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Multiple imaging modalities are often used for disease diagnosis, prediction,or population-based analyses. However, not all modalities might be availabledue to cost, different study designs, or changes in imaging technology. If thedifferences between the types of imaging are small, data harmonizationapproaches can be used; for larger changes, direct image synthesis approacheshave been explored. In this paper, we develop an approach based on multi-modalmetric learning to synthesize images of diverse modalities. We use metriclearning via multi-modal image retrieval, resulting in embeddings that canrelate images of different modalities. Given a large image database, thelearned image embeddings allow us to use k-nearest neighbor (k-NN) regressionfor image synthesis. Our driving medical problem is knee osteoarthritis (KOA),but our developed method is general after proper image alignment. We test ourapproach by synthesizing cartilage thickness maps obtained from 3D magneticresonance (MR) images using 2D radiographs. Our experiments show that theproposed method outperforms direct image synthesis and that the synthesizedthickness maps retain information relevant to downstream tasks such asprogression prediction and Kellgren-Lawrence grading (KLG). Our results suggestthat retrieval approaches can be used to obtain high-quality and meaningfulimage synthesis results given large image databases.

MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities