用高斯表示自监督重建胎儿MRI，无需真实标签
Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI
- 用3D高斯表示体积，模拟切片采集过程实现自监督训练
- 多分辨率策略优化参数与空间变换，提升重建精度与效率
- 适合无真实标签的胎儿MRI重建，尤其适用于运动伪影场景
从运动伪影的2D切片堆栈中重建3D胎儿MR体积是一项关键且具有挑战性的任务。传统切片到体积分割（SVR）方法耗时且需多个正交切片堆栈。基于学习的SVR方法虽显著缩短了推理时间，但严重依赖训练所需的真值信息，实际中难以获取。为此，我们提出GaussianSVR，一种自监督切片到体积分割框架。GaussianSVR采用3D高斯表示目标体积，实现高保真重建。其利用模拟的前向切片采集模型实现自监督训练，无需真值体积。此外，为提升准确性和效率，我们引入多分辨率训练策略，联合优化不同分辨率下的高斯参数与空间变换。实验表明，GaussianSVR在胎儿MR体积分割任务中优于基线方法。代码已开源：https://github.com/Yinsong0510/GaussianSVR-Self-Supervised-Slice-to-Volume-Reconstruction-with-Gaussian-Representations。
原文摘要 · Abstract (English)
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.
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