Joint scene and object tracking for cost-Effective augmented reality guided patient positioning in radiation therapy.

BACKGROUND AND OBJECTIVE: The research is done in the field of Augmented Reality (AR) for patient positioning in radiation therapy is scarce. We propose an efficient and cost-effective algorithm for tracking the scene and the patient to interactively assist the patient's positioning process by providing visual feedback to the operator. Up to our knowledge, this is the first framework that can be employed for mobile interactive AR to guide patient positioning. METHODS: We propose a pointcloud processing method that, combined with a fiducial marker-mapper algorithm and the generalized ICP algorithm, tracks the patient and the camera precisely and efficiently only using the CPU unit. The 3D reference model and body marker map alignment is calculated employing an efficient body reconstruction algorithm. RESULTS: Our quantitative evaluation shows that the proposed method achieves a translational and rotational error of 4.17 mm/0.82∘ at 9 fps. Furthermore, the qualitative results demonstrate the usefulness of our algorithm in patient positioning on different human subjects. CONCLUSION: Since our algorithm achieves a relatively high frame rate and accuracy employing a regular laptop (without a dedicated GPU), it is a very cost-effective AR-based patient positioning method. It also opens the way for other researchers by introducing a framework that could be improved upon for better mobile interactive AR patient positioning solutions in the future.

3D Reconstruction and alignment by consumer RGB-D sensors and fiducial planar markers for patient positioning in radiation therapy.

BACKGROUND AND OBJECTIVE: Patient positioning is a crucial step in radiation therapy, for which non-invasive methods have been developed based on surface reconstruction using optical 3D imaging. However, most solutions need expensive specialized hardware and a careful calibration procedure that must be repeated over time.This paper proposes a fast and cheap patient positioning method based on inexpensive consumer level RGB-D sensors. METHODS: The proposed method relies on a 3D reconstruction approach that fuses, in real-time, artificial and natural visual landmarks recorded from a hand-held RGB-D sensor. The video sequence is transformed into a set of keyframes with known poses, that are later refined to obtain a realistic 3D reconstruction of the patient. The use of artificial landmarks allows our method to automatically align the reconstruction to a reference one, without the need of calibrating the system with respect to the linear accelerator coordinate system. RESULTS: The experiments conducted show that our method obtains a median of 1 cm in translational error, and 1∘of rotational error with respect to reference pose. Additionally, the proposed method shows as visual output overlayed poses (from the reference and the current scene) and an error map that can be used to correct the patient's current pose to match the reference pose. CONCLUSIONS: A novel approach to obtain 3D body reconstructions for patient positioning without requiring expensive hardware or dedicated graphic cards is proposed. The method can be used to align in real time the patient's current pose to a preview pose, which is a relevant step in radiation therapy.