2017 Winter Project Week/Population Based Image Imputation

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Home < 2017 Winter Project Week < Population Based Image Imputation

Key Investigators

  • Adrian Dalca, MIT, MGH
  • Katie Bouman, MIT
  • Mert Sabuncu, MGH
  • 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
  • To test (and implement) a GPU version of Population Based Image Imputation, which learns a sparse-observation low-dimensional Gaussian Mixture Model at many locations in an image.
  • Test whether the type of operations we do (most many many small matrix multiplications and look-up) can be made significantly more efficient on a GPU
  • Implement algorithm updates on GPU
  • investigate banding side-effects.

Background and References