An image‐based method to synchronize cone‐beam CT and optical surface tracking

Abstract

The integration of in‐room X‐ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X‐ray projections and surface data. We present an image‐based method for the synchronization of cone‐beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X‐ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between ‐3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient‐specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.PACS number: 87

Recent advances in Surface Guided Radiation Therapy

“…Even more complex, is the application of this technology for particle therapy, specifically in pencil beam delivery, where parameters as latency, the impact of room geometries into the system performance and potential prediction algorithms, when using beam-gating or -tracking [97,98] need to be taken into account. Also in ring-like Linacs, a QA program needs to be implemented [37,99,100], as well as when the surface is used as a surrogate for 4D-acquisitions [101][102][103][104]. Although the majority of the users use commercially available phantoms, some features are currently impossible (or challenging) to test, such as deformable registration algorithms [7,36], latency time [105], skin colors and room lightening, full-workflow integration [106], and thermal signature.…”

“…Even more complex, is the application of this technology for particle therapy, specifically in pencil beam delivery, where parameters as latency, the impact of room geometries into the system performance and potential prediction algorithms, when using beam-gating or -tracking [ 97 , 98 ] need to be taken into account. Also in ring-like Linacs, a QA program needs to be implemented [ 37 , 99 , 100 ], as well as when the surface is used as a surrogate for 4D-acquisitions [ 101 – 104 ].…”

Assessment of Iterative Closest Point Registration Accuracy for Different Phantom Surfaces Captured by an Optical 3D Sensor in Radiotherapy

“…The possible synchronization of a classical CBCT system with the AlignRT has been shown by [10]. An image guided method for the synchronization of the X-ray projections is synchronized with optically sensed surface during using CBCT without any further hardware requirements.…”

“…The investigated ICP algorithms do not treat shape variations which is a motivation for using solid phantoms instead of real cases. The influence of motion of real human bodies, caused by respiration for instance, is considered by other papers (e.g., [10, 11]).…”

Head motion during cone-beam computed tomography: Analysis of frequency and influence on image quality

“…Fassi et al 30 described a way to reduce motion-induced artifacts by the synchronization of CBCT acquisition and optical surface tracking of respiratory movements. Moreover, a marker-based numerical optimization method has been described to detect and prevent head motion.…”