POCS-Augmented CycleGAN for MR Image Reconstruction

Abstract

Recent years have seen increased research interest in replacing the computationally intensive Magnetic resonance (MR) image reconstruction process with deep neural networks. We claim in this paper that the traditional image reconstruction methods and deep learning (DL) are mutually complementary and can be combined to achieve better image reconstruction quality. To test this hypothesis, a hybrid DL image reconstruction method was proposed by combining a state-of-the-art deep learning network, namely a generative adversarial network with cycle loss (CycleGAN), with a traditional data reconstruction algorithm: Projection Onto Convex Set (POCS). The output of the first iteration’s training results of the CycleGAN was updated by POCS and used as the extra training data for the second training iteration of the CycleGAN. The method was validated using sub-sampled Magnetic resonance imaging data. Compared with other state-of-the-art, DL-based methods (e.g., U-Net, GAN, and RefineGAN) and a traditional method (compressed sensing), our method showed the best reconstruction results.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

“…The artifact scores of motion-corrupted images and MC-Net predictions were compared using the one-sided Wilcoxon signed-rank test after averaging the reader scores. For simulated motions, artifact scores were compared for three ranges of summed standard deviations: [0-5] (mm/ • ), (6)(7)(8)(9)(10) (mm/ • ), and (10)(11)(12)(13)(14)(15) (mm/ • ).…”

“…Conversely, the scores of the corrupted images consistently worsened with the degree of motion such that most images with >7.5 mm/ • motion (standard deviation across scan) were rated as having "moderate" or "severe" artifacts (blue lines). The visual scores of images processed using MC-Net improved significantly for motions from [0-5] mm/ • (p = 5 × 10 −4 on Wilcoxon signed-rank test), [5][6][7][8][9][10] mm/ • (p = 6 × 10 −5 ), and [10][11][12][13][14][15] mm/ • (p = 2 × 10 −3 ). Importantly, even images with the most severe ranges of simulated motion (>7.5 mm/ • ; green versus blue lines; left column) were rated to have only "minor artifacts" (on average) after correction.…”

“…It is worth noting that this model was not trained and tested for extremely large motions. Extreme cases can be added by fine-tuning the model with new training samples and intermediate samples, possibly to be generated using a Projection-On-Convex-Setbased cycle Generative Adversarial Network [11].…”

“…Therefore, retrospective motion correction by means of post-processing provides a good complement. One promising approach involves deep learning (DL) [1,2,[9][10][11], using deep convolutional neural networks (DCNN) or other network architectures with supervised learning. Given sufficient training pairs (inputs and reference images), DCNNs can be trained to recognize the transformation from the input (motion-corrupted image) to the reference (motion-free image).…”