Difference between revisions of "2014 Summer Project Week:Stroke-SuperResolution"
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Image:STROKE_SR1.png|Super-Resolution initial results -- top-left is a 'real' image, from which we simulate a 1x1x7mm clinical image. The bottom images are state of the art interpolations. The top right is our current result. | Image:STROKE_SR1.png|Super-Resolution initial results -- top-left is a 'real' image, from which we simulate a 1x1x7mm clinical image. The bottom images are state of the art interpolations. The top right is our current result. | ||
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Revision as of 16:02, 23 June 2014
Home < 2014 Summer Project Week:Stroke-SuperResolution
Clinical Stroke Image
Super-Resolution initial results -- top-left is a 'real' image, from which we simulate a 1x1x7mm clinical image. The bottom images are state of the art interpolations. The top right is our current result.
Key Investigators
- Adrian Dalca, Ramesh Sridharan, Polina Golland, MIT
Project Description
To improve results for Large Datasets of clinical-quality data, we are investigating super-resolution methods. Here, we are using a patch-based approach with MRF priors and utilizing only the current dataset, without an external training dataset.
Objective
- We will investigate/implement a scale-space MRF inference based on patch search results.
Approach, Plan
- We are developing a patch library in MATLAB, and need to apply it in a scale-space framework to the T2-FLAIR dataset.
