Dynamic Tumor Tracking Irradiation Research Articles

Comparison of dynamic tumor tracking error measurement methods for robotic radiosurgery.

Dynamic tumor motion tracking is used in robotic radiosurgery for targets subject to respiratory motion, such as lung and liver cancers. Different methods of measuring tracking error have been reported, but the differences among these methods have not been studied, and the optimal method is unknown. The purpose of this study was to assess and compare tracking errors encountered with individual patients using different evaluation methods for method optimization. We compared the beam's eye view (BEV), machine learning (ML), log (addition error: AE), and log (root sum square: RSS) methods. Log (AE) and log (RSS) were calculated from log files. These tracking errors were compared, and the optimal evaluation method was ascertained. A t-test was performed to evaluate statistically significant differences. Here, the significance level was set at 5%. The mean values of BEV, log (AE), log (RSS), and ML were 2.87, 3.91, 2.91, and 3.74mm, respectively. The log (AE) and ML were higher than BEV (p<0.001), and log (RSS) was equivalent to the BEV, suggesting that the log (RSS) calculated with the log file method can substitute for the BEV calculated with the BEV method. As RSS error calculation is simpler than BEV calculation, using it may improve clinical practice throughput. This study clarified differences among three tracking error evaluation methods for dynamic tumor tracking radiotherapy using a robotic radiosurgery system. The log (RSS) calculated by the log file method was found to be the best alternative to BEV method, as it can calculate tracking errors more easily than the BEV method.

Prediction of lung tumor motion using nonlinear autoregressive model with exogenous input

The present note addresses the development of a lung tumor position predictor to be used in dynamic tumor tracking radiotherapy, abbreviated as DTT-RT. As there exists 50–500 ms positioning lag in the control of the multi-leaf collimator (MLC) of commercial medical linear accelerators, prediction of future lung tumor position with sufficiently long prediction horizon is inevitable for the successful implementation of DTT-RT. The present article proposes a lung tumor position predictor, which is classified as a nonlinear autoregressive model with exogenous input (NARX). The proposed predictor was trained using seven lung tumor motion trajectories of patients who underwent respiratory gated radiotherapy at Yamaguchi University Hospital. We considered three different prediction horizons, 600 ms, 800 ms and 1 s, which were sufficiently long to compensate for the possible positioning control lag of the MLC. A patient-specific model corresponding to an intended prediction horizon was obtained by training it using the selected tumor motion trajectory with the specified horizon. Accordingly, we obtained three NARX predictors for a single patient. We calculated two performance metrics: the RMS prediction errors and the rate of coverage of the entire tumor trajectory defined by the number of samples of the measured tumor position which was inside the 4 mm cube centered at the corresponding predicted tumor position. The latter quantifies the feasibility of the predictors to generate future gating cubes in the implementation of DTT-RT. The (mean standard deviation) values of the rates of 600 ms, 800 ms and 1 s prediction horizon calculated using the proposed NARX predictors were %, % and %, respectively.

Optimization of a newly defined target volume in fiducial marker-based dynamic tumor-tracking radiotherapy

Abstract Background and purpose To optimize the selection of fiducial markers used to determine the internal target volume for (ITV tracking ) for fiducial marker-based dynamic tumor-tracking (DTT) radiotherapy for lung tumors, with four-dimensional 320-row area-detector computed tomography (320-ADCT). Materials and methods Ten patients with early-stage non-small cell lung carcinomas or metastatic lung tumors were enrolled. After implanting 4 or 5 fiducial markers around the lung tumor, all patients underwent 320-ADCT to determine the coordinates of the tumors and markers. The coordinate origin of each phase CT dataset was translated so that the marker-derived coordinate centers coincided. The ITV tracking was then defined by compositing the gross tumor volumes from all four-dimensional CT phases. We compared the ITV tracking with the typical ITV used in motion-encompassing method. We also compared the following 5 scenarios in selection of fiducial markers to define the coordinate center: a ) the closest marker to the tumor, b ) the closest two makers, c ) the centroid of the all markers, d ) the farthest two markers, and e ) the farthest marker. Results The scenario b was the best in 5 patients. The difference of the ITV tracking between the best scenario and b was less than 20% in the other 5 patients. The scenario c was the best in 4 patients. The ITV tracking was smaller than the typical ITV. Conclusion ITV tracking was reasonable target in DTT radiotherapy. Proper ITV tracking might be created with choosing those midway between the two markers closest to the tumor or in the centroid of the markers.

Dosimetric evaluation of MLC-based dynamic tumor tracking radiotherapy using digital phantom: Desired setup margin for tracking radiotherapy

The purpose of this study is to evaluate the dosimetric impact of the margin on the multileaf collimator-based dynamic tumor tracking plan. Furthermore, an equivalent setup margin (EM) of the tracking plan was determined according to the gated plan. A 4-dimensional extended cardiac-torso was used to create 9 digital phantom datasets of different tumor diameters (TDs) of 1, 3, and 5 cm and motion ranges (MRs) of 1, 2, and 3 cm. For each dataset, respiratory gating (30% to 70% phase) and tumor tracking treatment plans were prepared using 8-field 3-dimensional conformal radiation therapy by 4-dimensional dose calculation. The total lung V20 was calculated to evaluate the dosimetric impact for each case and to estimate the EM with the same impact on lung V20 obtained with the gating plan with a setup margin of 5 mm. The EMs for {TD = 1 cm, MR = 1 cm}, {TD = 1 cm, MR = 2 cm}, and {TD = 1 cm, MR = 3 cm} were estimated as 5.00, 4.16, and 4.24 mm, respectively. The EMs for {TD = 5 cm, MR = 1 cm}, {TD = 5 cm, MR = 2 cm}, and {TD = 5 cm, MR = 3 cm} were estimated as 4.24 mm, 6.35 mm, and 7.49 mm, respectively. This result showed that with a larger MR, the EM was found to be increased. In addition, with a larger TD, the EM became smaller. Our result showing the EMs provided the desired accuracy for multileaf collimator-based dynamic tumor tracking radiotherapy.

Comparison of Target Delineation Methods in Dynamic Tumor Tracking Radiation Therapy for Lung Tumors Based on Internal Fiducial Markers

We previously defined a new target delineation method for dynamic tumor tracking (DTT) radiotherapy (RT) based on internal fiducial markers which were inserted near a tumor. An internal target volume for tracking (ITVtracking) is assumed to compensate for the positional variations between the tumor and fiducial makers and tumor deformation. In general, a three-dimension (3D) gross tumor volume (GTV) to planning target volume isotopic margin of 5 mm is widely used in DTT RT with a linear accelerator with a robotic arm. However, thus far, there is no theoretical or quantitative evaluation and comparison between the “ITVtracking method” and the “isotropic margin method.” The purpose of this study was to compare between these two methods. We evaluated the four-dimensional (4D) computed tomography (CT) images of patients who received DTT RT at our institution. Ten phases of 4DCT were acquired with a 16-row multidetector CT during free breathing. The GTV in each phase was delineated semiautomatically with manual modifications. ITVtracking was created as a composite of the GTVs in 10-phase images of 4DCT sets, which were transposed to the centroid of their markers coincided at a reference CT phase. The reference CT phase was selected based on image quality and used for dose calculation. ITVs with the isotropic margin method (ITVisos) were created by adding a 5-mm margin to the GTV at the same reference CT phase. We compared the values of ITVtracking and ITViso through statistical evaluation with a paired t-test, and we evaluated their overlapping volume and Dice’s similarity coefficient (DSC). Moreover, we determined the indispensable margin for the GTV at the reference CT phase to encompass the entire ITVtracking. Between September 2011 and December 2016, 26 4DCT sets in 28 patients were evaluated. Two CT sets could not be evaluated because the 4DCT images were not reconstructed. The median tumor diameter was 21 mm (range, 10–44 mm). The end-exhale phase was used as the reference CT phase in 17 patients, and the mid-ventilation phase was used in 9 patients. The median values of ITViso and ITVtracking were 18.5 and 12.4 mL, respectively (range, 5.1–70.3 and 1.8–64.8 mL, p < 0.01). The ITViso included the entire ITVtracking in nine patients. The average rate of non-inclusion of ITVtracking in ITViso was 5.7% (range, 0.1–31.8%) in the other 17 patients. The median DSC was 0.73 (range, 0.46–0.89). The median margin for the GTV to encompass the entire ITVtracking was 6.5 mm (range, 2–15 mm). ITViso was statistically larger than ITVtracking, but ITViso did not always include ITVtracking. The ITVtracking method enables a reasonable reduction of the target size.

Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor-tracking radiotherapy.

To explore the effect of sampling interval of training data acquisition on the intrafractional prediction error of surrogate signal-based dynamic tumor-tracking using a gimbal-mounted linac. Twenty pairs of respiratory motions were acquired from 20 patients (ten lung, five liver, and five pancreatic cancer patients) who underwent dynamic tumor-tracking with the Vero4DRT. First, respiratory motions were acquired as training data for an initial construction of the prediction model before the irradiation. Next, additional respiratory motions were acquired for an update of the prediction model due to the change of the respiratory pattern during the irradiation. The time elapsed prior to the second acquisition of the respiratory motion was 12.6 ± 3.1 min. A four-axis moving phantom reproduced patients' three dimensional (3D) target motions and one dimensional surrogate motions. To predict the future internal target motion from the external surrogate motion, prediction models were constructed by minimizing residual prediction errors for training data acquired at 80 and 320 ms sampling intervals for 20 s, and at 500, 1,000, and 2,000 ms sampling intervals for 60 s using orthogonal kV x-ray imaging systems. The accuracies of prediction models trained with various sampling intervals were estimated based on training data with each sampling interval during the training process. The intrafractional prediction errors for various prediction models were then calculated on intrafractional monitoring images taken for 30 s at the constant sampling interval of a 500 ms fairly to evaluate the prediction accuracy for the same motion pattern. In addition, the first respiratory motion was used for the training and the second respiratory motion was used for the evaluation of the intrafractional prediction errors for the changed respiratory motion to evaluate the robustness of the prediction models. The training error of the prediction model was 1.7 ± 0.7 mm in 3D for all sampling intervals. The intrafractional prediction error for the same motion pattern was 1.9 ± 0.7 mm in 3D for an 80 ms sampling interval, which increased larger than 1 mm in 10.0% of prediction models trained at a 2,000 ms sampling interval with a significant difference (P < 0.01) and up to 2.5% for the other sampling intervals without a significant difference (P > 0.05). The intrafractional prediction error for the changed respiratory motion pattern increased to 5.1 ± 2.4 mm in 3D for an 80 ms sampling interval; however, there was not a significant difference in the robustness of the prediction model between the 80 ms sampling interval and other sampling intervals (P > 0.05). Although the training error of the prediction model was consistent for the all sampling intervals, the prediction model using the larger sampling interval of the 2,000 ms increased the intrafractional prediction error for the same motion pattern. The realistic accuracy of the prediction model was difficult to estimate using the larger sampling interval during the training process. It is recommended to construct the prediction model at sampling interval ≤ 1,000 ms and to reconstruct the model during treatment.

Investigation of Well-Balanced kV X-Ray Imaging Conditions between Skin Dose and Image Noise for Dynamic Tumor Tracking Irradiation

Purpose: The purposes of this study were to estimate accumulated kV X-ray imaging dose throughout dynamic tumor tracking (DTT) irradiation by Vero 4DRT system and to address an analytical skin dose formula for well-balanced kV X-ray imaging conditions between skin dose and image noise. Method: First, skin dose was measured using kV X-ray tube, chamber, and water-equivalent phantoms. Next, imaging dose for six patients in DTT treatment was computed using log files. Subsequently, scattered dose ratio was calculated by amount of ionization in front of flat panel detector (FPD) for fields with size of maximum and the chamber for 0 - 200 mm-thickness phantoms and tube voltage of 60, 80, 100, 120 kV, respectively. Furthermore, image noise was computed from FPD images. Results: The skin dose was greater by a factor of 1.4 - 1.6 than those in Synergy XVI system. The image noise in FPD, was expressed as N = 0.045×(1/QFPDen)0.479, where QFPDen denotes amount of ionization in front of FPD. Then, skin dose, D (N, t, v) was formulated as (0.045/N)(1/0.479)/QFPDen/mAs (t, v) ×D/mAs (v), where QFPDen/mAs (t, v) and D/mAs (v) denote amount of ionization in front of FPD and skin dose per mAs, respectively. Using the formulae, it has been demonstrated that skin dose with 120 kV has become lower than any other tube voltage in this study. Conclusion: Using skin doses for the phantom, the skin dose throughout DTT irradiation was estimated as 0.50 Gy. Furthermore, skin dose by kV X-ray imaging was described as a function of image noise, phantom thickness, and tube voltage, suggesting image noise may be reduced with higher X-ray tube voltage in this phantom study.

Dynamic tumor-tracking radiotherapy with real-time monitoring for liver tumors using a gimbal mounted linac

PurposeDynamic tumor-tracking stereotactic body radiotherapy (DTT-SBRT) for liver tumors with real-time monitoring was carried out using a gimbal-mounted linear accelerator and the efficacy of the system was determined. In addition, four-dimensional (4D) dose distribution, tumor-tracking accuracy, and tumor-marker positional variations were evaluated. Materials and methodsA fiducial marker was implanted near the tumor prior to treatment planning. The prescription dose at the isocenter was 48–60Gy, delivered in four or eight fractions. The 4D dose distributions were calculated with a Monte Carlo method and compared to the static SBRT plan. The intrafractional errors between the predicted target positions and the actual target positions were calculated. ResultsEleven lesions from ten patients were treated successfully. DTT-SBRT allowed an average 16% reduction in the mean liver dose compared to static SBRT, without altering the target dose. The average 95th percentiles of the intrafractional prediction errors were 1.1, 2.3, and 1.7mm in the left–right, cranio-caudal, and anterior–posterior directions, respectively. After a median follow-up of 11months, the local control rate was 90%. ConclusionsOur early experience demonstrated the dose reductions in normal tissues and high accuracy in tumor tracking, with good local control using DTT-SBRT with real-time monitoring in the treatment of liver tumors.

Baseline correction of a correlation model for improving the prediction accuracy of infrared marker-based dynamic tumor tracking.

We previously found that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker‐based dynamic tumor tracking irradiation (IR Tracking) using the Vero4DRT system. Here, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. To perform IR Tracking, a four‐dimensional (4D) model was constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the model was expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, F1st(x,v), was adjusted by the baseline drift of IR markers (BDIR) along the x‐axis, as function F′(x,v). Next, BDdetect, that defined as the difference between the target positions indicated by the implanted fiducial markers (Pdetect) and the predicted target positions with F′(x,v) (Ppredict) was determined using orthogonal kV X‐ray images at the peaks of the Pdetect of the end‐inhale and end‐exhale phases for 10 s just before irradiation. F′(x,v) was corrected with BDdetect to compensate for the residual error. The final corrected 4D model was expressed as Fcor(x,v)=F1st{(x−BDIR),v}−BDdetect. We retrospectively applied this function to 53 paired log files of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between Pdetect and Ppredict (|Ep|) was compared between F1st(x,v) and Fcor(x,v). The median 95th percentile of |Ep| (units: mm) was 1.0, 1.7, and 3.5 for F1st(x,v), and 0.6, 1.1, and 2.1 for Fcor(x,v) in the left–right, anterior–posterior, and superior–inferior directions, respectively. Over all treatment sessions, the 95th percentile of |Ep| peaked at 3.2 mm using Fcor(x,v) compared with 8.4 mm using F1st(x,v). Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation.PACS number: 87.19.rs, 87.19.Wx, 87.56.‐v, 87.59.‐e, 88.10.gc

Influence of the correlation modeling period on the prediction accuracy of infrared marker-based dynamic tumor tracking using a gimbaled X-ray head

PurposeTo assess the utility of 10 s and 20 s modeling periods, rather than the 40 s currently used, in the clinical construction of practical correlation models (CMs) in dynamic tumor tracking irradiation using the Vero4DRT. MethodsThe CMs with five independent parameters (CM parameters) were analyzed retrospectively for 10 consecutive lung cancer patients. CM remodeling was performed two or three times per treatment session. Three different CMs trained over modeling periods of 10, 20, and 40 s were built from a single, original CM log file. The predicted target positions were calculated from the CM parameters and the vertical displacement of infrared markers on the abdomen (PIR) during the modeling. We assessed how the CM parameters obtained over modeling periods of T s (T = 10, 20, and 40 s) were robust to changes in respiratory patterns after several minutes. The mimic-predicted target positions after several minutes were computed based on the previous CM parameters and PIR during the next modeling. The 95th percentiles of the differences between mimic-predicted and detected target positions over 40 s (E95robust,T: T = 10, 20, and 40 s) were then calculated. ResultsStrong correlations greater than 0.92 were observed between the E95robust,20 and E95robust,40 values. Meanwhile, irregular respiratory patterns with inconsistent amplitudes of motion created differences between the E95robust,10 and E95robust,40 values of ≥10 mm. ConclusionsThe accuracies of CMs derived using 20 s were almost identical to those obtained over 40 s, and superior to those obtained over 10 s.

Comparison of 4-Dimensional Doses and 3-Dimensional Doses in Dynamic Tumor Tracking Irradiation for Lung Tumor Using a Gimbaled X-Ray Head

To evaluate the difference between the dose-volume metrics calculated with four-dimensional (4D) Monte Carlo (MC) dose evaluation system and those calculated on breath-hold exhale CT alone in dynamic tumor tracking (DTT) irradiation for lung tumors.

Baseline Correction of a Correlation Model in Infrared Marker-Based Dynamic Tumor Tracking With A Gimbaled Linac

Our previous study demonstrated that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker-based dynamic tumor tracking irradiation (IR Tracking) with a gimbaled Linac. In this study, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking.

Evaluation of Dose Distribution and Tracking Accuracy in Dynamic Tumor-Tracking Irradiation for Liver Tumors Using a Gimbaled Linac

The purpose of this study is to evaluate the feasibility of dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT) for liver tumors with real-time monitoring using a gimbaled Linac by calculating four-dimensional (4D) dose distribution and evaluating the tracking accuracy.

Dosimetric impact of gold markers implanted closely to lung tumors: a Monte Carlo simulation.

We are developing an innovative dynamic tumor tracking irradiation technique using gold markers implanted around a tumor as a surrogate signal, a real‐time marker detection system, and a gimbaled X‐ray head in the Vero4DRT. The gold markers implanted in a normal organ will produce uncertainty in the dose calculation during treatment planning because the photon mass attenuation coefficient of a gold marker is much larger than that of normal tissue. The purpose of this study was to simulate the dose variation near the gold markers in a lung irradiated by a photon beam using the Monte Carlo method. First, the single‐beam and the opposing‐beam geometries were simulated using both water and lung phantoms. Subsequently, the relative dose profiles were calculated using a stereotactic body radiotherapy (SBRT) treatment plan for a lung cancer patient having gold markers along the anteriorposterior (AP) and right‐left (RL) directions. For the single beam, the dose at the gold marker‐phantom interface laterally along the perpendicular to the beam axis increased by a factor of 1.35 in the water phantom and 1.58 in the lung phantom, respectively. Furthermore, the entrance dose at the interface along the beam axis increased by a factor of 1.63 in the water phantom and 1.91 in the lung phantom, while the exit dose increased by a factor of 1.00 in the water phantom and 1.12 in the lung phantom, respectively. On the other hand, both dose escalations and dose de‐escalations were canceled by each beam for opposing portal beams with the same beam weight. For SBRT patient data, the dose at the gold marker edge located in the tumor increased by a factor of 1.30 in both AP and RL directions. In clinical cases, dose escalations were observed at the small area where the distance between a gold marker and the lung tumor was ≤ 5 mm, and it would be clinically negligible in multibeam treatments, although further investigation may be required.PACS number: 87.10.Rt

PO-0867: Patient-specific quality assurance for intensity-modulated dynamic tumor-tracking radiotherapy with Vero4DRT

PO-0867: Patient-specific quality assurance for intensity-modulated dynamic tumor-tracking radiotherapy with Vero4DRT

OC-0276: Development of 4D Monte Carlo dose calculation system for intensity modulated dynamic tumor-tracking radiotherapy

OC-0276: Development of 4D Monte Carlo dose calculation system for intensity modulated dynamic tumor-tracking radiotherapy

Predictive uncertainty in infrared marker‐based dynamic tumor tracking with Vero4DRTa)

To quantify the predictive uncertainty in infrared (IR)-marker-based dynamic tumor tracking irradiation (IR Tracking) with Vero4DRT (MHI-TM2000) for lung cancer using logfiles. A total of 110 logfiles for 10 patients with lung cancer who underwent IR Tracking were analyzed. Before beam delivery, external IR markers and implanted gold markers were monitored for 40 s with the IR camera every 16.7 ms and with an orthogonal kV x-ray imaging subsystem every 80 or 160 ms. A predictive model [four-dimensional (4D) model] was then created to correlate the positions of the IR markers (PIR) with the three-dimensional (3D) positions of the tumor indicated by the implanted gold markers (Pdetect). The sequence of these processes was defined as 4D modeling. During beam delivery, the 4D model predicted the future 3D target positions (Ppredict) from the PIR in real-time, and the gimbaled x-ray head then tracked the target continuously. In clinical practice, the authors updated the 4D model at least once during each treatment session to improve its predictive accuracy. This study evaluated the predictive errors in 4D modeling (E4DM) and those resulting from the baseline drift of PIR and Pdetect during a treatment session (EBD). E4DM was defined as the difference between Ppredict and Pdetect in 4D modeling, and EBD was defined as the mean difference between Ppredict calculated from PIR in updated 4D modeling using (a) a 4D model created from training data before the model update and (b) an updated 4D model created from new training data. The mean E4DM was 0.0 mm with the exception of one logfile. Standard deviations of E4DM ranged from 0.1 to 1.0, 0.1 to 1.6, and 0.2 to 1.3 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The median elapsed time before updating the 4D model was 13 (range, 2-33) min, and the median frequency of 4D modeling was twice (range, 2-3 times) per treatment session. EBD ranged from -1.0 to 1.0, -2.1 to 3.3, and -2.0 to 3.5 mm in the LR, AP, and SI directions, respectively. EBD was highly correlated with BDdetect in the LR (R = -0.83) and AP directions (R = -0.88), but not in the SI direction (R = -0.40). Meanwhile, EBD was highly correlated with BDIR in the SI direction (R = -0.67), but not in the LR (R = 0.15) or AP (R = -0.11) direction. If the 4D model was not updated in the presence of intrafractional baseline drift, the predicted target position deviated from the detected target position systematically. Application of IR Tracking substantially reduced the geometric error caused by respiratory motion; however, an intrafractional error due to baseline drift of >3 mm was occasionally observed. To compensate for EBD, the authors recommend checking the target and IR marker positions constantly and updating the 4D model several times during a treatment session.

SU‐E‐J‐220: Respiration Simulating Phantom for Verifying Four‐Dimensional Radiation Therapy

Purpose: To develop the respiration simulating phantom moves real organ motion for verifying 4D radiotherapy such as gated radiotherapy, breathing control radiotherapy and dynamic tumor tracking radiotherapy. Methods: Conventional 4D verifying phantoms move periodically and regularly, unlike the real patients' organs. The phantom developed in this study to simulate patients' organ motion. The controller of moving phantom monitors and records the patients' respiratory waves measured by various modalities, and moves the phantom according to the prerecorded respiratory signal. The organ and the phantom motion were compared with its respiratory curves to evaluate the simulating phantom. Results: Comparing with the respiration signals and the phantom motion, the correlation coefficient between the curves were acceptable relative to the organ motion. The discrepancy between the respiratory curve and the organ motion was 8.52% of motion rangeConclusion: The phantom simulated the organ motion according to the respiratory signal. The relationship between the signal of respiration and the phantom motion showed strong correlation. It is expected that the record and playable simulating phantom is useful to verify the 4D radiotherapy. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education, Science and Technology (2010‐0013701).

Accuracy verification of infrared marker‐based dynamic tumor‐tracking irradiation using the gimbaled x‐ray head of the Vero4DRT (MHI‐TM2000)a)

To verify the accuracy of an infrared (IR) marker-based dynamic tumor-tracking irradiation system (IR tracking) using the gimbaled x-ray head of the Vero4DRT (MHI-TM2000). The gimbaled 6-MV C-band x-ray head of the Vero4DRT can swing along the pan-and-tilt direction to track a moving target. During beam delivery, the Vero4DRT predicts the future three-dimensional (3D) target position in real time using a correlation model [four-dimensional (4D) model] between the target and IR marker motion, and then continuously transfers the corresponding tracking orientation to the gimbaled x-ray head. The 4D-modeling error (E4DM) and the positional tracking error (EP) were defined as the difference between the predicted and measured positions of the target in 4D modeling and as the difference between the tracked and measured positions of the target during irradiation, respectively. For the clinical application of IR tracking, we assessed the relationship between E4DM and EP for three 1D sinusoidal (peak-to-peak amplitude [A]: 20-40 mm, breathing period [T]: 2-4 s), five 1D phase-shifted sinusoidal (A: 20 mm, T: 4 s, phase shift [τ]: 0.2-2 s), and six 3D patient respiratory patterns. The difference between the 95th percentile of the absolute EP (EP (95)) and the mean (μ) + two standard deviations (SD) of absolute E4DM (E4DM (μ+2SD)) was within ± 1 mm for all motion patterns. As the absolute correlation between the target and IR marker motions decreased from 1.0 to 0.1 for the 1D phase-shifted sinusoidal patterns, the E4DM (μ+2SD) and EP (95) increased linearly, from 0.4 to 3.0 mm (R = -0.98) and from 0.5 to 2.2 mm (R = -0.95), respectively. There was a strong positive correlation between E4DM (μ+2SD) and EP (95) in each direction [(lateral, craniocaudal, anteroposterior) = (0.99, 0.98, 1.00)], even for the 3D respiratory patterns; thus, EP (95) was readily estimated from E4DM (μ+2SD). Positional tracking errors correlated strongly with 4D-modeling errors in IR tracking. Thus, the accuracy of the 4D model must be verified before treatment, and margins are required to compensate for the 4D-modeling error.

Intrafractional Tracking Accuracy in Surrogate Signal-based Dynamic Tumor Tracking Irradiation With 4DRT

We have developed an innovative four-dimensional (4D) image-guided radiation therapy system, and clinically applied its dynamic tumor-tracking (DTT) irradiation to patients with lung cancer since September 2011. 4DRT swings its gimbaled 6 MV x-ray head along the pan and tilt direction to track a moving tumor. The purpose of this study was to verify the intra-fractional tracking accuracy in DTT irradiation with 4DRT. Four lung cancer patients who had undergone the DTT irradiation were included in this study. The peak-to-peak amplitudes of lung tumor motions were in range of 0.6 to 5.5 mm in left-right (LR), of 10.2 to 26.0 mm in cranio-caudal (CC), and of 2.0 to 5.4 mm in anterior-posterior (AP) direction. Patient-specific tracking margins of 5 to 8 mm were anisotoripically added to compensate the uncertainties of DTT irradiation. 4DRT dynamically tracks a moving tumor by predicting its future position with a correlation model (4D-model) between external surrogate signals of the abdominal wall and tumor positions indicated by implanted fiducial gold markers. During beam delivery, tumor and fiducial markers' position were monitored by two orthogonal kV x-ray imaging subsystem every 1 s. Meanwhile, the predicted and tracked positions of the tumor were recorded on the log files every 5 ms. Intra-fractional tracking accuracy was verified by the detected tumor position on the fluoroscopy images and the corresponding predicted and tracked tumor positions calculated from the synchronized log files. The tracked tumor position was derived from the intersection of the tracking orientation of the gimbaled head with the tracked tumor plane. The tracked tumor plane was the perpendicular plane to the gantry angle of each port at the depth of the tracked tumor. The difference between the predicted and detected tumor positions was defined as the prediction error (EP), and the difference between the tracked and predicted tumor positions as mechanical error (EM), and the difference between the tracked and detected tumor positions as the total tracking system error (ET). Averaged 99th percentiles [range] of the EM, EP, and ET were (LR, CC, AP) = (0.2 mm [0.1-0.4 mm], 0.7 mm [0.5-1.2 mm], 0.2 mm [0.1-0.3 mm]), (1.3 mm [0.7-2.7 mm], 3.9 mm [2.9-5.5 mm], 1.6 mm [0.9-2.0 mm]), and (1.6 mm [1.1-2.8 mm], 3.4 mm [2.5-4.9 mm], 1.3 mm [1.0-1.5 mm]), respectively. The intrafractional tracking errors were fully ensured by the tracking margin. We demonstrated that DTT irradiation with 4DRT can substantially reduce the impacts of respiratory motion with high accuracy. The prediction error mainly caused the total tracking system error while the mechanical error was negligible.