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Steve T Lam

Publications and source records attributed to Steve T Lam.

2 recordsLinked to original sources

Detection and correction of patient movement in prostate brachytherapy seed reconstruction.

Intraoperative dosimetry of prostate brachytherapy can help optimize the dose distribution and potentially improve clinical outcome. Evaluation of dose distribution during the seed implant procedure requires the knowledge of 3D seed coordinates. Fluoroscopy-based seed localization is a viable option. From three x-ray projections obtained at different gantry angles, 3D seed positions can be determined. However, when local anaesthesia is used for prostate brachytherapy, the patient movement during fluoroscopy image capture becomes a practical problem. If uncorrected, the errors introduced by patient motion between image captures would cause seed mismatches. Subsequently, the seed reconstruction algorithm would either fail to reconstruct or yield erroneous results. We have developed an algorithm that permits detection and correction of patient movement that may occur between fluoroscopy image captures. The patient movement is decomposed into translational shifts along the tabletop and rotation about an axis perpendicular to the tabletop. The property of spatial invariance of the co-planar imaging geometry is used for lateral movement correction. Cranio-caudal movement is corrected by analysing the perspective invariance along the x-ray axis. Rotation is estimated by an iterative method. The method can detect and correct for the range of patient movement commonly seen in the clinical environment. The algorithm has been implemented for routine clinical use as the preprocessing step for seed reconstruction.

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Three-dimensional seed reconstruction for prostate brachytherapy using Hough trajectories.

In order to perform intra-operative or post-implant dosimetry in prostate brachytherapy, the 3D coordinates of the implanted radioactive seeds must be determined. Film or fluoroscopy based seed reconstruction techniques use back projection of x-ray data obtained at two or three x-ray positions. These methods, however, do not perform well when some of the seed images are undetected. To overcome this problem we have developed an alternate technique for 3D seed localization using the principle of Hough transform. The Hough method utilizes the fact that, for each seed coordinate in three dimensions, there exists a unique trajectory in Hough feature space. In this paper we present the Hough transform parametric equations to describe the path of the seed projections from one view to the next and a method to reconstruct the 3D seed coordinates. The results of simulation and phantom studies indicate that the Hough trajectory method can accurately determine the 3D seed positions even from an incomplete dataset.

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