Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice-to-volume reconstruction (SVR) methods are time-consuming and require multiple orthogonal stacks for reconstruction. While learning-based SVR approaches have significantly reduced the time required at the inference stage, they heavily rely on ground truth information for training, which is inaccessible in practice. To address these challenges, we propose GaussianSVR, a self-supervised framework for slice-to-volume reconstruction. GaussianSVR represents the target volume using 3D Gaussian representations to achieve high-fidelity reconstruction. It leverages a simulated forward slice acquisition model to enable self-supervised training, alleviating the need for ground-truth volumes. Furthermore, to enhance both accuracy and efficiency, we introduce a multi-resolution training strategy that jointly optimizes Gaussian parameters and spatial transformations across different resolution levels. Experiments show that GaussianSVR outperforms the baseline methods on fetal MR volumetric reconstruction. Code will be available upon acceptance.

翻译：从运动伪影干扰的二维切片堆栈中重建三维胎儿磁共振体积是一项关键且具有挑战性的任务。传统的切片到体积重建方法耗时较长，且通常需要多组正交扫描堆栈才能完成重建。尽管基于学习的SVR方法在推理阶段显著缩短了处理时间，但其训练过程严重依赖真实体积数据作为监督信号，而这在实际临床场景中往往难以获取。为应对这些挑战，我们提出了GaussianSVR——一种自监督的切片到体积重建框架。该方法采用三维高斯表示对目标体积进行建模，以实现高保真度的三维重建。通过构建模拟的前向切片采集模型，该框架实现了完全自监督的训练，从而摆脱了对真实体积数据的依赖。此外，为同步提升重建精度与计算效率，我们提出了一种多分辨率联合训练策略，该策略能在不同分辨率层级上同步优化高斯表示参数与空间变换矩阵。实验结果表明，GaussianSVR在胎儿磁共振体积重建任务中优于现有基准方法。代码将在论文录用后开源。