Modality Propagation: Coherent Synthesis of Subject-Specific Scans with Data-Driven Regularization.

We propose a general database-driven framework for coherent synthesis of subject-specific scans of desired modality, which adopts and generalizes the patch-based label propagation (LP) strategy. While modality synthesis has received increased attention lately, current methods are mainly tailored to specific applications. On the other hand, the LP framework has been extremely successful for certain segmentation tasks, however, so far it has not been used for estimation of entities other than categorical segmentation labels. We approach the synthesis task as a modality propagation, and demonstrate that with certain modifications the LP framework can be generalized to continuous settings providing coherent synthesis of different modalities, beyond segmentation labels. To achieve high-quality estimates we introduce a new data-driven regularization scheme, in which we integrate intermediate estimates within an iterative search-and-synthesis strategy. To efficiently leverage population data and ensure coherent synthesis, we employ a spatio-population search space restriction. In experiments, we demonstrate the quality of synthesis of different MRI signals (T2 and DTI-FA) from a T1 input, and show a novel application of modality synthesis for abnormality detection in multi-channel MRI of brain tumor patients.