Difference between revisions of "2014 Summer Project Week:Stroke-SuperResolution"
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| − | To improve results for | + | Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, analysis, will fail. 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. |
Revision as of 16:04, 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
Due to the low quality of clinical images (often with many artifacts, 7mm thick slices, etc), most standard algorithms, such as those for registration, segmentation, analysis, will fail. 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.
