Abstract:A system for patient set-up in external beam radiotherapy was developed using Augmented Reality (AR). Live images of the linac treatment couch and patient were obtained with video cameras and displayed on a nearby monitor. A 3D model of the patient's external contour was obtained from planning CT data, and AR tracking software was used to superimpose the model onto the video images in the correct position for treatment. Throughout set-up and treatment, the user can view the monitor and visually confirm that th… Show more
“…Different approaches are developed for this task, such as the broadly applied reconstruction based techniques using cone beam CT, [2][3][4] optical cameras, [29][30][31] ultrasound methods, 32 or positioning techniques based on just a few radiographs. [5][6][7][8] Typically, these methods provide submillimeter accuracy for rigid bodies and good contrast but lead to problems in interpretation for determining the rigid transformation parameters when interfractional deformations occurred inside the patient.…”
Purpose:In this work, a novel stochastic framework for patient positioning based on linac-mounted CB projections is introduced. Based on this formulation, the most probable shifts and rotations of the patient are estimated, incorporating interfractional deformations of patient anatomy and other uncertainties associated with patient setup. Methods: The target position is assumed to be defined by and is stochastically determined from positions of various features such as anatomical landmarks or markers in CB projections, i.e., radiographs acquired with a CB-CT system. The patient positioning problem of finding the target location from CB projections is posed as an inverse problem with prior knowledge and is solved using a Bayesian maximum a posteriori ͑MAP͒ approach. The prior knowledge is three-fold and includes the accuracy of an initial patient setup ͑such as in-room laser and skin marks͒, the plasticity of the body ͑relative shifts between target and features͒, and the feature detection error in CB projections ͑which may vary depending on specific detection algorithm and feature type͒. For this purpose, MAP estimators are derived and a procedure of using them in clinical practice is outlined. Furthermore, a rule of thumb is theoretically derived, relating basic parameters of the prior knowledge ͑initial setup accuracy, plasticity of the body, and number of features͒ and the parameters of CB data acquisition ͑number of projections and accuracy of feature detection͒ to the expected estimation accuracy. Results: MAP estimation can be applied to arbitrary features and detection algorithms. However, to experimentally demonstrate its applicability and to perform the validation of the algorithm, a water-equivalent, deformable phantom with features represented by six 1 mm chrome balls were utilized. These features were detected in the cone beam projections ͑XVI, Elekta Synergy ® ͒ by a local threshold method for demonstration purposes only. The accuracy of estimation ͑strongly varying for different plasticity parameters of the body͒ agreed with the rule of thumb formula. Moreover, based on this rule of thumb formula, about 20 projections for 6 detectable features seem to be sufficient for a target estimation accuracy of 0.2 cm, even for relatively large feature detection errors with standard deviation of 0.5 cm and spatial displacements of the features with standard deviation of 0.5 cm.
Conclusions:The authors have introduced a general MAP-based patient setup algorithm accounting for different sources of uncertainties, which are utilized as the prior knowledge in a transparent way. This new framework can be further utilized for different clinical sites, as well as theoretical developments in the field of patient positioning for radiotherapy.
“…Different approaches are developed for this task, such as the broadly applied reconstruction based techniques using cone beam CT, [2][3][4] optical cameras, [29][30][31] ultrasound methods, 32 or positioning techniques based on just a few radiographs. [5][6][7][8] Typically, these methods provide submillimeter accuracy for rigid bodies and good contrast but lead to problems in interpretation for determining the rigid transformation parameters when interfractional deformations occurred inside the patient.…”
Purpose:In this work, a novel stochastic framework for patient positioning based on linac-mounted CB projections is introduced. Based on this formulation, the most probable shifts and rotations of the patient are estimated, incorporating interfractional deformations of patient anatomy and other uncertainties associated with patient setup. Methods: The target position is assumed to be defined by and is stochastically determined from positions of various features such as anatomical landmarks or markers in CB projections, i.e., radiographs acquired with a CB-CT system. The patient positioning problem of finding the target location from CB projections is posed as an inverse problem with prior knowledge and is solved using a Bayesian maximum a posteriori ͑MAP͒ approach. The prior knowledge is three-fold and includes the accuracy of an initial patient setup ͑such as in-room laser and skin marks͒, the plasticity of the body ͑relative shifts between target and features͒, and the feature detection error in CB projections ͑which may vary depending on specific detection algorithm and feature type͒. For this purpose, MAP estimators are derived and a procedure of using them in clinical practice is outlined. Furthermore, a rule of thumb is theoretically derived, relating basic parameters of the prior knowledge ͑initial setup accuracy, plasticity of the body, and number of features͒ and the parameters of CB data acquisition ͑number of projections and accuracy of feature detection͒ to the expected estimation accuracy. Results: MAP estimation can be applied to arbitrary features and detection algorithms. However, to experimentally demonstrate its applicability and to perform the validation of the algorithm, a water-equivalent, deformable phantom with features represented by six 1 mm chrome balls were utilized. These features were detected in the cone beam projections ͑XVI, Elekta Synergy ® ͒ by a local threshold method for demonstration purposes only. The accuracy of estimation ͑strongly varying for different plasticity parameters of the body͒ agreed with the rule of thumb formula. Moreover, based on this rule of thumb formula, about 20 projections for 6 detectable features seem to be sufficient for a target estimation accuracy of 0.2 cm, even for relatively large feature detection errors with standard deviation of 0.5 cm and spatial displacements of the features with standard deviation of 0.5 cm.
Conclusions:The authors have introduced a general MAP-based patient setup algorithm accounting for different sources of uncertainties, which are utilized as the prior knowledge in a transparent way. This new framework can be further utilized for different clinical sites, as well as theoretical developments in the field of patient positioning for radiotherapy.
“…A method for AR-facilitated patient set-up was proposed by Talbot et al 16 in a pilot study using an anthropomorphic phantom. The 3D external body contour was obtained from planning CT data.…”
Section: Treatment Deliverymentioning
confidence: 99%
“…The authors state that the developed technique needs further investigation before clinical useFigure 5Body contour from CT scan (grey virtual image) and patient's true image (red anthropomorphous phantom). Reproduced with kind permission of Springer from Talbot et al 16 …”
Section: Treatment Deliverymentioning
confidence: 99%
“…Body contour from CT scan (grey virtual image) and patient's true image (red anthropomorphous phantom). Reproduced with kind permission of Springer from Talbot et al 16 …”
Purpose: We review augmented (AR) and virtual reality (VR) applications in radiotherapy as found in the scientific literature and highlight future developments enabled by the use of small mass-produced devices and portability of techniques developed in other fields to radiotherapy.Analysis: The application of AR and VR within radiotherapy is still in its infancy, with the notable exception of training and teaching applications. The relatively high cost of equipment needed to generate a realistic 3D effect seems one factor that has slowed down its use, but also the sheer amount of image data is relatively recent, were radiotherapy professionals are only beginning to explore how to use this to its full potential. This increased availability of 3D data in radiotherapy will drive the application of AR and VR in radiotherapy to efficiently recognise and extract key features in the data to act on in clinical decision making.
Conclusion:The development of small mass-produced tablet devices coming on the market will allow the user to interact with computer-generated information more easily, facilitating the application of AR and VR. The increased connectivity enabling virtual presence of remote multidisciplinary team meetings heralds significant changes to how radiotherapy professionals will work, to the benefit of our patients.
“…Although AR has been increasingly applied to the medical field, it is hardly used in radiotherapy around the whole world. In 2009, Talbot et al [4] proposed the application of AR to external radiotherapy for the first time. However, this system is only used as a feasibility study.…”
Objective: This study is conducted to explore the development of a noninvasive and nonradiative guided radiotherapy positioning system based on augmented reality (AR) technology to increase positioning accuracy and completely eradicate positioning errors.Method: The development system includes three monitors and supporting equipment. The 3D virtual model is obtained by 3D reconstruction of the radiotherapy plan data set and is superimposed on the correct treatment position of the real treatment environment by using augmented reality production software. This approach provides the standard for the patient's positioning and guides the patient. The position of the patient then coincides with the 3D virtual model through the visual image method, and positioning error of the radiotherapy position is reduced.Results: A total of nine cases are included in this study, namely, three patients who require head and neck radiotherapy and six patients who require chest and abdomen radiotherapy. According to experimental results, no statistical difference exists between AR-guided positioning and manual positioning for patients requiring head and neck radiotherapy. However, AR-guided positioning can keep errors of x-axis, y-axis and z-axis within small ranges, such as -0.092–0.072, -0.052–0.228, and -0.192–0.05 cm, respectively. For patients requiring chest and abdomen radiotherapy, AR-guided positioning is not statistically different from manual positioning on the x-axis, but statistical differences were found on the y-axis and z-axis. The errors of AR-guided positioning on y-axis and z-axis are much smaller than those of manual positioning.Conclusions: The AR visual image guided radiotherapy set-up system can increase radiotherapy positioning accuracy well, especially for patients requiring chest and abdomen radiotherapy.
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