POCS-Augmented CycleGAN for MR Image Reconstruction
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced MRI Techniques and Applications · Medical Imaging Techniques and Applications · Medical Image Segmentation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD 21201, USA
Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19121, USA
Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD 21250, USA
Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang 330209, China
Funding
National Institute on Aging
References (39)
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