A 3D computer-assisted treatment planning system for breast cancer brachytherapy treatment

A Real-Time Tracking and Visualization System for Robot-Assisted Template Location Method Applied to Lung Cancer Brachytherapy

Accurate location of the puncture-guiding template, including the position and orientation, is essential for surgeons to implant radioactive seeds into an internal tumor in lung cancer brachytherapy. The objective of this paper is to establish a real-time tracking and visualization system (RTVS) to monitor the robot-assisted location process distantly and confirm the ultimate location accuracy without redundant computed tomography (CT) scans. RTVS consists of tracking and visualization components. A quaternion-based iterative closest point (QICP) algorithm for higher accuracy was proposed for the premised module of spatial registration. Arithmetic accuracy of QICP and clinical performance of RTVS were both validated by a series of experiments in a CT room. Spatial registration experiment shows that QICP consistently presents a distinctly higher degree of accuracy of 0.87±0.11 mm compared with other two conventional algorithms. RTVS, evaluated by tracking and visualization experiments, achieves a tracking accuracy of 1.05±0.05 mm position and (0.29±0.14) deg orientation. In addition, the time cost for template location is greatly reduced, so are the CT scan times. RTVS has the potential on lessening the workload of surgeons, reducing the CT radiation injury to the patient, and accelerating the progress of a brachytherapy surgery. The system presented means a new contribution to the lung cancer brachytherapy.

Automatic seed picking for brachytherapy postimplant validation with 3D CT images

PURPOSE

Postimplant validation is an indispensable part in the brachytherapy technique. It provides the necessary feedback to ensure the quality of operation. The ability to pick implanted seed relates directly to the accuracy of validation. To address it, an automatic approach is proposed for picking implanted brachytherapy seeds in 3D CT images.

METHODS

In order to pick seed configuration (location and orientation) efficiently, the approach starts with the segmentation of seed from CT images using a thresholding filter which based on gray-level histogram. Through the process of filtering and denoising, the touching seed and single seed are classified. The true novelty of this approach is found in the application of the canny edge detection and improved concave points matching algorithm to separate touching seeds. Through the computation of image moments, the seed configuration can be determined efficiently. Finally, two different experiments are designed to verify the performance of the proposed approach: (1) physical phantom with 60 model seeds, and (2) patient data with 16 cases.

RESULTS

Through assessment of validated results by a medical physicist, the proposed method exhibited promising results. Experiment on phantom demonstrates that the error of seed location and orientation is within ([Formula: see text]) mm and ([Formula: see text])[Formula: see text], respectively. In addition, the most seed location and orientation error is controlled within 0.8 mm and 3.5[Formula: see text] in all cases, respectively. The average process time of seed picking is 8.7 s per 100 seeds.

CONCLUSIONS

In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.