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.

Determining hysteresis thresholds for edge detection by combining the advantages and disadvantages of thresholding methods.

Hysteresis is an important technique for edge detection, but the unsupervised determination of its parameters is not an easy problem. In this paper, we propose a method for unsupervised determination of hysteresis thresholds using the advantages and disadvantages of two thresholding methods. The basic idea of our method is to look for the best hysteresis thresholds in a set of candidates. First, the method finds a subset and a overset of the unknown edge points set. Then, it determines the best edge map with the measure chi(2). Compared with a general method to determine the parameters of an edge detector, our method performs well and is less computationally complex. The basic idea of our method can be generalized to other pattern recognition problems.

2D versus 3D in the kinematic analysis of the horse at the trot.

The handled trot of three Lusitano Purebred stallions was analyzed by using 2D and 3D kinematical analysis methods. Using the same capture and analysis system, 2D and 3D data of some linear (stride length, maximal height of the hoof trajectories) and angular (angular range of motion, inclination of bone segments) variables were obtained. A paired Student T-test was performed in order to detect statistically significant differences between data resulting from the two methodologies With respect to the angular variables, there were significant differences in scapula inclination, shoulder angle, cannon inclination and protraction-retraction angle in the forelimb variables, but none of them were statistically different in the hind limb. Differences between the two methods were found in most of the linear variables analyzed.

Design and evaluation of a new three-dimensional motion capture system based on video.

This technical note describes the development and evaluation of a low-cost motion capture and analysis system (SOMCAM3D). From diverse video sequences obtained by commercial cameras, synchronized by a certain event, and calibrated from a calibration process, the system is able to reconstruct three-dimensional positions in a reference system defined by the calibration process. From these positions, a wide variety of kinematic variables could be calculated. This system aims to analyze movement in applications of gait analysis, sports, animals, ergonomics, robotics, etc. It is designed to work with low-cost equipment (commercial cameras, computers, etc.). Also it allows us to work in 2D, outdoors, and without marks. Finally, this technical note includes a study of how precise and accurate this system is.