K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
This work addresses the need for faster MRI scans to improve accessibility, representing an incremental advancement in deep generative models for medical imaging.
The authors tackled the problem of long MRI acquisition times by proposing a k-space and image domain collaborative generative model for parallel MRI reconstruction, achieving less error and greater stability compared to state-of-the-art methods under various acceleration factors.
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Experimental comparisons with the state-of-the-arts demonstrated that the proposed hybrid method has less error in reconstruction accuracy and is more stable under different acceleration factors