Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Visualizing the morphology and appearance of non-planar sparse anatomical structures that extend over multiple 2D slices in tomographic volumes is inherently difficult but valuable for decision-making and reporting. Hence, various organ-specific unfolding techniques exist to map their densely sampled 3D surfaces to a distortion-minimized 2D representation. However, there is no versatile framework to flatten complex sparse structures including vascular, duct or bone systems. We deploy a neural field to fit the transformation of the anatomy of interest to a 2D overview image. We further propose distortion regularization strategies and combine geometric with intensity-based loss formulations to also display non-annotated and auxiliary targets. In addition to improved versatility, our unfolding technique outperforms mesh-based baselines for sparse structures w.r.t. peak distortion and our regularization scheme yields smoother transformations compared to Jacobian formulations from neural field-based image registration.

本研究解决了在医学成像中可视化非平面稀疏解剖结构的难题。论文提出了一种新颖的神经场框架，能够将复杂稀疏结构展平为2D图像，并通过扭曲正则化策略与几何和强度损失结合，显著提高了展平的灵活性和准确性。该方法在稀疏结构的峰值扭曲方面超越了基于网格的基准测试，具有重要的应用潜力。

神经图像展开：利用神经场展平稀疏解剖结构