DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI
This addresses the need for more efficient and generalizable medical image synthesis methods, though it appears incremental as it builds on existing convolutional approaches.
The paper tackles the problem of cross-modal image synthesis in MRI by proposing DOTE, a dual convolutional filter learning approach that reduces required training data size while achieving superior performance in super-resolution and cross-modality synthesis tasks compared to state-of-the-art methods.
Cross-modal image synthesis is a topical problem in medical image computing. Existing methods for image synthesis are either tailored to a specific application, require large scale training sets, or are based on partitioning images into overlapping patches. In this paper, we propose a novel Dual cOnvolutional filTer lEarning (DOTE) approach to overcome the drawbacks of these approaches. We construct a closed loop joint filter learning strategy that generates informative feedback for model self-optimization. Our method can leverage data more efficiently thus reducing the size of the required training set. We extensively evaluate DOTE in two challenging tasks: image super-resolution and cross-modality synthesis. The experimental results demonstrate superior performance of our method over other state-of-the-art methods.