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|Title:||2D/3D Registration Algorithm for Lung Brachytherapy|
|Keywords:||2D/3D registration;lung brachytherapy;computed tomography;Physics;Physics|
|Abstract:||<p>The typical High Dose Rate (HDR) lung brachytherapy procedure involves inserting treatment catheters into the bronchi next to the tumour location using a bronchoscope. The anterior-posterior and lateral fluoroscopy images are acquired in order to localize the catheters prior to treatment. Although, these images enable accurate reconstruction of the catheter location, they do not allow for the visualization of the tumour or organs-at-risk due to poor soft tissue contrast. Although CT images offer an improved soft tissue contrast, moving the patient with catheters in place prior to each treatment is impractical.</p> <p>An alternative option is to use prior diagnostic or external beam radiation treatment planning CT images. These images cannot be used for treatment planning directly because of the variation in patient positioning between the CT and orthogonal images acquisition. In order to account for positioning differences, a 2D/3D registration algorithm that registers the orthogonal images with a previously acquired CT data was developed. The algorithm utilizes a rigid registration model based on a pixel/voxel intensity matching approach. A similarity measure combining normalized mutual information (NMI), image gradient, and intensity difference was developed. Evaluation of the algorithm was performed using tissue equivalent phantoms, and, in the clinical setting using data from six patients. The mean registration error was 2.1 mm and 3.2 mm for phantoms and patients respectively.</p> <p>External objects such as the treatment table and ECG leads are often in CT images, affecting the above mentioned 2D/3D registration. To address this, an algorithm for automatic removal of external objects from CT images was developed. This was applied to automatic contouring and removal of the fiducial markers in CT images used for external beam radiation therapy treatment planning for breast cancer. The algorithm was further modified to compute the girth of patients as part of a diagnostic radiology clinical trial.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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