利用无监督学习增强的 3T fMRI 数据重建视网膜视觉图像
Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning
AI总结：
针对 7T fMRI 数据稀缺及 3T 扫描短暂低质的问题，提出基于无监督 GAN 的跨 7T/3T 非配对数据增强框架，以提升 3T fMRI 的视觉图像重建效果。
摘要：
从大脑活动重建人类视觉输入，尤其是通过功能磁共振成像（fMRI），为揭示人类视觉系统机制提供了富有前景的途径。尽管深度学习方法在提升视觉重建质量和可解释性方面取得了显著进展，但目前仍高度依赖高质量、长时程、针对特定受试者的 7-Tesla fMRI 实验。挑战在于整合多样且规模较小的 3-Tesla 数据集，或适应仅具有短暂、低质量 fMRI 扫描的新受试者。针对这些限制，我们提出了一种新颖框架，通过无监督生成对抗网络（GAN）生成增强的 3T fMRI 数据，并利用分别来自 7T 和 3T 的两个不同 fMRI 数据集进行非配对训练。该方法旨在克服高质量 7-Tesla 数据稀缺，以及 3-Tesla 实验中短暂、低质量扫描所带来的挑战。本文展示了增强后 3T fMRI 数据的重建能力，突出表明与在单一受试者上训练和测试的数据密集型方法相比，其能够生成更优的输入视觉图像。
英文摘要：
The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechanisms of the human visual system. Despite the significant strides made by deep learning methods in improving the quality and interpretability of visual reconstruction, there remains a substantial demand for high-quality, long-duration, subject-specific 7-Tesla fMRI experiments. The challenge arises in integrating diverse smaller 3-Tesla datasets or accommodating new subjects with brief and low-quality fMRI scans. In response to these constraints, we propose a novel framework that generates enhanced 3T fMRI data through an unsupervised Generative Adversarial Network (GAN), leveraging unpaired training across two distinct fMRI datasets in 7T and 3T, respectively. This approach aims to overcome the limitations of the scarcity of high-quality 7-Tesla data and the challenges associated with brief and low-quality scans in 3-Tesla experiments. In this paper, we demonstrate the reconstruction capabilities of the enhanced 3T fMRI data, highlighting its proficiency in generating superior input visual images compared to data-intensive methods trained and tested on a single subject.
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。
