Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging
This work addresses the challenge of improving multi-modality imaging accuracy for clinical and biomedical applications, representing an incremental advancement through a novel framework.
The paper tackled the problem of limited accuracy in multi-modality imaging by proposing a data-driven strategy using multi-sensor learning to synergize CT and MRI, resulting in augmented imaging capabilities as demonstrated in brain imaging.
Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI. We reveal two distinct types of features in multi-modality imaging, namely intra- and inter-modality features, and present a multi-sensor learning (MSL) framework to utilize the crossover inter-modality features for augmented multi-modality imaging. The MSL imaging approach breaks down the boundaries of traditional imaging modalities and allows for optimal hybridization of CT and MRI, which maximizes the use of sensory data. We showcase the effectiveness of our DMI strategy through synergetic CT-MRI brain imaging. The principle of DMI is quite general and holds enormous potential for various DMI applications across disciplines.