Development of system using beam's eye view images to measure respiratory motion tracking errors in image-guided robotic radiosurgery system

Comparative evaluation of a surface-based respiratory monitoring system against a pressure sensor for 4DCT image reconstruction in phantoms

Four-dimensional computed tomography (4DCT), which relies on breathing-induced motion, requires realistic surrogate information of breathing variations to reconstruct the tumor trajectory and motion variability of normal tissues accurately. Therefore, the SimRT surface-guided respiratory monitoring system has been installed on a Siemens CT scanner. This work evaluated the temporal and spatial accuracy of SimRT versus our commonly used pressure sensor, AZ-733 V. A dynamic thorax phantom was used to reproduce regular and irregular breathing patterns acquired by SimRT and Anzai. Various parameters of the recorded breathing patterns, including mean absolute deviations (MAD), Pearson correlations (PC), and tagging precision, were investigated and compared to ground-truth. Furthermore, 4DCT reconstructions were analyzed to assess the volume discrepancy, shape deformation and tumor trajectory. Compared to the ground-truth, SimRT more precisely reproduced the breathing patterns with a MAD range of 0.37 ± 0.27 and 0.92 ± 1.02 mm versus Anzai with 1.75 ± 1.54 and 5.85 ± 3.61 mm for regular and irregular breathing patterns, respectively. Additionally, SimRT provided a more robust PC of 0.994 ± 0.009 and 0.936 ± 0.062 for all investigated breathing patterns. Further, the peak and valley recognition were found to be more accurate and stable using SimRT. The comparison of tumor trajectories revealed discrepancies up to 7.2 and 2.3 mm for Anzai and SimRT, respectively. Moreover, volume discrepancies up to 1.71 ± 1.62% and 1.24 ± 2.02% were found for both Anzai and SimRT, respectively. SimRT was validated across various breathing patterns and showed a more precise and stable breathing tracking, (i) independent of the amplitude and period, (ii) and without placing any physical devices on the patient's body. These findings resulted in a more accurate temporal and spatial accuracy, thus leading to a more realistic 4DCT reconstruction and breathing-adapted treatment planning.

Real-Time 2D MR Cine From Beam Eye's View With Tumor-Volume Projection to Ensure Beam-to-Tumor Conformality for MR-Guided Radiotherapy of Lung Cancer

Purpose

To minimize computation latency using a predictive strategy to retrieve and project tumor volume onto 2D MR beam eye’s view (BEV) cine from time-resolved four-dimensional magnetic resonance imaging (TR-4DMRI) libraries (inhalation/exhalation) for personalized MR-guided intensity-modulated radiotherapy (IMRT) or volumetric-modulated arc therapy (VMAT).

Methods

Two time-series forecasting algorithms, autoregressive (AR) modeling and deep-learning-based long short-term memory (LSTM), were applied to predict the diaphragm position in the next 2D BEV cine to identify a motion-matched and hysteresis-accounted image to retrieve the tumor volume from the inhalation/exhalation TR-4DMRI libraries. Three 40-s TR-4DMRI (2 Hz, 3 × 80 images) per patient of eight lung cancer patients were used to create patient-specific inhalation/exhalation 4DMRI libraries, extract diaphragmatic waveforms, and interpolate them to f = 4 and 8 Hz to match 2D cine frame rates. Along a (40•f)-timepoint waveform, 30•f training timepoints were moved forward to produce 3×(10•f-1) predictions. The accuracy of position prediction was assessed against the waveform ground truth. The accuracy of tumor volume projections was evaluated using the center-of-mass difference (∆COM) and Dice similarity index against the TR-4DMRI ground truth for both IMRT (six beam angles, 30° interval) and VMAT (240/480 beam angles, 1.5°/0.75° interval, at 4/8 Hz, respectively).

Results

The accuracy of the first-timepoint prediction is 0.36 ± 0.10 mm (AR) and 0.62 ± 0.21 mm (LSTM) at 4 Hz and 0.06 ± 0.02 mm (AR) and 0.18 ± 0.06 mm (LSTM) at 8 Hz. A 10%–20% random error in prediction-library matching increases the overall uncertainty slightly. For both IMRT and VMAT, the accuracy of projected tumor volume contours on 2D BEV cine is ∆COM = 0.39 ± 0.13 mm and DICE = 0.97 ± 0.02 at 4 Hz and ∆COM = 0.10 ± 0.04 mm and DICE = 1.00 ± 0.00 at 8Hz.

Conclusion

This study demonstrates the feasibility of accurately predicting respiratory motion during 2D BEV cine imaging, identifying a motion-matched and hysteresis-accounted tumor volume, and projecting tumor volume contour on 2D BEV cine for real-time assessment of beam-to-tumor conformality, promising for optimal personalized MR-guided radiotherapy.

Development of a tracking error prediction system for the CyberKnife Synchrony Respiratory Tracking System with use of support vector regression

PURPOSE

The accuracy of the CyberKnife Synchrony Respiratory Tracking System is dependent on the breathing pattern of a patient. Therefore, the tracking error in each patient must be determined. Support vector regression (SVR) can be used to easily identify the tracking error in each patient. This study aimed to develop a system with SVR that can predict tracking error according to a patient's respiratory waveform.

METHODS

Datasets of the respiratory waveforms of 93 patients were obtained. The feature variables were variation in respiration amplitude, tumor velocity, and phase shift between tumor and the chest wall, and the target variable was tracking error. A learning model was evaluated with tenfold cross-validation. We documented the difference between the predicted and actual tracking errors and assessed the correlation coefficient and coefficient of determination.

RESULTS

The average difference and maximum difference between the actual and predicted tracking errors were 0.57 ± 0.63 mm and 2.1 mm, respectively. The correlation coefficient and coefficient of determination were 0.86 and 0.74, respectively.

CONCLUSION

We developed a system for obtaining tracking error by using SVR. The accuracy of such a system is clinically useful. Moreover, the system can easily evaluate tracking error. We developed a system that can be used to predict the tracking error of SRTS in the CyberKnife Robotic Radiosurgery System using machine learning. The feature variables were the breathing parameters, and the target variable was the tracking error. We used support vector regression algorithm.

Comparison of modeling accuracy between Radixact®and CyberKnife®Synchrony®respiratory tracking system

Synchrony Respiratory Tracking system adapted from CyberKnife has been introduced in Radixact to compensate the tumor motion caused by respiration. This study aims to compare the modeling accuracy of the Synchrony system between Radixact and CyberKnife. Two Synchrony plans based on fiducial phantoms were created for CyberKnife and Radixact, respectively. Different respiratory motion traces were used to drive a motion platform to move along the superoinferior and left-right direction. The cycle time and the amplitude of target/surrogate motion of one selected motion trace were scaled to investigate the dependence of modeling accuracy on the motion characteristic. The predicted target position, the correlation error, potential difference (Radixact only) and standard error (CyberKnife only) were extracted from raw data or log files of the two systems. The modeling accuracy was evaluated by calculating the root-mean-square (RMS) error between the predicted target positions and the input motion trace. A threshold T95 within which 95% of the potential difference or the standard error lay was defined and evaluated. Except for the motion trace with a small amplitude and a good (linear) correlation between target and surrogate motion, Radixact showed smaller RMS errors than CyberKnife. The RMS error of both systems increased with the motion amplitude and showed a decreasing trend with the increasing cycle time. No correlation was found between the RMS error and the amplitude of surrogate motion. T95 could be a good estimator of modeling accuracy for CyberKnife rather than Radixact. The correlation error defined in Radixact were largely affected by the number of fiducial markers and the setup error. In general, the modeling accuracy of the Radixact Synchrony system is better than that of the CyberKnife Synchrony system under unfavorable conditions.

Detectability of fiducials' positions for real-time target tracking system equipping with a standard linac for multiple fiducial markers

PURPOSE

To investigate the detectability of fiducial markers' positions for real-time target tracking system equipping with a standard linac. The hypothesis is that the detectability depends on the type of fiducial marker and the gantry angle of acquired triggered images.

METHODS

Three types of ball fiducials and four slim fiducials with lengths of 3 and 5 mm were prepared for this study. Triggered images with three similar fiducials were acquired at every 10° during the conformal arc irradiation to detect the target position. Although only one type of arrangement was prepared for the ball fiducials, a three-type arrangement was prepared for the slim fiducials, such as parallel, orthogonal, and oblique with 45° to the gantry-couch direction. To measure the detectability of the real-time target tracking system for each fiducial and arrangement, detected marker positions were compared with expected marker positions at every angle of acquired triggered images.

RESULTS

For the ball-type fiducial, the maximum difference between the detected marker positions and expected marker positions was 0.3 mm in all directions. For the slim fiducial arranged parallel and oblique with 45°, the maximum difference was 0.4 mm in all directions. When each slim fiducial was arranged orthogonal to the gantry-couch direction, the maximum difference was 1.5 mm for the length of 3 mm, and 3.2 mm for the length of 5 mm.

CONCLUSIONS

The detectability of fiducial markers' positions for the real-time target tracking system equipping with a standard linac depends on the form and insertion angles of the fiducials.

Evaluation of radixact motion synchrony for 3D respiratory motion: Modeling accuracy and dosimetric fidelity

The Radixact® linear accelerator contains the motion Synchrony system, which tracks and compensates for intrafraction patient motion. For respiratory motion, the system models the motion of the target and synchronizes the delivery of radiation with this motion using the jaws and multi-leaf collimators (MLCs). It was the purpose of this work to determine the ability of the Synchrony system to track and compensate for different phantom motions using a delivery quality assurance (DQA) workflow. Thirteen helical plans were created on static datasets from liver, lung, and pancreas subjects. Dose distributions were measured using a Delta4® Phantom+ mounted on a Hexamotion® stage for the following three case scenarios for each plan: (a) no phantom motion and no Synchrony (M0S0), (b) phantom motion and no Synchrony (M1S0), and (c) phantom motion with Synchrony (M1S1). The LEDs were placed on the Phantom+ for the 13 patient cases and were placed on a separate one-dimensional surrogate stage for additional studies to investigate the effect of separate target and surrogate motion. The root-mean-square (RMS) error between the Synchrony-modeled positions and the programmed phantom positions was <1.5 mm for all Synchrony deliveries with the LEDs on the Phantom+. The tracking errors increased slightly when the LEDs were placed on the surrogate stage but were similar to tracking errors observed for other motion tracking systems such as CyberKnife Synchrony. One-dimensional profiles indicate the effects of motion interplay and dose blurring present in several of the M1S0 plans that are not present in the M1S1 plans. All 13 of the M1S1 measured doses had gamma pass rates (3%/2 mm/10%T) compared to the planned dose > 90%. Only two of the M1S0 measured doses had gamma pass rates > 90%. Motion Synchrony offers a potential alternative to the current, ITV-based motion management strategy for helical tomotherapy deliveries.

Evaluation of newly implemented dose calculation algorithms for multileaf collimator-based CyberKnife tumor-tracking radiotherapy

PURPOSE

In the previous treatment planning system (TPS) for CyberKnife (CK), multileaf collimator (MLC)-based treatment plans could be created only by using the finite-size pencil beam (FSPB) algorithm. Recently, a new TPS, including the FSPB with lateral scaling option (FSPB+) and Monte Carlo (MC) algorithms, was developed. In this study, we performed basic and clinical end-to-end evaluations for MLC-based CK tumor-tracking radiotherapy using the MC, FSPB+, and FSPB.

METHODS

Water- and lung-equivalent slab phantoms were combined to obtain the percentage depth dose (PDD) and off-center ratio (OCR). The CK M6 system and Precision TPS were employed, and PDDs and OCRs calculated by the MC, FSPB+, and FSPB were compared with the measured doses obtained for 30.8 × 30.8 mm² and 60.0 × 61.6 mm² fields. A lung motion phantom was used for clinical evaluation and MLC-based treatment plans were created using the MC. The doses were subsequently recalculated using the FSPB+ and FSPB, while maintaining the irradiation parameters. The calculated doses were compared with the doses measured using a microchamber (for target doses) or a radiochromic film (for dose profiles). The dose volume histogram (DVH) indices were compared for all plans.

RESULTS

In homogeneous and inhomogeneous phantom geometries, the PDDs calculated by the MC and FSPB+ agreed with the measurements within ±2.0% for the region between the surface and a depth of 250 mm, whereas the doses calculated by the FSPB in the lung-equivalent phantom region were noticeably higher than the measurements, and the maximum dose differences were 6.1% and 4.4% for the 30.8 × 30.8 mm² and 60.0 × 61.6 mm² fields, respectively. The maximum distance to agreement values of the MC, FSPB+, and FSPB at the penumbra regions of OCRs were 1.0, 0.6, and 1.1 mm, respectively, but the best agreement was obtained between the MC-calculated curve and measurements at the boundary of the water- and lung-equivalent slabs, compared with those of the FSPB+ and FSPB. For clinical evaluations using the lung motion phantom, under the static motion condition, the dose errors measured by the microchamber were -1.0%, -1.9%, and 8.8% for MC, FSPB+, and FSPB, respectively; their gamma pass rates for the 3%/2 mm criterion comparing to film measurement were 98.4%, 87.6%, and 31.4% respectively. Under respiratory motion conditions, there was no noticeable decline in the gamma pass rates. In the DVH indices, for most of the gross tumor volume and planning target volume, significant differences were observed between the MC and FSPB, and between the FSPB+ and FSPB. Furthermore, significant differences were observed for lung D_mean , V_{15 Gy} , and V_{20 Gy} between the MC, FSPB+, and FSPB.

CONCLUSIONS

The results indicate that the doses calculated using the MC and FSPB+ differed remarkably in inhomogeneous regions, compared with the FSPB. Because the MC was the most consistent with the measurements, it is recommended for final dose calculations in inhomogeneous regions such as the lung. Furthermore, the sufficient accuracy of dose delivery using MLC-based tumor-tracking radiotherapy by CK was demonstrated for clinical implementation.

Factors affecting the accuracy of respiratory tracking of the image-guided robotic radiosurgery system

PURPOSE

To analyze the factors affecting the tracking accuracy of the CyberKnife Synchrony Respiratory Tracking System (SRTS).

MATERIALS AND METHODS

A dynamic motion phantom (motion phantom) reproduced the respiratory motions of each patient treated with the SRTS using a ball as the target. CyberKnife tracked the ball using the SRTS, and this process was recorded by a video camera mounted on the linear accelerator head. The tracking error was evaluated from the images captured by the video camera. Multiple regression analysis was used to identify factors affecting tracking accuracy from 91 cases.

RESULTS

The median tracking error was 1.9 mm (range 0.9-5.3 mm). Four factors affected the tracking accuracy: the average absolute amplitude of the tumor motion in the cranio-caudal (CC) direction (p = 0.007), average position gap due to the phase shift between the internal tumor and external marker positions in the CC direction (p < 0.001), and average velocity of the tumor in the CC (p < 0.001) and anterior-posterior directions (p = 0.033).

CONCLUSION

We identified factors that affected tracking accuracy. This information may assist the identification of suitable margins that should be added to each patient's clinical target volume.

Impacts of respiratory phase shifts on motion-tracking accuracy of the CyberKnife Synchrony™ Respiratory Tracking System

PURPOSE

The Synchrony^TM Respiratory Tracking System (SRTS) component of the CyberKnife^® Robotic Radiosurgery System (Accuray, Inc., Sunnyvale CA) enables real-time tracking of moving targets by modeling the correlation between the targets and external surrogate light-emitting diode (LED) markers placed on the patient's chest. Previous studies reported some cases with respiratory phase shifts between lung tumor and chest wall motions. In this study, the impacts of respiratory phase shifts on the motion-tracking accuracy of the SRTS were investigated.

METHODS

A plastic scintillator was used to detect the position of the x-ray beams. The scintillation light was recorded using a camera in a dark room. A moving phantom moved a U-shaped frame on the scintillator with a 4th power of sinusoidal functions. Three metallic markers for motion tracking and four fluorescent tapes were attached to the frame. The fluorescent tapes were used to identify phantom position and respiratory phase for each video frame. The beam positions collected, when considered relative to the phantom motion, represent the degree of tracking error. Beam position was calculated by adding error value to phantom position. Motions with respiratory phase shifts between the target and an extra stage mimicking chest wall motion were also tested for LED markers. Log files of the SRTS were analyzed to evaluate correlation errors.

RESULTS

When target and LED marker motions were synchronized with a respiratory cycle of 4 s, the maximum tracking errors for 90% and 95% of beam-on time were 1.0 mm and 1.2 mm, respectively. The frequency of tracking errors increased when LED marker motion phase preceded target motion. Tracking errors that corresponded to 90% beam-on time were within 2.4 mm for 5-15% of phase shifts. In contrast, the tracking errors were very large when the LED marker delayed to the target motions; the maximum errors of 90% beam-on time were 3.0, 3.8, and 7.5 mm for 5%, 10%, and 15% of phase shifts, respectively. The patterns of the tracking errors derived from the scintillation light were very similar to those of the correlation data of the SRTS derived from the log files, indicating that the tracking errors caused mainly due to the errors in modeling the correlation data. With long respiratory cycle of 6 s, the tracking errors were significantly decreased; the maximum tracking errors for 95% beam-on time were 1.6 mm and 2.2 mm for early and delayed LED motion.

CONCLUSION

We have investigated the motion-tracking accuracy of the CyberKnife SRTS for cases with the respiratory phase shift between the target and the LED marker. The maximum tracking errors for 90% probability were within 2.4 mm when the target delays to the LED markers. When LED marker delays, however, very large tracking errors were observed. With a long respiratory cycle, the tracking errors were greatly improved to less than 2.2 mm. Coaching slow breathing will be useful for accurate motion tracking radiotherapy.

Performance evaluation of the CyberKnife system in real-time target tracking during beam delivery using a moving phantom coupled with two-dimensional detector array

The aim of the current study was to evaluate the tracking error of the Synchrony Respiratory Tracking system by conducting beam-by-beam analyses to determine the variation in the tracking beams measured during target motion. A moving phantom of in-house design coupled with a two-dimensional (2D) detector array was used to simulate respiratory motion in the superoinferior (SI) and anteroposterior (AP) direction. A styrofoam block with four implanted fiducial markers was placed on top of the detector to enable the fiducial-based respiratory tracking. Measurements were performed with the phantom under either stationary mode or sinusoidal motion of 6-s cycle and 15/20-mm amplitude at SI and AP direction. The measurement data were saved as movie files that were used to calculate the center shift of the beam with 100-ms sampling time. The tracking accuracy of the system was defined as the targeting error, which could be tracked with probability of > 95% (Ep95). The mean ± standard deviation of Ep95 was 0.28 ± 0.08 mm under stationary condition; 0.66 ± 0.23 mm (range: 0.28-1.22 mm) under sinusoidal respiratory motion. The maximum drift of the beam center for all beam paths was 2.7 mm. The tracking accuracy of CyberKnife Synchrony system was successfully evaluated using a moving phantom and 2D detector array; the maximum tracking error was < 1.5 mm for sinusoidal motion of amplitude ≤ 20 mm.

Evaluation of the 4D RADPOS dosimetry system for dose and position quality assurance of CyberKnife

PURPOSE

The Synchrony respiratory motion tracking of the CyberKnife system purports to provide real-time tumor motion compensation during robotic radiosurgery. Such a complex delivery system requires thorough quality assurance. In this work, RADPOS applicability as a dose and position quality assurance tool for CyberKnife treatments is assessed quantitatively for different phantom types and breathing motions, which increase in complexity to more closely resemble clinical situations.

METHODS

Two radiotherapy treatment experiments were performed where dose and position were measured with the RADPOS probe housed within a Solid Water phantom. For the first experiment, a Solid Water breast phantom was irradiated using isocentric beam delivery while stationary or moving sinusoidally in the anterior/posterior direction. For the second experiment, a phantom consisting of a Solid Water tumor in lung equivalent material was irradiated using isocentric and non-isocentric beam delivery while either stationary or moving. The phantom movement was either sinusoidal or based on a real patient's breathing waveform. For each experiment, RADPOS dose measurements were compared to EBT3 GafChromic film dose measurements and the CyberKnife treatment planning system's (TPS) Monte Carlo and ray-tracing dose calculation algorithms. RADPOS position measurements were compared to measurements made by the CyberKnife system and to the predicted breathing motion models used by the Synchrony respiratory motion compensation.

RESULTS

For the static and dynamic (i.e., sinusoidal motion) cases of the breast experiment, RADPOS, film and the TPS agreed at the 2.0% level within 1.1 σ of estimated combined uncertainties. RADPOS position measurements were in good agreement with LED and fiducial position measurements, where the average standard deviation (SD) of the differences between any two of the three position datasets was ≤0.5 mm for all directions. For the 10 mm peak to peak amplitude sinusoidal motion of the breast experiment, the average Synchrony correlation errors were ≤0.2 mm, indicative of an accurate predictive model. For all the cases of the lung experiment, RADPOS and film measurements agreed with each other at the 2.0% level within 1.5 σ of estimated experimental uncertainties provided that the measurements were corrected for imaging dose. The measured dose for RADPOS and film were 4.0% and 3.4% higher, respectively, than the TPS for the most complex dynamic cases (i.e., irregular motion) considered for the lung experiment. Assessment of the Synchrony correlation models by RADPOS showed that model accuracy declined as motion complexity increased; the SD of the differences between RADPOS and model position data measurements was ≤0.8 mm for sinusoidal motion but increased to ≤2.6 mm for irregular patient waveform motion. These results agreed with the Synchrony correlation errors reported by the CyberKnife system.

CONCLUSIONS

RADPOS is an accurate and precise QA tool for dose and position measurements for CyberKnife deliveries with respiratory motion compensation.

Evaluation of the accuracy of the CyberKnife Synchrony™ Respiratory Tracking System using a plastic scintillator

PURPOSE

The Synchrony™ Respiratory Tracking System of the CyberKnife® Robotic Radiosurgery System (Accuray, Inc., Sunnyvale, CA, USA) enables real-time tracking of moving targets such as lung and liver tumors during radiotherapy. Although film measurements have been used for quality assurance of the tracking system, they cannot evaluate the temporal tracking accuracy. We have developed a verification system using a plastic scintillator that can evaluate the temporal accuracy of the CyberKnife Synchrony.

METHODS

A phantom consisting of a U-shaped plastic frame with three fiducial markers was used. The phantom was moved on a plastic scintillator plate. To identify the phantom position on the recording video in darkness, four pieces of fluorescent tape representing the corners of a 10 cm × 10 cm square around an 8 cm × 8 cm window were attached to the phantom. For a stable respiration model, the phantom was moved with the fourth power of a sinusoidal wave with breathing cycles of 4, 3, and 2 s and an amplitude of 1 cm. To simulate irregular breathing, the respiratory cycle was varied with Gaussian random numbers. A virtual target was generated at the center of the fluorescent markers using the MultiPlan™ treatment planning system. Photon beams were irradiated using a fiducial tracking technique. In a dark room, the fluorescent light of the markers and the scintillation light of the beam position were recorded using a camera. For each video frame, a homography matrix was calculated from the four fluorescent marker positions, and the beam position derived from the scintillation light was corrected. To correct the displacement of the beam position due to oblique irradiation angles and other systematic measurement errors, offset values were derived from measurements with the phantom held stationary.

RESULTS

The average SDs of beam position measured without phantom motion were 0.16 and 0.20 mm for lateral and longitudinal directions, respectively. For the stable respiration model, the tracking errors (mean ± SD) were 0.40 ± 0.64 mm, -0.07 ± 0.79 mm, and 0.45 ± 1.14 mm for breathing cycles of 4, 3, and 2 s, respectively. The tracking errors showed significant linear correlation with the phantom velocity. The correlation coefficients were 0.897, 0.913, and 0.957 for breathing cycles of 4, 3, and 2 s, respectively. The unstable respiration model also showed linear correlation between tracking errors and phantom velocity. The probability of tracking error incidents increased with decreasing length of the respiratory cycles. Although the tracking error incidents increased with larger variations in respiratory cycle, the effect on the cumulative probability was insignificant. For a respiratory cycle of 4 s, the maximum tracking error was 1.10 and 1.43 mm at the probability of 10% and 5%, respectively. Large tracking errors were observed when there was phase shift between the tumor and the LED marker.

CONCLUSION

This technique allows evaluation of the motion tracking accuracy of the Synchrony™ system over time by measurement of the photon beam. The velocity of the target and phase shift have significant effects on accuracy.

Experimental verification of a two-dimensional respiratory motion compensation system with ultrasound tracking technique in radiation therapy

This study proposed respiratory motion compensation system (RMCS) combined with an ultrasound image tracking algorithm (UITA) to compensate for respiration-induced tumor motion during radiotherapy, and to address the problem of inaccurate radiation dose delivery caused by respiratory movement. This study used an ultrasound imaging system to monitor respiratory movements combined with the proposed UITA and RMCS for tracking and compensation of the respiratory motion. Respiratory motion compensation was performed using prerecorded human respiratory motion signals and also sinusoidal signals. A linear accelerator was used to deliver radiation doses to GAFchromic EBT3 dosimetry film, and the conformity index (CI), root-mean-square error, compensation rate (CR), and planning target volume (PTV) were used to evaluate the tracking and compensation performance of the proposed system. Human respiratory pattern signals were captured using the UITA and compensated by the RMCS, which yielded CR values of 34-78%. In addition, the maximum coronal area of the PTV ranged from 85.53 mm² to 351.11 mm² (uncompensated), which reduced to from 17.72 mm² to 66.17 mm² after compensation, with an area reduction ratio of up to 90%. In real-time monitoring of the respiration compensation state, the CI values for 85% and 90% isodose areas increased to 0.7 and 0.68, respectively. The proposed UITA and RMCS can reduce the movement of the tracked target relative to the LINAC in radiation therapy, thereby reducing the required size of the PTV margin and increasing the effect of the radiation dose received by the treatment target.

Long-term results of hypofractionated stereotactic radiotherapy with CyberKnife for growth hormone-secreting pituitary adenoma: evaluation by the Cortina consensus

The aim of the present study was to evaluate the safety and feasibility of hypofractionated stereotactic radiotherapy (SRT) with CyberKnife for growth hormone-secreting pituitary adenoma (GH-PA). Fifty-two patients with GH-PA were treated with hypofractionated SRT between September 2001 and October 2012. Eight patients had clinically silent GH-PA and 44 were symptomatic. Only 1 patient was inoperable. The other patients had recurrent or postoperative residual tumors on MRI. All patients had received pharmacotherapy prior to SRT with a somatostatin analog, dopamine agonist, and/or GH receptor antagonist. The marginal doses were 17.4-26.8 Gy for the 3-fraction schedule and 20.0-32.0 Gy for the 5-fraction schedule. Endocrinological remission was assessed by the Cortina consensus criteria 2010 (random GH <1 ng/ml or nadir GH after an oral glucose tolerance test <0.4 ng/ml and normalization of age- and sex-adjusted insulin-like growth factor-1). The median follow-up period was 60 months (range 27-137). The 5-year overall survival, local control, and disease-free survival rates were 100, 100, and 96 %, respectively. Nine patients (5 clinically silent and 4 symptomatic patients) satisfied the Cortina criteria without receiving further pharmacotherapy, whereas the remaining 43 patients did not. No post-SRT grade 2 or higher visual disorder occurred. Symptomatic post-SRT hypopituitarism was observed in 1 patient. CyberKnife hypofractionated SRT is safe and effective when judged by imaging findings for GH-PA. However, it may be difficult to satisfy the Cortina consensus criteria in most symptomatic patients with SRT alone. Further investigations of optimal treatments are warranted.

Evaluation of tracking accuracy of the CyberKnife system using a webcam and printed calibrated grid

Tracking accuracy for the CyberKnife's Synchrony system is commonly evaluated using a film‐based verification method. We have evaluated a verification system that uses a webcam and a printed calibrated grid to verify tracking accuracy over three different motion patterns. A box with an attached printed calibrated grid and four fiducial markers was attached to the motion phantom. A target marker was positioned at the grid's center. The box was set up using the other three markers. Target tracking accuracy was evaluated under three conditions: 1) stationary; 2) sinusoidal motion with different amplitudes of 5, 10, 15, and 20 mm for the same cycle of 4 s and different cycles of 2, 4, 6, and 8 s with the same amplitude of 15 mm; and 3) irregular breathing patterns in six human volunteers breathing normally. Infrared markers were placed on the volunteers’ abdomens, and their trajectories were used to simulate the target motion. All tests were performed with one‐dimensional motion in craniocaudal direction. The webcam captured the grid's motion and a laser beam was used to simulate the CyberKnife's beam. Tracking error was defined as the difference between the grid's center and the laser beam. With a stationary target, mean tracking error was measured at 0.4 mm. For sinusoidal motion, tracking error was less than 2 mm for any amplitude and breathing cycle. For the volunteers’ breathing patterns, the mean tracking error range was 0.78‐1.67 mm. Therefore, accurate lesion targeting requires individual quality assurance for each patient.

PACS number(s): 87.55.D‐, 87.55.km, 87.55.Qr, 87.56.Fc