Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging

TL;DR

This paper introduces a data-driven multi-modality imaging strategy using multi-sensor learning to enhance CT and MRI integration, surpassing traditional post hoc fusion methods for more accurate biomedical imaging.

Contribution

It presents a novel multi-sensor learning framework that leverages inter-modality features to enable direct, synergistic hybridization of CT and MRI imaging.

Findings

Effective synergetic CT-MRI brain imaging demonstrated

Breaks down boundaries of traditional imaging modalities

Potential for broad applications across disciplines

Abstract

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.