Difference between revisions of "2015 Summer Project Week:TrackerlessMRIUSFusion"
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* Utsav Pardasani | * Utsav Pardasani | ||
* Adam Rankin | * Adam Rankin | ||
| + | * Robert Owen, BK | ||
| + | * Andrey Fedorov, BWH | ||
==Project Description== | ==Project Description== | ||
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* Evaluate and develop various image-similarity metrics in a geometrically constrained search space. | * Evaluate and develop various image-similarity metrics in a geometrically constrained search space. | ||
* Rather than relying on an optimizer, the goal would be to find a fast similarity metric that enables a dense sampling of the objective function search space. | * Rather than relying on an optimizer, the goal would be to find a fast similarity metric that enables a dense sampling of the objective function search space. | ||
| + | * Make use of the BITE dataset for US-MRI images. http://www.bic.mni.mcgill.ca/Services/ServicesBITE | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | * Initial good results with the LC2 metric constrained to a craniotomy site. | + | * Initial good results with the LC2 metric constrained to a craniotomy site with 3 degrees of freedom. |
* Given a pair of registered US-MRI images, the module calculates the similarity metric output perturbed between the two images. | * Given a pair of registered US-MRI images, the module calculates the similarity metric output perturbed between the two images. | ||
| + | * Plan to integrate information from image-features per Matthew Toews advice. | ||
| + | * Working toward expanding the code to work with prostate dataset from Andre | ||
</div> | </div> | ||
</div> | </div> | ||
Latest revision as of 15:07, 24 June 2015
Home < 2015 Summer Project Week:TrackerlessMRIUSFusion
Key Investigators
- Utsav Pardasani
- Adam Rankin
- Robert Owen, BK
- Andrey Fedorov, BWH
Project Description
Objective
- Work toward a real-time trackerless image-based registration that is constrained by a clinically relevant geometry. (With special emphasis on intra-operative neuroimaging)
- Can support calibration / registration with tracked-systems
Approach, Plan
- Evaluate and develop various image-similarity metrics in a geometrically constrained search space.
- Rather than relying on an optimizer, the goal would be to find a fast similarity metric that enables a dense sampling of the objective function search space.
- Make use of the BITE dataset for US-MRI images. http://www.bic.mni.mcgill.ca/Services/ServicesBITE
Progress
- Initial good results with the LC2 metric constrained to a craniotomy site with 3 degrees of freedom.
- Given a pair of registered US-MRI images, the module calculates the similarity metric output perturbed between the two images.
- Plan to integrate information from image-features per Matthew Toews advice.
- Working toward expanding the code to work with prostate dataset from Andre
