快速融合多角度2D切片重建高质量3D影像，10秒内完成。
Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization
- 用轻量级优化融合多视角2D切片，恢复3D结构。
- 胎儿脑MRI重建<10秒，切片对齐仅需1秒，精度媲美顶尖方法。
- 适合需要实时三维反馈的临床MRI场景。
全卷积网络因能学习多尺度表征并实现端到端推理，已成为现代医学成像的基石。然而其在切片到体积重建（SVR）中的潜力仍待挖掘，即从错位的2D扫描中联合估计3D解剖结构与切片姿态。本文提出一种快速卷积框架，通过多尺度非刚性形变场融合多个正交2D切片堆栈，重建连贯3D结构，并优化切片对齐。应用于胎儿脑MRI时，该方法可在10秒内完成高质量3D体积重建，其中切片注册仅耗时1秒，精度达到当前最优迭代式SVR流水线水平，显著提升效率。该框架可推广至胎儿体部及胎盘MRI等其他SVR任务。此外，其快速推理能力为磁共振扫描过程中实现扫描仪侧实时三维反馈提供了可能。
原文摘要 · Abstract (English)
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.
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