2014 Summer Project Week:Stroke-SuperResolution

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Home < 2014 Summer Project Week:Stroke-SuperResolution

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.


  • 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.