高维成像新模型：高分辨率fMRI加速与量化
Novel Models for High-Dimensional Imaging: High-Resolution fMRI Acceleration and Quantification
AI总结：
针对功能磁共振成像同时提升时空分辨率与信噪比的需求，提出补丁张量低秩模型、基于物理的流形模型及体素级注意力网络，可在不增加扫描时间的前提下实现性能提升，效果优于对比方法。
摘要：
功能磁共振成像（fMRI）的目标包括实现高空间分辨率、高时间分辨率以及高信噪比（SNR）。为同时提升空间与时间分辨率，并保留OSSI的高信噪比优势，我们提出了用于快速采集、高分辨率fMRI重建及物理参数量化的新型流程。我们提出了补丁张量低秩模型、基于物理的流形模型以及体素级注意力网络。借助这些新型采集与重建模型，我们证明可在不增加扫描时间的前提下同时提升SNR与分辨率。所有提出的模型均优于其他对比方法，具备更高分辨率与更多功能信息。
英文摘要：
The goals of functional Magnetic Resonance Imaging (fMRI) include high spatial and temporal resolutions with a high signal-to-noise ratio (SNR). To simultaneously improve spatial and temporal resolutions and maintain the high SNR advantage of OSSI, we present novel pipelines for fast acquisition and high-resolution fMRI reconstruction and physics parameter quantification. We propose a patch-tensor low-rank model, a physics-based manifold model, and a voxel-wise attention network. With novel models for acquisition and reconstruction, we demonstrate that we can improve SNR and resolution simultaneously without compromising scan time. All the proposed models outperform other comparison approaches with higher resolution and more functional information.
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。
