Three-dimensional Cherenkov emission surface mapping into the patient coordinate system for spatial delivery...

Cherenkov imaging provides the ability to visualize beam delivery during radiation therapy treatments in real-time informing the therapy team about possible deviations from the treatment plan. Current clinical practice with Cherenkov imaging relies on the real-time view and post-treatment qualitative assessment of Cherenkov images by therapists, physicists and clinicians who may then perform secondary treatment checks.

While Cherenkov images provide new insights into beam location, the limitation is that quantifying the observed deviation is difficult due to the lack of spatial registration of the Cherenkov emission in patient's coordinate space. This work addresses this problem with the first fusion of Cherenkov images with the three-dimensional patient surface to enable quantitative spatial beam verification in patient coordinate space. By combining multiple camera viewpoints with CT-sim derived surface coordinates, this system is capable of generating virtual beam's eye view images for objective measurement of beam delivery deviations on patient surfaces. This approach enables automated error detection thresholds and represents the first step toward real-time, quantitative optical treatment verification and dosimetry.

Cherenkov images from multiple geometrically calibrated cameras were projected onto Computed Tomography (CT) surface datasets to provide a Cherenkov surface image map on the patient. A raycasting algorithm was utilized to virtually map the Cherenkov surface image into the beam's eye view. To test the ability to quantitatively measure beam versus patient surface deviations from the virtual beam's eye view, a series of 10 × 10 cm² 6X fields with simulated spatial shifts were delivered to both flat and anthropomorphic phantoms. Virtual beam's eye view images were generated for each shift, and the magnitudes of Cherenkov map shifts were measured with Iterative Closest Point (ICP) and Hausdorff distance metrics with respect to a reference field.

Iterative Closest Point algorithms achieved an average x and y accuracy of 0.5 ± 0.2 mm and 0.3 ± 0.2 mm for the flat phantom and 0.5 mm ± 0.7 mm and 0.3 mm ± 0.2 mm for the anthropomorphic torso phantom. Camera projection error adds up to an additional 0.9 mm of error.

Multi-view surface mapping of Cherenkov images onto the patient surface mesh enabled quantitative assessment of beam versus topology shifts between fractions, using the Cherenkov emission outline as a beam extent surrogate. We demonstrated the utility and metrics of Cherenkov 3D mapping towards future applications utilizing daily or real-time surfaces obtained from, for example, cone-beam CT or surface-guided radiotherapy (SGRT) systems.