Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks

2020/12/18 by Muzaffer Özbey, Mahmut Yurt, Özbey, Muzaffer +5
Computer Science · Engineering · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image Processing and 3D Reconstruction #Image and Video Processing (eess.IV) #cs.CV #eess.IV #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2101.05218

Presented on April 4, 2020 in the IEEE International Symposium on Biomedical Imaging (ISBI) 2020

arxiv created 2020/12/18 · openalex publication_date 2020/12/18 · arxiv updated 2021/01/14 · openalex created_date 2021/01/18 · openalex updated_date 2026/07/28

Abstract

Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input. Cross-sectional models can decrease the model complexity, but they may lead to discontinuity artifacts. On the other hand, volumetric models can alleviate the discontinuity artifacts, but they might suffer from loss of spatial resolution due to increased model complexity coupled with scarce training data. To mitigate the limitations of both approaches, we propose a novel model that progressively recovers the target volume via simpler synthesis tasks across individual orientations.

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