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 is available at https://github.com/Yinsong0510/GaussianSVR-Self-Supervised-Slice-to-Volume-Reconstruction-with-Gaussian-Representations.

翻译：从受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。传统的切片到体素重建（SVR）方法耗时长，且需要多个正交堆栈才能完成重建。尽管基于学习的SVR方法显著缩短了推理阶段所需时间，但它们严重依赖难以在实际中获取的真实标签信息进行训练。为解决这些挑战，我们提出GaussianSVR——一种用于切片到体素重建的自监督框架。GaussianSVR采用三维高斯表示来表征目标体积，从而实现高保真度重建。该方法利用模拟的前向切片采集模型实现自监督训练，无需依赖真实体积数据。此外，为提升精度与效率，我们引入了多分辨率训练策略，在不同分辨率层级上联合优化高斯参数与空间变换。实验表明，GaussianSVR在胎儿磁共振体积重建任务上优于基线方法。代码已开源：https://github.com/Yinsong0510/GaussianSVR-Self-Supervised-Slice-to-Volume-Reconstruction-with-Gaussian-Representations.