Development and clinical evaluation of automatic fiducial detection for tumor tracking in cine megavoltage images during volumetric modulated arc therapy

Abstract

Purpose: Real-time tracking of implanted fiducials in cine megavoltage (MV) imaging during volumetric modulated arc therapy (VMAT) delivery is complicated due to the inherent low contrast of MV images and potential blockage of dynamic leaves configurations. The purpose of this work is to develop a clinically practical autodetection algorithm for motion management during VMAT. Methods: The expected field-specific segments and the planned fiducial position from the Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system were projected onto the MV images. The fiducials were enhanced by applying a Laplacian of Gaussian filter in the spatial domain for each image, with a blob-shaped object as the impulse response. The search of implanted fiducials was then performed on a region of interest centered on the projection of the fiducial when it was within an open field including the case when it was close to the field edge or partially occluded by the leaves. A universal template formula was proposed for template matching and normalized cross correlation was employed for its simplicity and computational efficiency. The search region for every image was adaptively updated through a prediction model that employed the 3D position of the fiducial estimated from the localized positions in previous images. This prediction model allowed the actual fiducial position to be tracked dynamically and was used to initialize the search region. The artifacts caused by electronic interference during the acquisition were effectively removed. A score map was computed by combining both morphological information and image intensity. The pixel location with the highest score was selected as the detected fiducial position. The sets of cine MV images taken during treatment were analyzed with in-house developed software written in MATLAB (The Mathworks, Inc., Natick, MA). Five prostate patients were analyzed to assess the algorithm performance by measuring their positioning accuracy during treatment. Results: The algorithm was able to accurately localize the fiducial position on MV images with success rates of more than 90% per case. The percentage of images in which each fiducial was localized in the studied cases varied between 23% and 65%, with at least one fiducial having been localized between 40% and 95% of the images. This depended mainly on the modulation of the plan and fiducial blockage. The prostate movement in the presented cases varied between 0.8 and 3.5 mm (mean values). The maximum displacement detected among all patients was of 5.7 mm. Conclusions: An algorithm for automatic detection of fiducial markers in cine MV images has been developed and tested with five clinical cases. Despite the challenges posed by complex beam aperture shapes, fiducial localization close to the field edge, partial occlusion of fiducials, fast leaf and gantry movement, and inherently low MV image quality, good localization results were achieved in patient images. This work provides a technique for enabling real-time accurate fiducial d...

Automatic Prostate Tracking and Motion Assessment in Volumetric Modulated Arc Therapy With an Electronic Portal Imaging Device

“…An automatic fiducial detection algorithm was developed that detected the fiducial positions in cine MV images acquired with the therapeutic beam (18). It employed a prediction model for the expected fiducial position on an image, based on its previous detections in the same arc during that fraction of treatment.…”

“…The search area was dynamically adapted between subsequent images to track the fiducial motion. The algorithm could successfully detect fiducial markers in >90% of the MV images acquired during patient VMAT treatments (18). The average error in the detection was typically less than 1 mm (18).…”

“…We present measured data of the prostate motion during treatment and analyzed retrospectively using a novel automatic fiducial detection algorithm (18). The technique proposed has the advantage of being readily implementable with the equipment currently available in modern linacs.…”

Simultaneous MV‐kV imaging for intrafractional motion management during volumetric‐modulated arc therapy delivery*

“…A variety of methods for detecting and quantifying intratreatment motion have been developed, including those that rely on the implantation of radiofrequency beacons (1–4) and those that are based on the imaging of bony anatomy or implanted radiopaque fiducials. (5–14) For the most part, image-based intratreatment motion detection has been studied using MV or on-board (i.e., gantry-mounted) kilovoltage (kV) single-source systems (6,9,11) or external (i.e., room-mounted) dual-kV source systems. (5,13,14) Although kV systems provide high-quality images, current capabilities related to image acquisition frequency and directionality of the images can limit three-dimensional motion detection, either because the imaging system consists of a single kV source orthogonal to the MV beam direction (as is the case for accelerator on-board systems) or because the patient field of view may periodically be occluded by the accelerator itself (for external dual-source systems).…”

Recommendation of fiducial marker implantation for better target tracking using MV imager in prostate radiotherapy

“…Initially, the fiducial markers implanted inside the prostate served as a surrogate for precise alignment of prostate using portal imaging. In most of the recently developed techniques, fiducial markers were used as a prostate margin tracker either by MV, kV or kV‐MV combined images 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. It is important to note that the use of kV imaging techniques is not available in all clinics, and the additional dose that would be applied for every treatment fraction is of concern as well.…”