" shape priors are necessary. They will be included in the following manner. First, a structure we are confident in will be segmented. Using probabilistic PCA a metric used to describe how likely the structure whose segmentation we have obtained is; this metric is essentially a description of how confident we are in the correct segmentation. Then, the location of this structure will be used as prior information(it becomes a landmark) to segment a more difficult structure. Iteratively, the nth structure to be segmented will have n-1 priors do draw information from with a confidence metric for each prior. The likely location of the nth structure, calculated as described above, will serve as an input to a graph cut algorithm, which will perform the optimization, in the form of a distance function. This distance function regulates how easy or difficult it is to cut links between neighbors(in other words, where the segmentation boundary will be drawn). | Initial experiments show that bony structures such as the mandible can be segmented accurately with a graph cut based algorithm. However, for soft tissue such as the brain stem, the intensity profile does not contain sufficient information for reasonably accurate segmentation. To deal with "soft boundaries" shape priors are necessary. They will be included in the following manner. First, a structure we are confident in will be segmented. Using probabilistic PCA a metric used to describe how likely the structure whose segmentation we have obtained is; this metric is essentially a description of how confident we are in the correct segmentation. Then, the location of this structure will be used as prior information(it becomes a landmark) to segment a more difficult structure. Iteratively, the nth structure to be segmented will have n-1 priors do draw information from with a confidence metric for each prior. The likely location of the nth structure, calculated as described above, will serve as an input to a graph cut algorithm, which will perform the optimization, in the form of a distance function. This distance function regulates how easy or difficult it is to cut links between neighbors(in other words, where the segmentation boundary will be drawn). | ||
Revision as of 18:28, 20 October 2009
Home < Projects:MGH-HeadAndNeck-RTBack to Georgia Tech Algorithms
Adaptive Radiotherapy for head, neck and thorax
Proton therapy is used to deliver accurate doses of radiation to people undergoing cancer treatment. At the beginning of treatment, a personalized plan describing the amount of radiation and location to which it must be delivered is created for the patient. However, over the course of the treatment, which lasts weeks, a person's anatomy is likely to change. Adaptive radiotherapy aims to improve fractionated radiotherapy by re-optimizing the radiation treatment plan for each session. To update the plan, the CT images acquired during a treatment session are registered to the treatment plan and doses of radiation to be delivered are re-calculated accordingly. A correct segmentation is a pre-requisite for this dose re-optimization and is the starting point for this project.
Description
Initial experiments show that bony structures such as the mandible can be segmented accurately with a graph cut based algorithm. However, for soft tissue such as the brain stem, the intensity profile does not contain sufficient information for reasonably accurate segmentation. To deal with "soft boundaries" shape priors are necessary. They will be included in the following manner. First, a structure we are confident in will be segmented. Using probabilistic PCA a metric used to describe how likely the structure whose segmentation we have obtained is; this metric is essentially a description of how confident we are in the correct segmentation. Then, the location of this structure will be used as prior information(it becomes a landmark) to segment a more difficult structure. Iteratively, the nth structure to be segmented will have n-1 priors do draw information from with a confidence metric for each prior. The likely location of the nth structure, calculated as described above, will serve as an input to a graph cut algorithm, which will perform the optimization, in the form of a distance function. This distance function regulates how easy or difficult it is to cut links between neighbors(in other words, where the segmentation boundary will be drawn).
Current State of Work
Currently, we are collecting patient scan with physician drawn label maps to build the model for shape analysis. In parallel, synthetic data is being used to develop/test the algorithm used to calculate the likelihood metric and the likely location of a structure given previously segmented structures. The graph cut implementation is read to accept the unsigned distance map as prior information.
Key Investigators
- Georgia Tech: Ivan Kolesov, Vandana Mohan, and Allen Tannenbaum
- Massachusetts General Hospital: Gregory Sharp
Publications
In Press
I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).
