Assessment of Iterative Closest Point Registration Accuracy for Different Phantom Surfaces Captured by an Optical 3D Sensor in Radiotherapy.

Gerald Krell,Ayoub Al-Hamadi,Mathias Walke,Nazila Saeid Nezhad,Günther Gademann

doi:10.1155/2017/2938504

Abstract

An optical 3D sensor provides an additional tool for verification of correct patient settlement on a Tomotherapy treatment machine. The patient's position in the actual treatment is compared with the intended position defined in treatment planning. A commercially available optical 3D sensor measures parts of the body surface and estimates the deviation from the desired position without markers. The registration precision of the in-built algorithm and of selected ICP (iterative closest point) algorithms is investigated on surface data of specially designed phantoms captured by the optical 3D sensor for predefined shifts of the treatment table. A rigid body transform is compared with the actual displacement to check registration reliability for predefined limits. The curvature type of investigated phantom bodies has a strong influence on registration result which is more critical for surfaces of low curvature. We investigated the registration accuracy of the optical 3D sensor for the chosen phantoms and compared the results with selected unconstrained ICP algorithms. Safe registration within the clinical limits is only possible for uniquely shaped surface regions, but error metrics based on surface normals improve translational registration. Large registration errors clearly hint at setup deviations, whereas small values do not guarantee correct positioning.

Highlights

Tomotherapy combines a CT scanner with a computer-controlled radiation beam collimation system at the treatment machine [1] to precisely target tumors sparing healthy tissue
Different unconstrained ICP algorithms have been compared for real data produced by an optical sensor as part of a Tomotherapy HD system
We could show that such accuracies are only possible for well curved surfaces whereas gross errors may occur for registration of other not uniquely shaped surfaces and are not much effected by the chosen ICP registration algorithm

Summary

Introduction

Tomotherapy combines a CT scanner with a computer-controlled radiation beam collimation system at the treatment machine [1] to precisely target tumors sparing healthy tissue. A helical slit delivers radiation with most conformal image guided radiotherapy (imrt). The x-ray source rotates in a helical path around the patient in order to acquire a 3D image. The same x-ray source is used as treatment beam This source is rotating in a helical pattern around the patient, while the intensity of beam is modulated according to the tumor shape using “tungsten leaves.”. These leaves create thousands of beam elements, called “beamlets” [2] This source is rotating in a helical pattern around the patient, while the intensity of beam is modulated according to the tumor shape using “tungsten leaves.” These leaves create thousands of beam elements, called “beamlets” [2]

Methods

Results

Conclusion