通过多传感器学习融合CT与MRI数据，提升医学影像精度。
Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging
- 设计多传感器学习框架，挖掘跨模态特征以增强成像
- 实现CT与MRI协同成像，显著提升脑部影像质量
- 适用于多种医学影像场景，具有广泛推广潜力
多模态成像在临床实践和生物医学研究中广泛应用，用于全面理解成像对象。目前，多模态成像通常依赖于独立重建图像后通过互信息或空间配准硬件进行融合，这限制了成像的准确性和实用性。本文研究了一种数据驱动的多模态成像（DMI）策略，用于协同生成CT与MRI图像。我们揭示了多模态成像中的两类特征：模态内特征和模态间特征，并提出多传感器学习（MSL）框架，利用交叉模态特征实现增强型多模态成像。该方法打破了传统成像模态的边界，实现了CT与MRI的最优混合，最大化利用感官数据。通过脑部CT-MRI协同成像展示了本策略的有效性。该DMI原理具有高度通用性，在多个学科领域具有巨大应用潜力。
原文摘要 · Abstract (English)
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.
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