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An Extension of Iterative Closest Point Algorithm for 3D-2D Registration for Pre-treatment Validation in Radiotherapy | IEEE Conference Publication | IEEE Xplore

An Extension of Iterative Closest Point Algorithm for 3D-2D Registration for Pre-treatment Validation in Radiotherapy


Abstract:

The paper presents a novel feature-based 3D-2D registration method to align a pair of orthogonal X-ray images to the corresponding CT volumetric data with full 6 degrees ...Show More

Abstract:

The paper presents a novel feature-based 3D-2D registration method to align a pair of orthogonal X-ray images to the corresponding CT volumetric data with full 6 degrees of freedom by combining the Iterative Closest Point (ICP) and Z-buffer algorithms. The proposed method has been evaluated using simulated data as well as skull phantom data. For the latter, the alignment errors were found to vary from 0.04 mm to 3.3 mm with an average of 1.27 mm for translation, and from 0.02 to 1.64 with an average of 0.82 for rotation. With the accuracy comparing favourably against other feature-based registration methods and the computational load being much less than intensity-based registration methods, the proposed method provides a good basis for validation of patient and machine set-up in the pretreatment procedure in radiotherapy.
Date of Conference: 05-07 July 2006
Date Added to IEEE Xplore: 11 September 2006
Print ISBN:0-7695-2603-9
Conference Location: London, England, UK

1. Introduction

Radiotherapy is a major cancer treatment approach. In radiotherapy, undesirable side effects may be caused by healthy cells being exposed to radiation. In order to reduce the damage to healthy tissues as well as eradicate the tumour accurately, the accuracy of machine set-up and patient placement are crucial. Before treatment delivery, the patient's pose (position and orientation) within the treatment device must be accurately determined according to the 3D CT planning data. A treatment simulation phase is therefore carried out to validate machine and patient set-up to improve the treatment accuracy, and involves matching a pair of orthogonal kilo-volts X-ray images (anterior-posterior and lateral views) taken from the patient with the corresponding digital reconstructed radiographs (DRRs) derived from the 3D CT planning data to determine the final machine and patient set-up for treatment delivery. At present, clinicians are required to identify and match manually some rigid features from the kilo-volts X-ray image pair (known as the simulator images) and the corresponding DRR image pair in order to obtain the data for position modification in three dimensions. The main problems with this approach are (1) low resolution of the DRR images giving rise to errors in visual comparison; and (2) only translation errors are considered without taking out-of-plane rotations into account. In addition, the matching accuracy depends highly on the clinician's experience which may directly affect the quality of the final treatment.

References

References is not available for this document.