Difference between revisions of "2015 Summer Project Week:TrackerlessMRIUSFusion"

From NAMIC Wiki
Jump to: navigation, search
Line 17: Line 17:
 
<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
 
* 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.
* Implement the kernels in PyCuda
+
* 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.
 
</div>
 
</div>
 
<div style="width: 27%; float: left; padding-right: 3%;">
 
<div style="width: 27%; float: left; padding-right: 3%;">

Revision as of 08:57, 22 June 2015

Home < 2015 Summer Project Week:TrackerlessMRIUSFusion


Key Investigators

  • Utsav Pardasani

Project Description

Objective

  • Work toward a real-time trackerless image-based registration that is constrained by a clinically relevant geometry.

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.

Progress

  • Initial good results with the LC2 metric constrained to a craniotomy site.
  • Given a pair of registered US-MRI images, the module calculates the similarity metric output perturbed between the two images.