This paper investigates a 2D to 3D image translation method with a
straightforward technique, enabling correlated 2D X-ray to 3D CT-like
reconstruction. We observe that existing approaches, which integrate
information across multiple 2D views in the latent space, lose valuable signal
information during latent encoding. Instead, we simply repeat and concatenate
the 2D views into higher-channel 3D volumes and approach the 3D reconstruction
challenge as a straightforward 3D to 3D generative modeling problem,
sidestepping several complex modeling issues. This method enables the
reconstructed 3D volume to retain valuable information from the 2D inputs,
which are passed between channel states in a Swin UNETR backbone. Our approach
applies neural optimal transport, which is fast and stable to train,
effectively integrating signal information across multiple views without the
requirement for precise alignment; it produces non-collapsed reconstructions
that are highly faithful to the 2D views, even after limited training. We
demonstrate correlated results, both qualitatively and quantitatively, having
trained our model on a single dataset and evaluated its generalization ability
across six datasets, including out-of-distribution samples.

该研究探讨了一种简单直接的二维到三维图像转换方法，通过重复和连接二维视图生成高通道的三维模型，解决了现有方法在潜在空间中整合信息时丢失信号信息的问题，并通过神经最优传输实现了在多个视角上无需精确对齐即可整合信号信息，训练结果在多个数据集上得出了定性和定量一致的重建效果。