GDoT: A gated dual domain transformer for enhanced MRI off-resonance correction

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jaesin Ahn, Heechul Jung
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引用次数: 0

Abstract

Deep learning-based MRI reconstruction methods have gained significant attention recently due to the need for accelerated MRI scans. However, existing deep learning-based methods for off-resonance correction rely on simple CNNs, resulting in suboptimal solutions. In this paper, we propose a gated dual domain transformer with gated spatial projection and gated frequency projection to effectively handle complex-valued MRI, as the first attempt to utilize transformer-based model for off-resonance correction. Additionally, we introduce a selective perceptual loss with a novel test-time translation-merger to reconstruct perceptually high-quality images without checkerboard artifacts. Experiments on both simulated and real off-resonance MRI datasets demonstrate the effectiveness of our approach. Furthermore, we also present ablation studies to determine the optimal design choices.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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