Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks

Reconstruction solvers requiring too muh time to recontruct images or videos is nothing new as pointed out four years ago ( Sunday Morning Insight: Faster Than a Blink of an Eye). Last week, we saw another example of that in DiffuserCam: Lensless Single-exposure 3D Imaging where one could read:

A typical reconstruction requires at least 200 iterations. Solving for 2048 × 2048 × 128 = 537 million voxels takes 26 minutes (8 seconds per iteration) on a 144-core workstation and requires 85 Gigabytes of RAM. A smaller reconstruction (512 × 512 × 128 = 33.5 million voxels) takes 3 minutes (1 second per iteration) on a 4-core laptop with 16 Gigabytes of RAM.

Hence much like a few folks, it is becoming interesting to see if there is a way to learn to reconstruction solvers (see the recent Learning to Invert: Signal Recovery via Deep Convolutional Networks) so that reconstruction can take seconds or less rather than hours. Here is a new and interesting example of that using GANs.

Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks by Tran Minh Quan, Thanh Nguyen-Duc, Won-Ki Jeong

Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled k-space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate - the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a 256 x 256 image, because it is one-way deployment on a feedforward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.

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