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a Catalyst™ optical surface imaging system and b modified QUASAR programmable respiratory motion phantom
Source publication
Background:
Surface-guided radiation therapy (SGRT) employs a non-invasive real-time optical surface imaging (OSI) technique for patient surface motion monitoring during radiotherapy. The main purpose of this study is to verify the real-time tracking accuracy of SGRT for respiratory motion and provide a fitting method to detect the time delay of g...
Contexts in source publication
Context 1
... Catalyst™ system (Fig. 1a), which was described by Hoisak et al. [28], includes three modules. In the test, the cRespiratory module was used for real-time motion tracking and gating radiotherapy. The sampling frequency of respiratory signals was more than 15 Hz. The appropriate scanning volume was selected in the Catalyst™ preset window, and camera parameters ...
Context 2
... QUASAR programmable respiratory motion phantom (Modus Medical Devices, London, ON, Canada) [22] was used to simulate respiratory curves. The phantom was modified in the experiment to explore the influence of amplitude variation on tracking accuracy (Fig. 1b). The phantom was placed vertically to move a translation stage along the anterior-posterior (AP) direction to simulate amplitude variation. In addition, a white plate was added as a detection plane, which was reinforced by two ...
Context 3
... to the fitted quadratic function trend line of the recorded data and its derivative curve, the thermal drift of the cameras stabilized after 17.2 min, with an average thermal drift of 0.12 mm (Fig. S1 Additional file 1). After stabilizing the thermal drifting of the cameras, the position recorded by the system remains stable, and no significant change is observed before and after the interruption. The fluctuation of the system record position is below 0.1 mm, which may be associated with the system ...
Citations
... These systems have been proposed as alternative methods in radiation therapy to indirectly track the RIM using the abdominal surface changes. Some researchers have extended the application of these systems to estimate internal breathing parameters or internal motion [15][16][17]. Future lines of research for OSI systems consider their implementation for respiratory-induced tumor motion prediction [14]. Needle insertion procedures can benefit from OSI technology as the clinician's hand, and needle can occlude markers partially (unlike radiation therapy) and thus surface scans with more points is expected to be more reliable. ...
Purpose
This work presents the implementation of an RGB-D camera as a surrogate signal for liver respiratory-induced motion estimation. This study aims to validate the feasibility of RGB-D cameras as a surrogate in a human subject experiment and to compare the performance of different correspondence models.
Methods
The proposed approach uses an RGB-D camera to compute an abdominal surface reconstruction and estimate the liver respiratory-induced motion. Two sets of validation experiments were conducted, first, using a robotic liver phantom and, secondly, performing a clinical study with human subjects. In the clinical study, three correspondence models were created changing the conditions of the learning-based model.
Results
The motion model for the robotic liver phantom displayed an error below 3 mm with a coefficient of determination above 90% for the different directions of motion. The clinical study presented errors of 4.5, 2.5, and 2.9 mm for the three different motion models with a coefficient of determination above 80% for all three cases.
Conclusion
RGB-D cameras are a promising method to accurately estimate the liver respiratory-induced motion. The internal motion can be estimated in a non-contact, noninvasive and flexible approach. Additionally, three training conditions for the correspondence model are studied to potentially mitigate intra- and inter-fraction motion.
... However, their study did not include the measurement of beam off time delay. Li et al. 16 developed a fitting-based algorithm to determine the time delay of the Catalyst™ system. Barfield et al. 17 utilized a commercial motion platform and EPID to investigate the time delay of AlignRT™ system. ...
... It is a characteristic of the system and is independent of the experimental design. In the study conducted by Li et al., 16 123.11 ± 6.44 ms,respectively.However,the consistency of beam off time delay for each energy was high in both versions, and the maximum time delay discrepancy is 4.74 ms. This phenomenon can be explained by the fact that when MU is sufficient, the time required for the dose rate of the Varian linac to reach the expected value and then decrease from the expected value to zero is almost the same. ...
Purpose
To propose a straightforward and time‐efficient quality assurance (QA) approach of beam time delay for respiratory‐gated radiotherapy and validate the proposed method on typical respiratory gating systems, Catalyst™ and AlignRT™.
Methods
The QA apparatus was composed of a motion platform and a Winston‐Lutz cube phantom (WL3) embedded with metal balls. The apparatus was first scanned in CT‐Sim and two types of QA plans specific for beam on and beam off time delay, respectively, were designed. Static reference images and motion testing images of the WL3 cube were acquired with EPID. By comparing the position differences of the embedded metal balls in the motion and reference images, beam time delays were determined. The proposed approach was validated on three linacs with either Catalyst™ or AlignRT™ respiratory gating systems. To investigate the impact of energy and dose rate on beam time delay, a range of QA plans with Eclipse (V15.7) were devised with varying energy and dose rates.
Results
For all energies, the beam on time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 92.13 5.79 ms, 123.11 6.44 ms, and 303.44 4.28 ms, respectively. The beam off time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 121.87 1.34 ms, 119.33 0.75 ms, and 97.69 2.02 ms, respectively. Furthermore, the beam on delays decreased slightly as dose rates increased for all gating systems, whereas the beam off delays remained unaffected.
Conclusions
The validation results demonstrate the proposed QA approach of beam time delay for respiratory‐gated radiotherapy was both reproducible and time‐efficient to practice for institutions to customize accordingly.
... Both the static and gated measurements were aligned by using beam spots which were irradiated under static conditions. Shifts of dose distributions obtained using gating were reported in previous studies and guidelines for both photon and particle therapies, 40,44,45,[63][64][65][66][67] and identified to be caused by delay times. Such a delay time in beam on/off can result in an under-and overdosage of OAR. ...
To account for intra-fractional tumor motion during dose delivery in radiotherapy,
various treatment strategies are clinically implemented such as breathing-adapted
gating and irradiating the tumor during specific breathing phases.
In this work, we present a comprehensive phantom-based end-to-end test of
breathing-adapted gating utilizing surface guidance for use in particle therapy.
A commercial dynamic thorax phantom was used to reproduce regular and
irregular breathing patterns recorded by the GateRT respiratory monitoring system.
The amplitudes and periods of recorded breathing patterns were analysed
and compared to planned patterns (ground-truth). In addition, the mean absolute
deviations (MAD) and Pearson correlation coefficients (PCC) between the
measurements and ground-truth were assessed. Measurements of gated and
non-gated irradiations were also analysed with respect to dosimetry and geometry,
and compared to treatment planning system (TPS). Further, the latency time
of beam on/off was evaluated. Compared to the ground-truth, measurements
performed with GateRT showed amplitude differences between 0.03 ± 0.02 mm
and 0.26 ± 0.03 mm for regular and irregular breathing patterns, whilst periods
of both breathing patterns ranged with a standard deviation between 10 and
190 ms. Furthermore, the GateRT software precisely acquired breathing patterns
with a maximum MAD of 0.30 ± 0.23 mm. The PCC constantly ranged
between 0.998 and 1.000. Comparisons between TPS and measured dose
profiles indicated absolute mean dose deviations within institutional tolerances
of ±5%. Geometrical beam characteristics also varied within our institutional
tolerances of 1.5 mm. The overall time delays were <60 ms and thus within
both recommended tolerances published by ESTRO and AAPM of 200 and
100 ms, respectively. In this study, a non-invasive optical surface-guided workflow
including image acquisition, treatment planning, patient positioning and
gated irradiation at an ion-beam gantry was investigated, and shown to be
clinically viable. Based on phantom measurements,our results show a clinically-appropriate spatial, temporal, and dosimetric accuracy when using surface
guidance in the clinical setting, and the results comply with international and
institutional guidelines and tolerances.
... Therefore, the parameter is a crucial indicator in determining the delivery accuracy. Each Linac must also measure the parameter when a new gating device is applied [12,13]. ...
... Some devices could not monitor in real-time due to their computational complexity. Studies using cameras tended to use either the algorithm by Viola-Jones [12][13][14][15] or YOLO [16] to automatically detect the ROI. Several studies have also used the KLT algorithm to track slight motions to stabilize the ROI for real-time measurement [12][13][14][15]17]. ...
... Studies using cameras tended to use either the algorithm by Viola-Jones [12][13][14][15] or YOLO [16] to automatically detect the ROI. Several studies have also used the KLT algorithm to track slight motions to stabilize the ROI for real-time measurement [12][13][14][15]17]. Meanwhile, other studies used Python libraries, such as OpenCV, which was then integrated with the aforementioned algorithms [13, [18][19][20]. ...
Background: The occurrence of motion in the thoracoabdominal region during radiotherapy treatment is an inherent challenge affecting the accuracy of the radiation beam. To address this challenge, a margin is often incorporated to compensate for the motion, but it has been reported to have several limitations. Consequently, respiratory gating has emerged as an integrated feature within radiotherapy-related machines. This innovative approach is designed to overcome motion-related challenges, leading to a reduction in the required margin and an improvement in the accuracy of the radiation beam. Methods: This study reviews the literature published in English between 2012 to 2021 regarding breathing monitoring devices used in the clinical or research stage. Furthermore, articles published before 2000 were traced to strengthen the theories. Results: Several monitoring devices had been reported to have respiratory gating purposes, but some were not equipped for this function. Furthermore, these devices were often developed using non-contact equipment, such as lasers and cameras, to provide accurate and precise measurements. One of their key advantages is the lack of physical attachment to the patients, thereby preserving comfort. The development of respiratory gating devices had significant potential to enhance the quality of radiotherapy treatment. This was manifested through more effective tumor and organ treatment and reduced toxicity. These benefits had the potential to extend the life expectancy of patients with respiratory-related cancer. Conclusions: Based on the results, respiratory gating was an advantageous technique in radiotherapy treatment. The development of respiratory gating devices enhanced patient comfort and the effectiveness of treatment.
... 15 Most published methods for gating latency measurements have relied on moving phantoms with radiographic film where latencies have been inferred from analyses of measured exposure areas or dose profiles of the moving film. [16][17][18] These methods are also indirect and susceptible to interpretation errors or uncertainties in film analyses as pointed out by Wiersma et al. 19 In an alternative approach, the beam signal during gated treatment has been measured with a PIN-diode circuit and compared with the trigger signal to a motion phantom through a microcontroller unit in order to estimate gating latencies. 20 A related method relied on temporal comparisons between diode-measured beam status and the position of a motion stage relative to the gating window as measured by a potentiometer coupled to the motion stage. ...
... For example, the latencies have been extracted by comparing the theoretical film exposure area of a moving film (function of τ on and τ off ) with the measured area or by fitting expected dose profiles (function of τ on and τ off ) with measured dose profiles. [16][17][18] For gating at a TrueBeam equipped with the RPM 1.7.5 system, Chugh et al. reported mean latencies of τ on = 64 ± 19 ms and τ off = 57 ± 17 ms based on radiographic film measurements with sine motion. 16 However, film-based methods are indirect and do not show the latency at each respiratory cycle. ...
Background
In respiratory gated radiotherapy, low latency between target motion into and out of the gating window and actual beam‐on and beam‐off is crucial for the treatment accuracy. However, there is presently a lack of guidelines and accurate methods for gating latency measurements.
Purpose
To develop a simple and reliable method for gating latency measurements that work across different radiotherapy platforms.
Methods
Gating latencies were measured at a Varian ProBeam (protons, RPM gating system) and TrueBeam (photons, TrueBeam gating system) accelerator. A motion‐stage performed 1 cm vertical sinusoidal motion of a marker block that was optically tracked by the gating system. An amplitude gating window was set to cover the posterior half of the motion (0–0.5 cm). Gated beams were delivered to a 5 mm cubic scintillating ZnSe:O crystal that emitted visible light when irradiated, thereby directly showing when the beam was on. During gated beam delivery, a video camera acquired images at 120 Hz of the moving marker block and light‐emitting crystal. After treatment, the block position and crystal light intensity were determined in all video frames. Two methods were used to determine the gate‐on (τon) and gate‐off (τoff) latencies.
By method 1, the video was synchronized with gating log files by temporal alignment of the same block motion recorded in both the video and the log files. τon was defined as the time from the block entered the gating window (from gating log files) to the actual beam‐on as detected by the crystal light. Similarly, τoff was the time from the block exited the gating window to beam‐off. By method 2, τon and τoff were found from the videos alone using motion of different sine periods (1–10 s). In each video, a sinusoidal fit of the block motion provided the times Tmin of the lowest block position. The mid‐time, Tmid‐light, of each beam‐on period was determined as the time halfway between crystal light signal start and end. It can be shown that the directly measurable quantity Tmid‐light − Tmin = (τoff+τon)/2, which provided the sum (τoff+τon) of the two latencies. It can also be shown that the beam‐on (i.e., crystal light) duration ΔTlight increases linearly with the sine period and depends on τoff − τon: ΔTlight = constant•period+(τoff − τon). Hence, a linear fit of ΔTlight as a function of the period provided the difference of the two latencies. From the sum (τoff+τon) and difference (τoff − τon), the individual latencies were determined.
Results
Method 1 resulted in mean (±SD) latencies of τon = 255 ± 33 ms, τoff = 82 ± 15 ms for the ProBeam and τon = 84 ± 13 ms, τoff = 44 ± 11 ms for the TrueBeam. Method 2 resulted in latencies of τon = 255 ± 23 ms, τoff = 95 ± 23 ms for the ProBeam and τon = 83 ± 8 ms, τoff = 46 ± 8 ms for the TrueBeam. Hence, the mean latencies determined by the two methods agreed within 13 ms for the ProBeam and within 2 ms for the TrueBeam.
Conclusions
A novel, simple and low‐cost method for gating latency measurements that work across different radiotherapy platforms was demonstrated. Only the TrueBeam fully fulfilled the AAPM TG‐142 recommendation of maximum 100 ms latencies.
... The system latency is the time delay between the instructed and the actual beam on/off during respiratory-gated radiation therapy. Medical linear accelerators (linacs) have system latencies ranging from 300 ms to 800 ms; the Elekta (Stockholm, Sweden) linacs have latencies of 300-800 ms, and the Varian (Crawley, United Kingdom) linacs have latencies of 300-500 ms [16][17][18]. The system latency can cause position errors to the target and OARs of up to 7.6 mm [19]. ...
For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated. Among the 540 respiration signals, 60 signals are used as test data. Each of the remaining 480 signals was spilt into training and validation data in a 7:3 ratio. A total of 1000 ms of the signal sequence (Ts) is entered to the models, and the signal at 500 ms afterward (Pt) is predicted (standard training condition). The accuracy measures are: (1) root mean square error (RMSE) and Pearson correlation coefficient (CC), (2) accuracy dependency on Ts and Pt, (3) respiratory pattern dependency, and (4) error for 30% and 70% of the respiration gating for a 5 mm tumor motion for latencies of 300, 500, and 700 ms. Under standard conditions, the Transformer model exhibits the highest accuracy with an RMSE and CC of 0.1554 and 0.9768, respectively. An increase in Ts improves accuracy, whereas an increase in Pt decreases accuracy. An evaluation of the regularity of the respiratory signals reveals that the lowest predictive accuracy is achieved with irregular amplitude patterns. For 30% and 70% of the phases, the average error of the three models is <1.4 mm for a latency of 500 ms and >2.0 mm for a latency of 700 ms. The prediction accuracy of the Transformer is superior to LSTM and Bi-LSTM. Thus, the three models have clinically applicable accuracies for a latency <500 ms for 10 mm of regular tumor motion. The clinical acceptability of the deep learning models depends on the inherent latency and the strategy for reducing the irregularity of respiration.
... Time-delays are general in many actual systems, for instance, circuits, neural network systems, biological medicine, building structure and multi-agent systems [1][2][3][4]. However, a time-delay may reduce the performance of dynamic systems and even lead to system instability. ...
The main concern of this paper is finite-time stability (FTS) for uncertain discrete-time stochastic nonlinear systems (DSNSs) with time-varying delay (TVD) and multiplicative noise. First, a Lyapunov–Krasovskii function (LKF) is constructed, using the forward difference, and less conservative stability criteria are obtained. By solving a series of linear matrix inequalities (LMIs), some sufficient conditions for FTS of the stochastic system are found. Moreover, FTS is presented for a stochastic nominal system. Lastly, the validity and improvement of the proposed methods are shown with two simulation examples.
... [25] Three different databases are utilized in this work to assess the effect of fuzzy logic on IGRT treatment quality enhancement as (1) tomography images provided by anthropomorphic 4DXCAT phantom for edge detection calculations, (2) computed tomography (CT) data of real patients with defining external markers located on patient body surface for simulating patient setup before the treatment and (3) motion data of real patients treated with Cyberknife Synchrony system for real-time tumor motion tracking during the treatment. [29][30][31][32][33] The latter case includes internal tumor motion data synchronized with corresponding external rib cage and abdominal motion data as a function of time. [31,32] In this work, fuzzy logic is proposed to increase treatment precision by: (1) modifying of edge detection process used for target definition and by (2) improving patient positioning and (3) real-time tumor motion tracking. ...
At image-guided radiotherapy, technique, different imaging, and monitoring systems are utilized for (i) organs border detection and tumor delineation during the treatment planning process and (ii) patient setup and tumor localization at pretreatment step and (iii) real-time tumor motion tracking for dynamic thorax tumors during the treatment. In this study, the effect of fuzzy logic is quantitatively investigated at different steps of image-guided radiotherapy. Fuzzy logic-based models and algorithms have been implemented at three steps, and the obtained results are compared with commonly available strategies. Required data are (i) real patients treated with Synchrony Cyberknife system at Georgetown University Hospital for real-time tumor motion prediction, (ii) computed tomography images taken from real patients for geometrical setup, and also (iii) tomography images of an anthropomorphic phantom for tumor delineation process. In real-time tumor tracking, the targeting error averages of the fuzzy correlation model in comparison with the Cyberknife modeler are 4.57 mm and 8.97 mm, respectively, for a given patient that shows remarkable error reduction. In the case of patient geometrical setup, the fuzzy logic-based algorithm has better influence in comparing with the artificial neural network, while the setup error averages is reduced from 1.47 to 0.4432 mm using the fuzzy logic-based method, for a given patient.Finally, the obtained results show that the fuzzy logic based image processing algorithm exhibits much better performance for edge detection compared to four conventional operators. This study is an effort to show that fuzzy logic based algorithms are also highly applicable at image-guided radiotherapy as one of the important treatment modalities for tumor delineation, patient setup error reduction, and intrafractional motion error compensation due to their inherent properties.
... Como puede apreciarse, existe gran disparidad en los valores de las latencias registradas, obtenidas mediante diferentes metodologías, aunque la mayoría de estas están centradas en la evaluación del ensanchamiento del campo de radiación administrado con control respiratorio respecto a una situación estática. [140][141][142] Según una revisión de Chen et al., 143 las máximas diferencias en los tiempos de activación e interrupción del haz, recopiladas en diferentes publicaciones, son de hasta 270 ms y 485 ms, respectivamente, e incluso para un mismo sistema de control respiratorio, las latencias encontradas por diversos autores difieren. 86,142 Uno de los motivos de estas variaciones se debe a los mecanismos empleados por las unidades de tratamiento para mantener, interrumpir y reanudar el haz de radiación. ...
Se presentan las conclusiones del estudio realizado por el Grupo de Trabajo sobre Radioterapia Guiada por Superficie, promovido por la Sociedad Española de Física Médica. Los objetivos han sido la elaboración de procedimientos para la aceptación y puesta en funcionamiento de dispositivos de posicionamiento, monitorización y seguimiento, basados en el reconocimiento de la superficie del paciente, orientaciones para la adquisición e instalación de este equipamiento, así como establecer recomendaciones para un uso clínico fiable. Se ha delimitado el campo de estudio a los programas y dispositivos comerciales, evaluando las ventajas y limitaciones de estas técnicas para diferentes aplicaciones en Radioterapia. Basados en la bibliografía disponible, y en la experiencia de centros nacionales con implantación de la SGRT, se han establecido tolerancias y periodicidades para las distintas pruebas de control de calidad. Además, se han propuesto líneas de trabajo para la implantación de técnicas especiales que requieren mayor exactitud por parte de estos dispositivos. Finalmente, se han incluido tres anexos con orientaciones prácticas sobre la medida y análisis de las latencias de esos sistemas, la construcción de maniquíes de control de calidad para su medición, así como las implicaciones clínicas asociadas a las mismas en el tratamiento de lesiones sometidas a movimiento respiratorio.
... Drift is generally assessed as part of system commissioning [3]. Limited results have been reported related to the drift of thermal cameras for SGRT [4,5] and other [6] patient monitoring systems. Conceptually simple to measure [7], the implications of drift on clinical procedures are more complex and have not been described in the literature. ...
Thermal drift of optical systems employed for surface guided radiation therapy (SGRT) adds uncertainty to patient setup and monitoring. This work describes methods to measure the drift of individual camera pods as well as the drift of the combined clinical signal. It presents results for four clinical C-Rad Catalyst⁺ HD systems. Based on the measured clinical drift, recipes are provided on how to calculate relevant uncertainties in patient setup and patient position monitoring with SGRT. Strategies to reduce the impact of drift are explained. While the results are specific to the systems investigated, the methodology is transferable and the clinical recipes are universally applicable.
