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Background: Administration of external radiation therapy via proton therapy systems carries a risk of occasional collisions between the patient's body and gantry, which is increased by the snout placed near the patient for better dose distribution. Although treatment planning software (TPS) can simulate controlled collisions, the computed tomography (CT) data used for treatment planning are insufficient given that collisions can occur outside the CT imaging region. Thus, imaging the three‐dimensional (3D) surface outside the CT range and combining the data with those obtained by CT are essential for avoiding collisions. Purpose: To construct a prototype for 3D surface imaging and an end‐to‐end framework for preventing collisions between the patient's body and the gantry. Methods: We obtained 3D surface data using a light sectioning method (LSM). By installing only cameras in front of the CT, we achieved LSM using the CT couch motion and preinstalled patient‐positioning lasers. The camera image contained both sagittal and coronal lines, which are unnecessary for LSM and were removed by deep learning. We combined LSM 3D surface data and original CT data to create synthetic Digital Imaging and Communications in Medicine (DICOM) data. Subsequently, we compared the TPS snout auto‐optimization using the original CT data with the synthetic DICOM data. Results: The mean positional error for LSM of the arms and head was 0.7 ± 0.8 and 0.8 ± 0.8 mm for axial and sagittal imaging, respectively. The TPS snout auto‐optimization indicated that the original CT data would cause collisions; however, the synthetic DICOM data prevented these collisions. Conclusions: The prototype system's acquisition accuracy for 3D surface data was approximately 1 mm, which was sufficient for the collision simulation. The use of a TPS with collision avoidance can help optimize the snout position using synthetic DICOM data. Our proposed method requires no external software for collision simulation and can be integrated into the clinical workflow to improve treatment planning efficiency. [ABSTRACT FROM AUTHOR]

Purpose: In the last few years, there has been a growing interest in surface imaging for patient positioning in external radiation therapy. The aim of this study is to evaluate the accuracy of daily patient positioning using the Azure Kinect surface imaging. Methods: A total of 50 fractions in 10 patients including lung, pelvic, and head and neck tumors were analyzed in real time. A rigid registration algorithm, based on the iterative closest point (ICP) approach, is employed to estimate the patient position in 6 degrees of freedom (DOF). This position is compared to the reference values obtained by the radiograph imaging. The mean setup error and its standard deviation were calculated for all measured fractions. Results: The positioning error showed 1.1 ± 1.1 mm in lateral, 1.8 ± 2.1 mm in longitudinal, and 0.8 ± 1.1 mm in vertical, and 0.3°± 0.4° in yaw, 0.2°± 0.2° in pitch, and 0.2°± 0.2° in roll directions. The larger setup error occurred in pelvic regions. Conclusion: We have evaluated in a radiotherapy set‐up considering different patient anatomical locations, a depth measurement based surface imaging solution for patient positioning considering the 6 DOF couch motion. We showed that the proposed solution allows an accurate patient positioning without the need for patient markings or the use of additional radiation dose. [ABSTRACT FROM AUTHOR]

Purpose: Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real‐time respiratory monitoring and function assessment of ICU patients. Methods: The proposed system uses a Time‐of‐Flight depth sensor to analyze the patient's chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital. Results: The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P < 0.001 and r = 0.99; P < 0.001) in the mannequin recordings and (r = 0.95, P < 0.001 and r = 0.90, P < 0.001) for patients. Conclusion: This study describes and evaluates a novel noncontact monitoring system suitable for continuous monitoring of key respiratory parameters for disease assessment of critically ill patients. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the patient positioning accuracy in radiotherapy using a stereo-time of flight (ToF)-camera system. Methods: A system using two ToF cameras was used to scan the surface of the patients in order to position them daily on the treatment couch. The obtained point clouds were registered to (a) detect translations applied to the table (intrafraction motion) and (b) predict the displacement to be applied in order to place the patient in its reference position (interfraction motion). The measures provided by this system were compared to the effectively applied translations. The authors analyzed 150 fractions including lung, pelvis/prostate, and head and neck cancer patients. Results: The authors obtained small absolute errors for displacement detection: 0.8 ± 0.7, 0.8 ± 0.7, and 0.7 ± 0.6 mm along the vertical, longitudinal, and lateral axes, respectively, and 0.8 ± 0.7 mm for the total norm displacement. Lung cancer patients presented the largest errors with a respective mean of 1.1 ± 0.9, 0.9 ± 0.9, and 0.8 ± 0.7 mm. Conclusions: The proposed stereo-ToF system allows for sufficient accuracy and faster patient repositioning in radiotherapy. Its capability to track the complete patient surface in real time could allow, in the future, not only for an accurate positioning but also a real time tracking of any patient intrafraction motion (translation, involuntary, and breathing). [ABSTRACT FROM AUTHOR]

Purpose: This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient's treatment position and allow for its modification if necessary. Methods: A Kinect camera (Microsoft, Redmond,WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the SKANECT software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0?, while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in PINNACLE, and this information was exported to AlignRT (VisionRT, London, UK)--a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Results: Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation -1.2°). The accuracy study for the Kinect-Skanect surface showed an average discrepancy between the CT external contour and the surface scan of 2.2 mm. Conclusions: This methodology provides fast and reliable collision predictions using surface imaging. The use of the Kinect-Skanect system allows for a comprehensive modeling of the patient topography including all the relevant anatomy and immobilization devices that may lead to collisions. The use of this tool at the treatment simulation stage may allow therapists to evaluate the clearance of a patient's treatment position and optimize it before the planning CT scan is performed. This can allow for safer treatments for the patients due to better collision predictions and improved clinical workflow by minimizing replanning and resimulations due to unforeseen clearance issues. [ABSTRACT FROM AUTHOR]

Purpose: Surface image guided, Linac-based radiosurgery (SIG-RS) is a modern approach for delivering radiosurgery that utilizes optical stereoscopic imaging to monitor the surface of the patient during treatment in lieu of using a head frame for patient immobilization. Considering the novelty of the SIG-RS approach and the severity of errors associated with delivery of large doses per fraction, a risk assessment should be conducted to identify potential hazards, determine their causes, and formulate mitigation strategies. The purpose of this work is to investigate SIG-RS using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA), report on the effort required to complete the analysis, and evaluate the use of FTA in conjunction with FMEA. Methods: A multidisciplinary team was assembled to conduct the FMEA on the SIG-RS process. A process map detailing the steps of the SIG-RS was created to guide the FMEA. Failure modes were determined for each step in the SIG-RS process, and risk priority numbers (RPNs) were estimated for each failure mode to facilitate risk stratification. The failure modes were ranked by RPN, and FTA was used to determine the root factors contributing to the riskiest failure modes. Using the FTA, mitigation strategies were formulated to address the root factors and reduce the risk of the process. The RPNs were re-estimated based on the mitigation strategies to determine the margin of risk reduction. Results: The FMEA and FTAs for the top two failure modes required an effort of 36 person-hours (30 person-hours for the FMEA and 6 person-hours for two FTAs). The SIG-RS process consisted of 13 major subprocesses and 91 steps, which amounted to 167 failure modes. Of the 91 steps, 16 were directly related to surface imaging. Twenty-five failure modes resulted in a RPN of 100 or greater. Only one of these top 25 failure modes was specific to surface imaging. The riskiest surface imaging failure mode had an overall RPN-rank of eighth. Mitigation strategies for the top failure mode decreased the RPN from 288 to 72. Conclusions: Based on the FMEA performed in this work, the use of surface imaging for monitoring intrafraction position in Linac-based stereotactic radiosurgery (SRS) did not greatly increase the risk of the Linac-based SRS process. In some cases, SIG helped to reduce the risk of Linac-based RS. The FMEA was augmented by the use of FTA since it divided the failure modes into their fundamental components, which simplified the task of developing mitigation strategies. [ABSTRACT FROM AUTHOR]

Surface imaging is in use in radiotherapy clinical practice for patient setup optimization and monitoring. Breast alignment is accomplished by searching for a tentative spatial correspondence between the reference and daily surface shape models. In this study, the authors quantify whole breast shape alignment by relying on texture features digitized on 3D surface models. Texture feature localization was validated through repeated measurements in a silicone breast phantom, mounted on a high precision mechanical stage. Clinical investigations on breast shape alignment included 133 fractions in 18 patients treated with accelerated partial breast irradiation. The breast shape was detected with a 3D video based surface imaging system so that breathing was compensated. An in-house algorithm for breast alignment, based on surface fitting constrained by nipple matching (constrained surface fitting), was applied. Results were compared with a commercial software where no constraints are utilized (unconstrained surface fitting). Texture feature localization was validated within 2 mm in each anatomical direction. Clinical data show that unconstrained surface fitting achieves adequate accuracy in most cases, though nipple mismatch is considerably higher than residual surface distances (3.9 mm vs 0.6 mm on average). Outliers beyond 1 cm can be experienced as the result of a degenerate surface fit, where unconstrained surface fitting is not sufficient to establish spatial correspondence. In the constrained surface fitting algorithm, average surface mismatch within 1 mm was obtained when nipple position was forced to match in the [1.5; 5] mm range. In conclusion, optimal results can be obtained by trading off the desired overall surface congruence vs matching of selected landmarks (constraint). Constrained surface fitting is put forward to represent an improvement in setup accuracy for those applications where whole breast positional reproducibility is an issue. [ABSTRACT FROM AUTHOR]