用3D高斯点云重建低采样率MRI，无需训练数据
Three-Dimensional MRI Reconstruction with Gaussian Representations: Tackling the Undersampling Problem
- 用3D高斯分布显式表示磁共振体积，替代传统体素
- 在自监督框架下实现与主流方法相当的图像质量
- 首次将3DGS用于复杂值信号的MRI重建，适合医疗影像研究者
三维高斯溅射（3DGS）在计算机视觉领域表现优异，但尚未应用于磁共振成像（MRI）。本研究探索其在从欠采样k-space数据重建各向同性分辨率3D MRI中的潜力。提出新型框架3D高斯MRI（3DGSMR），采用3D高斯分布作为磁共振体积的显式表示。实验表明，该方法可有效重建体素化MR图像，质量与文献中成熟的3D MRI重建技术相当。值得注意的是，3DGSMR在自监督框架下运行，无需大规模训练数据或预先训练模型。该方法在领域内具有显著创新性，包括首次将3DGS适配至MRI重建，并创新性地将现有3DGS方法用于处理以复数形式表示的MR信号。
原文摘要 · Abstract (English)
Three-Dimensional Gaussian Splatting (3DGS) has shown substantial promise in the field of computer vision, but remains unexplored in the field of magnetic resonance imaging (MRI). This study explores its potential for the reconstruction of isotropic resolution 3D MRI from undersampled k-space data. We introduce a novel framework termed 3D Gaussian MRI (3DGSMR), which employs 3D Gaussian distributions as an explicit representation for MR volumes. Experimental evaluations indicate that this method can effectively reconstruct voxelized MR images, achieving a quality on par with that of well-established 3D MRI reconstruction techniques found in the literature. Notably, the 3DGSMR scheme operates under a self-supervised framework, obviating the need for extensive training datasets or prior model training. This approach introduces significant innovations to the domain, notably the adaptation of 3DGS to MRI reconstruction and the novel application of the existing 3DGS methodology to decompose MR signals, which are presented in a complex-valued format.
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