Spatial and temporal performance of 3D optical surface imaging for real‐time head position tracking

Wiersma, R; Tomarken, Spencer; Grelewicz, Z; Belcher, AH; Kang, Hyejoo

doi:10.1118/1.4823757

Cited by 38 publications

(49 citation statements)

References 23 publications

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“…In clinical practice, this coordinate frame, called the virtual point, would be positioned at the treatment isocenter; but for use with volunteers, it was placed near the center of the head to help simulate a typical treatment condition. The camera system which monitors the movements of these four markers is a stereoscopic infrared (IR) marker tracking system (Polaris, Northern Digital, Inc., Waterloo, ON, Canada) (Wiersma et al, 2013). This device functions by having two IR cameras simultaneously track the set of four spherical markers which are IR-reflective and attached to a plate which is fixed to the forehead of the volunteer (Figure 2), and which act as external surrogates for motion of the internal target.…”

Section: Methodsmentioning

confidence: 99%

Towards frameless maskless SRS through real-time 6DoF robotic motion compensation

Belcher¹,

Liu²,

Chmura³

et al. 2017

Phys. Med. Biol.

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Purpose Stereotactic radiosurgery (SRS) uses precise dose placement to treat conditions of the CNS. Frame-based SRS uses a metal head ring fixed to the patient’s skull to provide high treatment accuracy, but patient comfort and clinical workflow may suffer. Frameless SRS, while potentially more convenient, may increase uncertainty of treatment accuracy and be physiologically confining to some patients. By incorporating highly precise robotics and advanced software algorithms into frameless treatments, we present a novel frameless and maskless SRS system where a robot provides real-time 6DOF head motion stabilization allowing positional accuracies to match or exceed those of traditional frame-based SRS. Methods A 6DOF parallel kinematics robot was developed and integrated with a real-time infrared camera in a closed loop configuration. A novel compensation algorithm was developed based on an iterative closest-path correction approach. The robotic SRS system was tested on six volunteers, whose motion was monitored and compensated for in real-time over 15-minute simulated treatments. The system’s effectiveness in maintaining the target’s 6DOF position within preset thresholds was determined by comparing volunteer head motion with and without compensation. Results Comparing corrected and uncorrected motion, the 6DOF robotic system showed an overall improvement factor of 21 in terms of maintaining target position within 0.5 mm and 0.5 degree thresholds. Although the system’s effectiveness varied among the volunteers examined, for all volunteers tested the target position remained within the preset tolerances 99.0% of the time when robotic stabilization was used, compared to 4.7% without robotic stabilization. Conclusion The pre-clinical robotic SRS compensation system was found to be effective at responding to sub-millimeter and sub-degree cranial motions for all volunteers examined. The system’s success with volunteers has demonstrated its capability for implementation with frameless and maskless SRS treatments, potentially able to achieve the same or better treatment accuracies compared to traditional frame-based approaches.

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Section: Methodsmentioning

confidence: 99%

Towards frameless maskless SRS through real-time 6DoF robotic motion compensation

Belcher¹,

Liu²,

Chmura³

et al. 2017

Phys. Med. Biol.

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“…To take into account camera uncertainty, a precise calibration between the camera frame to Linac reference frame is required. 17,18 …”

Section: C Linac Beam Gatingmentioning

confidence: 99%

Robotic real-time translational and rotational head motion correction during frameless stereotactic radiosurgery

et al. 2015

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Purpose: To develop a control system to correct both translational and rotational head motion deviations in real-time during frameless stereotactic radiosurgery (SRS). Methods: A novel feedback control with a feed-forward algorithm was utilized to correct for the coupling of translation and rotation present in serial kinematic robotic systems. Input parameters for the algorithm include the real-time 6DOF target position, the frame pitch pivot point to target distance constant, and the translational and angular Linac beam off (gating) tolerance constants for patient safety. Testing of the algorithm was done using a 4D (XYZ + pitch) robotic stage, an infrared head position sensing unit and a control computer. The measured head position signal was processed and a resulting command was sent to the interface of a four-axis motor controller, through which four stepper motors were driven to perform motion compensation. Results: The control of the translation of a brain target was decoupled with the control of the rotation.

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“…1, 5,7,27 The third motion was that of an actual Volunteer. This motion was obtained by using a marker-based visual tracking system 34 tracking the head movement of a human volunteer who was coached to move while lying in the gantry of a SPECT/CT system where the motion tracking system was installed.…”

Section: B Motion Simulationmentioning

confidence: 99%

“…External tracking utilizing an electro-mechanical system was used in Green et al; 13 however, by far the most commonly used method to track motion is with infrared stereo cameras by affixing passive reflective markers 1,3,4,6,7,10,11,[14][15][16][17][18][19][20][21] or active markers that emit light 22 to the head of the patient. Recently, researchers have begun investigating the use of structured light cameras, such as the Microsoft Kinect 23 or other devices 5,9,24 which can be used to track the surface of the head without the need for markers. Data-driven methods which estimate motion from temporal frames of reconstructed PET data using registration to a reference frame 2,22,[25][26][27][28] have also been reported.…”

Section: Introductionmentioning

confidence: 99%

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Improved frame-based estimation of head motion in PET brain imaging

et al. 2016

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Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type. C 2016 American Association of Physicists in Medicine. [http://dx

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Spatial and temporal performance of 3D optical surface imaging for real‐time head position tracking

Cited by 38 publications

References 23 publications

Towards frameless maskless SRS through real-time 6DoF robotic motion compensation

Towards frameless maskless SRS through real-time 6DoF robotic motion compensation

Robotic real-time translational and rotational head motion correction during frameless stereotactic radiosurgery

Improved frame-based estimation of head motion in PET brain imaging

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