Unified Dual-Modality Image Reconstruction With Dual Dictionaries

To utilize the synergy between CT and MR datasets from an object at the same time, a unified dual-modality image reconstruction approach is proposed using a dual-dictionary learning technique. The key is to establish a knowledge-based connection between these two datasets for a tight fusion of different imaging modalities. Our scheme consists of three inter-related elements: dual-dictionary learning, CT image reconstruction, and MR image reconstruction. Our experiments show that even with highly under-sampled MR data and few x-ray projections, we can still satisfactorily reconstruct both MR and CT images. This approach can be potentially useful for a CT-MRI system.