Abstract #4220

Magnetic Resonance Imaging Cross-Modality Synthesis

Yawen Huang¹, Leandro Beltrachini¹, Ling Shao², and Alejandro Frangi¹

¹Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, United Kingdom, ² Department of Computer Science and Digital Technologies, Northumbria University, Newcastle, United Kingdom

Multi-modality MRI protocols are becoming standard in the everyday clinical practise. The advantages of such acquisitions were shown to be fundamental in a wide range of applications, such as medical diagnosis and image segmentation. However, the implementation of these protocols tends to be time-consuming, consisting in one key limitation. In this paper we address this problem by presenting a novel method for synthesising any MRI modality from a single acquired image. This is done using machine learning techniques for dictionary learning. Results show that our approach can lead to significant performance over the state-of-the-art methods.

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