Projects:MGH-HeadAndNeck-RT
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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. Registering a patient scan to a model (ex.Atlas) provides important prior information for a segmentation algorithm and in the other direction, having segmentation of a structure, can be used for better registration; hence, segmentation and registration must be done concurrently.
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. Coupled Segmentation for Anatomical Structures by Combining Shape and Relational Spatial Information. MTNS 2010.
