Fully convolutional networks have become the backbone of modern medical imaging due to their ability to learn multi-scale representations and perform end-to-end inference. Yet their potential for slice-to-volume reconstruction (SVR), the task of jointly estimating 3D anatomy and slice poses from misaligned 2D acquisitions, remains underexplored. We introduce a fast convolutional framework that fuses multiple orthogonal 2D slice stacks to recover coherent 3D structure and refines slice alignment through lightweight model-based optimization. Applied to fetal brain MRI, our approach reconstructs high-quality 3D volumes in under 10s, with 1s slice registration and accuracy on par with state-of-the-art iterative SVR pipelines, offering more than speedup. The framework uses non-rigid displacement fields to represent transformations, generalizing to other SVR problems like fetal body and placental MRI. Additionally, the fast inference time paves the way for real-time, scanner-side volumetric feedback during MRI acquisition.

翻译：全卷积网络因其能够学习多尺度表示并执行端到端推理，已成为现代医学影像的支柱。然而，其在切片到体积重建任务中的潜力——即从错位的二维采集中联合估计三维解剖结构和切片姿态——仍未得到充分探索。我们引入了一种快速卷积框架，该框架融合多个正交的二维切片堆叠以恢复连贯的三维结构，并通过轻量级的基于模型优化来细化切片对齐。应用于胎儿脑部MRI时，我们的方法在10秒内重建出高质量的三维体积，其中切片配准仅需1秒，且精度与最先进的迭代式SVR流程相当，同时实现了超过数量级的加速。该框架使用非刚性位移场来表示变换，可推广至其他SVR问题，如胎儿身体和胎盘MRI。此外，快速的推理时间为MRI采集过程中实现实时的、扫描仪端的体积反馈铺平了道路。