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arXiv 1905.009852019-05-02cs.LGeess.IVstat.ML

Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction

GE Global Research（GE全球研究中心）；Tel Aviv University（特拉维夫大学）；GE Healthcare（GE医疗）

Itzik Malkiel, Sangtae Ahn, Valentina Taviani, Anne Menini, Lior Wolf, Christopher J. Hardy

英文摘要：

Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a natural appearance. In this work, we enhance the image quality by using a Conditional Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique that stabilizes the training and minimizes the degree of artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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