Tree-encoded conditional random fields for image synthesis

Amod Jog, Aaron Carass, Dzung L. Pham, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review


Magnetic resonance imaging (MRI) is the dominant modality for neuroimaging in clinical and research domains. The tremendous versatility of MRI as a modality can lead to large variability in terms of image contrast, resolution, noise, and artifacts. Variability can also manifest itself as missing or corrupt imaging data. Image synthesis has been recently proposed to homogenize and/or enhance the quality of existing imaging data in order to make them more suitable as consistent inputs for processing. We frame the image synthesis problem as an inference problem on a 3-D continuous-valued conditional random field (CRF).We model the conditional distribution as a Gaussian by defining quadratic association and interaction potentials encoded in leaves of a regression tree. The parameters of these quadratic potentials are learned by maximizing the pseudo-likelihood of the training data. Final synthesis is done by inference on this model. We applied this method to synthesize T2-weighted images from T1-weighted images, showing improved synthesis quality as compared to current image synthesis approaches. We also synthesized Fluid Attenuated Inversion Recovery (FLAIR) images, showing similar segmentations to those obtained from real FLAIRs. Additionally, we generated super-resolution FLAIRs showing improved segmentation.


  • Conditional random field
  • Image synthesis
  • Magnetic resonance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Tree-encoded conditional random fields for image synthesis'. Together they form a unique fingerprint.

Cite this