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Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality

Authors :
Feng, Chun-Mei
Fu, Huazhu
Zhou, Tianfei
Xu, Yong
Shao, Ling
Zhang, David
Publication Year :
2021

Abstract

Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts. Recently, many approaches have been developed to reconstruct full-sampled images from partially observed measurements to accelerate MR imaging. However, most approaches focused on reconstruction over a single modality, neglecting the discovery of correlation knowledge between the different modalities. Here we propose a Multi-modal Aggregation network for mR Image recOnstruction with auxiliary modality (MARIO), which is capable of discovering complementary representations from a fully sampled auxiliary modality, with which to hierarchically guide the reconstruction of a given target modality. This implies that our method can selectively aggregate multi-modal representations for better reconstruction, yielding comprehensive, multi-scale, multi-modal feature fusion. Extensive experiments on IXI and fastMRI datasets demonstrate the superiority of the proposed approach over state-of-the-art MR image reconstruction methods in removing artifacts.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2110.08080
Document Type :
Working Paper