We introduce a novel unsupervised approach to reconstructing a 3D volume from only two planar projections that exploits a previous\-ly-captured 3D volume of the patient. Such volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections typically fail when the number of projections is very low as the alignment becomes underconstrained. We show how to use a generative model of the volume structures to constrain the deformation and obtain a correct estimate. Moreover, our method is not bounded to a specific sensor calibration and can be applied to new calibrations without retraining. We evaluate our approach on a challenging dataset and show it outperforms state-of-the-art methods. As a result, our method could be used in treatment scenarios such as surgery and radiotherapy while drastically reducing patient radiation exposure.

翻译：我们提出了一种新颖的无监督方法，该方法仅利用两个平面投影重建三维体积，并利用患者先前捕获的三维体积数据。此类体积数据在许多重要医学流程中易于获取，且已有方法曾使用此类体积。早期通过形变该体积以匹配投影的方法，在投影数量极少时通常会失效，因为对齐过程变得欠约束。我们展示了如何利用体积结构的生成模型约束形变，从而获得准确的估计结果。此外，本方法不受特定传感器标定的限制，可在不重新训练的情况下适用于新的标定方案。我们在一个具有挑战性的数据集上评估了该方法，结果表明其优于现有技术。因此，该方法可应用于手术及放射治疗等治疗场景，同时显著减少患者的辐射暴露。