Difference between revisions of "2017 Winter Project Week/Population Based Image Imputation"
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* Test whether the type of operations we do (most many many small matrix multiplications and lookup) can be made significantly more efficient on a GPU  * Test whether the type of operations we do (most many many small matrix multiplications and lookup) can be made significantly more efficient on a GPU  
* Implement algorithm updates on GPU  * Implement algorithm updates on GPU  
−  * investigate banding sideeffects.  +  * investigate banding sideeffects. 
<! Progress and Next steps bullet points (fill out at the end of project week) >  <! Progress and Next steps bullet points (fill out at the end of project week) >  
*  * 
Revision as of 16:38, 9 January 2017
Home < 2017 Winter Project Week < Population Based Image ImputationKey Investigators
 Adrian Dalca, MIT
 Katie Bouman, MIT
 Polina Golland, MIT
Project Description
We developed a model for image imputation  or restoration  for clinical quality images where slice separation (e.g. 6mm) is significantly larger than slice resolution (e.g. 1mm^2). Our model captures statistical correlations within a collection of clinical images from a population of subjects at each location in the image. This means we learn different model parameters for many image locations involving methematical updates that involve many small matrix multiplications. In this project we want to investigate the potential for GPUs to help in the runtime of the algorithm.
Objective  Approach and Plan  Progress and Next Steps 


