Fast and flexible distance measures for treatment planning

Abstract

Distance measures are required for diagnoses, therapy decision and documentation. With today's high-resolution CT and MR imaging techniques, high- quality images have become possible. Yet, manual measurement can be tedious. We present a method for automatically determining different distance-based measures on segmented anatomic structures, like shortest distances, diameters, and wall thicknesses. Our method is inspired from computational geometry and based on a surface mesh representation. The computation takes all primitives (points, edges, faces) into account and organizes them efficiently in a spatial tree structure. We followed the generic design paradigm in order to achieve maximum flexibility. The generic approach allows for a variety of intervention-relevant distance measures to be computed, using only a single type of data structure. For shortest distance, our approach in empirical tests turned out to be more efficient than previous methods from medical application literature. Besides the numerical value, also its defining geometric primitives are determined. The presented technique is both, fast and flexible. It can be used to interactively derive automatic distance measures for arbitrary mesh-based segmentations. Due to the geometrically exact measurements, it is possible to reliably estimate safety margins, assess possible infiltrations and other clinically relevant measures. To exploit this benefit, the method requires precise segmentations as input data.