Proof of concept of near real-time intra-fractional 3D monitoring of prostate position using a scheme based on the skeletonization of implanted marker images and recursive least squares approximation motion tracking

Abstract

Background

The on-board imaging system using monoscopic X-ray technology struggles to detect motion along the beam direction. This study presents a method that combines skeletonization for marker recognition with a Recursive Least Squares Approximation (RLSA) algorithm to convert 2D motion data into a 3D representation.

Methods

Fiducial markers were represented as 2D lines through masking and skeletonization of paired-planar images, allowing for the construction of a 3D motion model. An iterative closest point (ICP) algorithm determined the 6D transformation from online to planning images. The accuracy of 3D motion estimation was evaluated across various angular separations (10°–170°) for both large (10 mm, 3°) and small (3 mm) marker offsets. The RLSA algorithm was validated against different motion drift patterns.

Results

The marker recognition process was robust against varying contrast noise ratios. Mean errors of various angle separation tests in the X and Y directions were within 0.3 mm for large offsets and 0.2 mm for small offsets across all angular separations. With The RLSA applied at 10-° intervals during a 360-° gantry rotation, mean errors in the beam direction for continuous drift and low, intermediate, and high-frequency excursions were (0.2 ± 0.3) mm, (0.2 ± 0.3) mm, (0.4 ± 0.5) mm, and (0.4 ± 0.5) mm, respectively, with a maximum error of 1.3 mm across all excursion conditions.

Conclusion

The proposed method demonstrates significant potential in effectively converting 2D motion into 3D and enabling real-time 3D motion tracking during intrafraction radiation therapy.