# DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI

## TL;DR

This paper introduces DOTE, a dual convolutional filter learning method that improves MRI image super-resolution and cross-modality synthesis by efficiently leveraging data and self-optimization, outperforming existing techniques.

## Contribution

The paper presents a novel closed-loop filter learning strategy that enhances MRI image synthesis and super-resolution with less training data and better performance.

## Key findings

- DOTE outperforms state-of-the-art methods in MRI super-resolution.
- DOTE effectively reduces training data requirements.
- The approach demonstrates superior results in cross-modality synthesis.

## Abstract

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.