Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction
This work addresses the slow and expensive MRI acquisition process for medical imaging, particularly in cardiac MRI, by enhancing reconstruction quality, though it appears incremental as it builds on existing GAN methods.
The authors tackled the problem of slow MRI acquisition by proposing Recon-GLGAN, a GAN-based model that incorporates global and local contextual information to improve reconstruction, showing significant performance improvements over baseline models and reconstructions yielding segmentation results similar to fully sampled images.
Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in better reconstruction. For instance, in the case of cardiac MRI, the physician would be interested in the heart region which is of diagnostic relevance while excluding the peripheral regions. In this work, we show that incorporating prior information about a region of interest in the model would offer better performance. Thereby, we propose a novel GAN based architecture, Reconstruction Global-Local GAN (Recon-GLGAN) for MRI reconstruction. The proposed model contains a generator and a context discriminator which incorporates global and local contextual information from images. Our model offers significant performance improvement over the baseline models. Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance. We also demonstrate that the reconstructions from the proposed method give segmentation results similar to fully sampled images.