Variation in target volume and centroid position due to breath holding during four-dimensional computed tomography scanning: A phantom study.

This study investigated the effects of respiratory motion, including unwanted breath holding, on the target volume and centroid position on four-dimensional computed tomography (4DCT) imaging. Cine 4DCT images were reconstructed based on a time-based sorting algorithm, and helical 4DCT images were reconstructed based on both the time-based sorting algorithm and an amplitude-based sorting algorithm. A spherical object 20 mm in diameter was moved according to several simulated respiratory motions, with a motion period of 4.0 s and maximum amplitude of 5 mm. The object was extracted automatically, and the target volume and centroid position in the craniocaudal direction were measured using a treatment planning system. When the respiratory motion included unwanted breath-holding times shorter than the breathing cycle, the root mean square errors (RSME) between the reference and imaged target volumes were 18.8%, 14.0%, and 5.5% in time-based images in cine mode, time-based images in helical mode, and amplitude-based images in helical mode, respectively. In helical mode, the RSME between the reference and imaged centroid position was reduced from 1.42 to 0.50 mm by changing the reconstruction method from time- to amplitude-based sorting. When the respiratory motion included unwanted breath-holding times equal to the breathing cycle, the RSME between the reference and imaged target volumes were 19.1%, 24.3%, and 15.6% in time-based images in cine mode, time-based images in helical mode, and amplitude-based images in helical mode, respectively. In helical mode, the RSME between the reference and imaged centroid position was reduced from 1.61 to 0.83 mm by changing the reconstruction method from time- to amplitude-based sorting. With respiratory motion including breath holding of shorter duration than the breathing cycle, the accuracies of the target volume and centroid position were improved by amplitude-based sorting, particularly in helical 4DCT.

Long-term stability assessment of a 4D tumor tracking system integrated into a gimbaled linear accelerator.

We assessed long-term stability of tracking accuracy using the Vero4DRT system. This metric was observed between September 2012 and March 2015. A programmable respiratory motion phantom, designed to move phantoms synchronously with respiratory surrogates, was used. The infrared (IR) markers moved in the anterior-posterior (AP) direction as respiratory surrogates, while a cube phantom with a steel ball at the center, representing the tumor, and with radiopaque markers around it moved in the superior-inferior (SI) direction with one-dimensional (1D) sinusoidal patterns. A correlation model between the tumor and IR marker motion (4D model) was created from the training data obtained for 20 s just before beam delivery. The irradiation field was set to 3 × 3 cm2 and 300 monitor units (MUs) of desired MV X-ray beam were delivered. The gantry and ring angles were set to 0° and 45°, respectively. During beam delivery, the system recorded approximately 60 electronic portal imaging device (EPID) images. We analyzed: 1) the predictive accuracy of the 4D model (EP), defined as the difference between the detected and predicted target positions during 4D model creation, and 2) the tracking accuracy (ET), defined as the difference between the center of the steel ball and the MV X-ray field on the EPID image. The median values of mean plus two standard deviations (SDs) for EP were 0.06, 0.35, and 0.06 mm in the left-right (LR), SI, and AP directions, respectively. The mean values of maximum deviation for ET were 0.38, 0.49, and 0.53 mm and the coefficients of variance (CV) were 0.16, 0.10, and 0.05 in lateral, longitudinal, and 2D directions, respectively. Consequently, the IR Tracking accuracy was consistent over a period of two years. Our proposed method assessed the overall tracking accuracy readily using real-time EPID images, and proved to be a useful QA tool for dynamic tumor tracking with the Vero4DRT system.

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.

Interfraction positional variation in pancreatic tumors using daily breath-hold cone-beam computed tomography with visual feedback.

We assessed interfraction positional variation in pancreatic tumors using daily breath-hold cone-beam computed tomography at end-exhalation (EE) with visual feedback (BH-CBCT). Eleven consecutive patients with pancreatic cancer who underwent BH intensity-modulated radiation therapy with visual feedback were enrolled. All participating patients stopped oral intake, with the exception of drugs and water, for > 3 hr before treatment planning and daily treatment. Each patient was fixed in the supine position on an individualized vacuum pillow. An isotropic margin of 5 mm was added to the clinical target volume to create the planning target volume (PTV). The prescription dose was 42 to 51 Gy in 15 fractions. After correcting initial setup errors based on bony anatomy, the first BH-CBCT scans were performed before beam delivery in every fraction. BH-CBCT acquisition was obtained in three or four times breath holds by interrupting the acquisition two or three times, depending on the patient's BH ability. The image acquisition time for a 360° gantry rotation was approximately 90 s, including the interruption time due to BH. The initial setup errors were corrected based on bony structure, and the residual errors in the target position were then recorded. The magnitude of the interfraction variation in target position was assessed for 165 fractions. The systematic and random errors were 1.2 and 1.8 mm, 1.1 and 1.8 mm, and 1.7 and 2.9 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. Absolute interfraction variations of > 5 mm were observed in 18 fractions (11.0%) from seven patients because of EE-BH failure. In conclusion, target matching is required to correct interfraction variation even with visual feedback, especially to ensure safe delivery of escalated doses to patients with pancreatic cancer.

Optimization of the x-ray monitoring angle for creating a correlation model between internal and external respiratory signals.

PURPOSE: To perform dynamic tumor tracking irradiation with the Vero4DRT (MHI-TM2000), a correlation model [four dimensional (4D) model] between the displacement of infrared markers on the abdominal wall and the three-dimensional position of a tumor indicated by a minimum of three implanted gold markers is required. However, the gold markers cannot be detected successfully on fluoroscopic images under the following situations: (1) overlapping of the gold markers; and (2) a low intensity ratio of the gold marker to its surroundings. In the present study, the authors proposed a method to readily determine the optimal x-ray monitoring angle for creating a 4D model utilizing computed tomography (CT) images. METHODS: The Vero4DRT mounting two orthogonal kV x-ray imaging subsystems can separately rotate the gantry along an O-shaped guide-lane and the O-ring along its vertical axis. The optimal x-ray monitoring angle was determined on CT images by minimizing the root-sum-square of water equivalent path lengths (WEPLs) on the orthogonal lines passing all of the gold markers while rotating the O-ring and the gantry. The x-ray monitoring angles at which the distances between the gold markers were within 5 mm at the isocenter level were excluded to prevent false detection of the gold markers in consideration of respiratory motions. First, the relationship between the WEPLs (unit: mm) and the intensity ratios of the gold markers was examined to assess the validity of our proposed method. Second, our proposed method was applied to the 4D-CT images at the end-expiration phase for 11 lung cancer patients who had four to five gold markers. To prove the necessity of the x-ray monitoring angle optimization, the intensity ratios of the least visible markers (minimum intensity ratios) that were estimated from the WEPLs were compared under the following conditions: the optimal x-ray monitoring angle and the angles used for setup verification. Additionally, the intra- and interfractional variations in the intensity ratio were examined from the optimal x-ray monitoring angle. RESULTS: A negative strong correlation was observed between the WEPL (x) and the intensity ratio (y) (y = 6.57 exp[-0.0125x] + 1, R = -0.88 [95% confidence interval: -0.85 to -0.90], p < 0.01). Our proposed method effectively avoided having the x-ray beam pass through high-density structures, although there were large interpatient variations in the optimal x-ray monitoring angle because of the geometric arrangement between the gold markers and the anatomical structures. The minimum intensity ratios that were estimated from the WEPLs at the optimal x-ray monitoring angle ranged from 1.43 to 2.48, which was an average of 1.27 times (range, 1.02-1.66) higher than the angles used for setup verification. The maximum intra- and interfractional decreases in the intensity ratio were 0.23 and 0.17, respectively. CONCLUSIONS: The authors demonstrated that the optimal x-ray monitoring angle for creating a 4D model can improve the visibility of gold markers.

Development of a portable quality control application using a tablet-type electronic device.

PURPOSE: Our aim was to develop a portable quality control (QC) application using a thermometer, a barometer, an angle gauge, and a range finder implemented in a tablet-type consumer electronic device (CED) and to assess the accuracies of the measurements made. METHODS: The QC application was programmed using Java and OpenCV libraries. First, temperature and atmospheric pressure were measured over 30 days using the temperature and pressure sensors of the CED and compared with those measured by a double-tube thermometer and a digital barometer. Second, the angle gauge was developed using the accelerometer of the CED. The roll and pitch angles of the CED were measured from 0 to 90° at intervals of 10° in the clockwise (CW) and counterclockwise (CCW) directions. The values were compared with those measured by a digital angle gauge. Third, a range finder was developed using the tablet's built-in camera and image-processing capacities. Surrogate markers were detected by the camera and their positions converted to actual positions using a homographic transformation method. Fiducial markers were placed on a treatment couch and moved 100 mm in 10-mm steps in both the lateral and longitudinal directions. The values were compared with those measured by the digital output of the treatment couch. The differences between CED values and those of other devices were compared by calculating means ± standard deviations (SDs). RESULTS: The means ± SDs of differences in temperature and atmospheric pressure were -0.07 ± 0.25°C and 0.05 ± 0.10 hPa, respectively. The means ± SDs of the difference in angle was -0.17 ± 0.87° (0.15 ± 0.23° degrees excluding the 90° angle). The means ± SDs of distances were 0.01 ± 0.07 mm in both the lateral and longitudinal directions. CONCLUSIONS: Our portable QC application was accurate and may be used instead of standard measuring devices. Our portable CED is efficient and simple when used in the field of medical physics.

Geometric and dosimetric accuracy of dynamic tumor tracking during volumetric-modulated arc therapy using a gimbal mounted linac.

PURPOSE: The aim was to examine the feasibility of a dynamic tumor-tracking volumetric modulated arc therapy (DTT-VMAT) technique using a gimbal-mounted linac and assess its positional, mechanical and dosimetric accuracy. MATERIALS AND METHODS: DTT-VMAT was performed using a surrogated signal-based technique. The positional tracking accuracy was evaluated as the difference between the predicted and detected target positions for various wave patterns. Mechanical accuracy measurements included gantry, multileaf collimator (MLC) and gimbal positions. The differences between the command and the measured positions were evaluated for various wave patterns. Dosimetric verification was performed using Gafchromic EBT3 films in the benchmark phantom and two clinical cases. RESULTS: The root mean square error (RMSE) of the positional accuracy was within 0.31 mm. The RMSE of mechanical accuracy was within 0.14° for the gantry, 0.11 ±â€¯0.02 mm for the MLC and 0.13 mm for the gimbal positions. The passing rate of the 3%/3 mm gamma index was greater than 83.3% and 91.2% for the benchmark phantom and two clinical cases, respectively. CONCLUSIONS: The positional, mechanical and dosimetric accuracy of DTT-VMAT were evaluated. DTT-VMAT with a gimbal-mounted linac had sufficient accuracy and presents a new strategy for treatment of several tumors with respiratory motion.

Evaluation of Dynamic Tumor-tracking Intensity-modulated Radiotherapy for Locally Advanced Pancreatic Cancer.

Intensity-modulated radiotherapy (IMRT) is now regarded as an important treatment option for patients with locally advanced pancreatic cancer (LAPC). To reduce the underlying tumor motions and dosimetric errors during IMRT as well as the burden of respiratory management for patients, we started to apply a new treatment platform of the dynamic tumor dynamic tumor-tracking intensity-modulated radiotherapy (DTT-IMRT) using the gimbaled linac, which can swing IMRT toward the real-time tumor position under patients' voluntary breathing. Between June 2013 and March 2015, ten patients were treated, and the tumor-tracking accuracy and the practical benefits were evaluated. The mean PTV size in DTT-IMRT was 18% smaller than a conventional ITV-based PTV. The root-mean-squared errors between the predicted and the detected tumor positions were 1.3, 1.2, and 1.5 mm in left-right, anterior-posterior, and cranio-caudal directions, respectively. The mean in-room time was 24.5 min. This high-accuracy of tumor-tracking with reasonable treatment time are promising and beneficial to patients with LAPC.

Clinical results of dynamic tumor tracking intensity-modulated radiotherapy with real-time monitoring for pancreatic cancers using a gimbal mounted linac.

OBJECTIVES: We performed dynamic tumor-tracking IMRT (DTT-IMRT) in locally advanced pancreatic cancer (LAPC) patients using a gimbaled linac of Vero4DRT. The purpose of this study is to report the first clinical results. METHODS: From June 2013 to June 2015, eleven LAPC patients enrolled in this study and DTT-IMRT was successfully performed. The locoregional progression free survival (LRPFS), distant metastasis free survival (DMFS), overall survival (OS), hematologic and gastrointestinal (GI) toxicities were evaluated. Oncologic outcomes were estimated using Kaplan-Meier analysis, and toxicities using CTCAE v4.0. RESULTS: The median radiation dose was 48 Gy (range, 45-51) in 15 fractions. Concurrent chemoradiotherapy (CCRT) was performed using gemcitabine in 9 patients and S-1 in one, while one patient refused. With a median follow-up of 22.9 months, 1-year LRPFS, DMFS, and OS rates were 90.9%, 70.7%, and 100%, respectively. Median survival time was 23.6 months. Grade-3 leucopenia and neutropenia were observed in two (18%) and one patient (9%), respectively. Grade-2 acute GI toxicity occurred in 2 patients (18%) and late grade-3 in 1 patient (9%). CONCLUSIONS: Preliminarily application of DTT-IMRT using a gimbaled linac on CCRT in LAPC patients resulted in excellent locoregional control and OS without severe toxicity.

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

PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Inter- and Intrafractional Variation in the 3-Dimensional Positions of Pancreatic Tumors Due to Respiration Under Real-Time Monitoring.

PURPOSE: To quantify the 3-dimensional pancreatic tumor motion during the overall treatment course using real-time orthogonal kilovoltage X-ray imaging. METHODS AND MATERIALS: This study included 10 patients with pancreatic cancer who underwent 6-port static intensity modulated radiation therapy with real-time tumor tracking in 15 fractions, except for 1 patient (5 fractions). The tumor and abdominal wall positions were acquired simultaneously during the overall treatment course. Then the tumor motion amplitude and reference positions were determined. RESULTS: The mean tumor amplitudes were 4.9, 6.5, and 13.4 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The intrafractional variations of the reference tumor position were up to 5.4, 10.2, and 10.7 mm in the LR, AP, and SI directions, and those of the reference abdominal position were up to 10.5 mm. The reference tumor position drifted significantly in the AP and SI directions after 10 minutes, and that of abdominal wall motion drifted during the first 15 minutes (P<.05). The interfractional variation of the reference tumor position after setup correction, based on bony structures, was up to 8.9, 9.8, and 11.0 mm in the LR, AP, and SI directions, respectively. CONCLUSIONS: Appropriate respiratory motion management techniques should be applied for the accurate localization of pancreatic tumors.

Use of a second-dose calculation algorithm to check dosimetric parameters for the dose distribution of a first-dose calculation algorithm for lung SBRT plans.

PURPOSE: To verify lung stereotactic body radiotherapy (SBRT) plans using a secondary treatment planning system (TPS) as an independent method of verification and to define tolerance levels (TLs) in lung SBRT between the primary and secondary TPSs. METHODS: A total of 147 lung SBRT plans calculated using X-ray voxel Monte Carlo (XVMC) were exported from iPlan to Eclipse in DICOM format. Dose distributions were recalculated using the Acuros XB (AXB) and the anisotropic analytical algorithm (AAA), while maintaining monitor units (MUs) and the beam arrangement. Dose to isocenter and dose-volumetric parameters, such as D2, D50, D95 and D98, were evaluated for each patient. The TLs of all parameters between XVMC and AXB (TLAXB) and between XVMC and AAA (TLAAA) were calculated as the mean±1.96 standard deviations. RESULTS: AXB values agreed with XVMC values within 3.5% for all dosimetric parameters in all patients. By contrast, AAA sometimes calculated a 10% higher dose in PTV D95 and D98 than XVMC. The TLAXB and TLAAA of the dose to isocenter were -0.3±1.4% and 0.6±2.9%, respectively. Those of D95 were 1.3±1.8% and 1.7±3.6%, respectively. CONCLUSIONS: This study quantitatively demonstrated that the dosimetric performance of AXB is almost equal to that of XVMC, compared with that of AAA. Therefore, AXB is a more appropriate algorithm for an independent verification method for XVMC.

Multivariate analysis for the estimation of target localization errors in fiducial marker-based radiotherapy.

PURPOSE: To assess the target localization error (TLE) in terms of the distance between the target and the localization point estimated from the surrogates (|TMD|), the average of respiratory motion for the surrogates and the target (|aRM|), and the number of fiducial markers used for estimating the target (n). METHODS: This study enrolled 17 lung cancer patients who subsequently underwent four fractions of real-time tumor tracking irradiation. Four or five fiducial markers were implanted around the lung tumor. The three-dimensional (3D) distance between the tumor and markers was at maximum 58.7 mm. One of the markers was used as the target (Pt), and those markers with a 3D |TMDn| ≤ 58.7 mm at end-exhalation were then selected. The estimated target position (Pe) was calculated from a localization point consisting of one to three markers except Pt. Respiratory motion for Pt and Pe was defined as the root mean square of each displacement, and |aRM| was calculated from the mean value. TLE was defined as the root mean square of each difference between Pt and Pe during the monitoring of each fraction. These procedures were performed repeatedly using the remaining markers. To provide the best guidance on the answer with n and |TMD|, fiducial markers with a 3D |aRM ≥ 10 mm were selected. Finally, a total of 205, 282, and 76 TLEs that fulfilled the 3D |TMD| and 3D |aRM| criteria were obtained for n = 1, 2, and 3, respectively. Multiple regression analysis (MRA) was used to evaluate TLE as a function of |TMD| and |aRM| in each n. RESULTS: |TMD| for n = 1 was larger than that for n = 3. Moreover, |aRM| was almost constant for all n, indicating a similar scale for the marker's motion near the lung tumor. MRA showed that |aRM| in the left-right direction was the major cause of TLE; however, the contribution made little difference to the 3D TLE because of the small amount of motion in the left-right direction. The TLE calculated from the MRA ((MRA)TLE) increased as |TMD| and |aRM| increased and adversely decreased with each increment of n. The median 3D (MRA)TLE was 2.0 mm (range, 0.6-4.3 mm) for n = 1, 1.8 mm (range, 0.4-4.0 mm) for n = 2, and 1.6 mm (range, 0.3-3.7 mm) for n = 3. Although statistical significance between n = 1 and n = 3 was observed in all directions, the absolute average difference and the standard deviation of the (MRA)TLE between n = 1 and n = 3 were 0.5 and 0.2 mm, respectively. CONCLUSIONS: A large |TMD| and |aRM| increased the differences in TLE between each n; however, the difference in 3D (MRA)TLEs was, at most, 0.6 mm. Thus, the authors conclude that it is acceptable to continue fiducial marker-based radiotherapy as long as |TMD| is maintained at ≤58.7 mm for a 3D |aRM| ≥ 10 mm.

Development of a four-axis moving phantom for patient-specific QA of surrogate signal-based tracking IMRT.

PURPOSE: The purposes of this study were two-fold: first, to develop a four-axis moving phantom for patient-specific quality assurance (QA) in surrogate signal-based dynamic tumor-tracking intensity-modulated radiotherapy (DTT-IMRT), and second, to evaluate the accuracy of the moving phantom and perform patient-specific dosimetric QA of the surrogate signal-based DTT-IMRT. METHODS: The four-axis moving phantom comprised three orthogonal linear actuators for target motion and a fourth one for surrogate motion. The positional accuracy was verified using four laser displacement gauges under static conditions (±40 mm displacements along each axis) and moving conditions [eight regular sinusoidal and fourth-power-of-sinusoidal patterns with peak-to-peak motion ranges (H) of 10-80 mm and a breathing period (T) of 4 s, and three irregular respiratory patterns with H of 1.4-2.5 mm in the left-right, 7.7-11.6 mm in the superior-inferior, and 3.1-4.2 mm in the anterior-posterior directions for the target motion, and 4.8-14.5 mm in the anterior-posterior direction for the surrogate motion, and T of 3.9-4.9 s]. Furthermore, perpendicularity, defined as the vector angle between any two axes, was measured using an optical measurement system. The reproducibility of the uncertainties in DTT-IMRT was then evaluated. Respiratory motions from 20 patients acquired in advance were reproduced and compared three-dimensionally with the originals. Furthermore, patient-specific dosimetric QAs of DTT-IMRT were performed for ten pancreatic cancer patients. The doses delivered to Gafchromic films under tracking and moving conditions were compared with those delivered under static conditions without dose normalization. RESULTS: Positional errors of the moving phantom under static and moving conditions were within 0.05 mm. The perpendicularity of the moving phantom was within 0.2° of 90°. The differences in prediction errors between the original and reproduced respiratory motions were -0.1 ± 0.1 mm for the lateral direction, -0.1 ± 0.2 mm for the superior-inferior direction, and -0.1 ± 0.1 mm for the anterior-posterior direction. The dosimetric accuracy showed significant improvements, of 92.9% ± 4.0% with tracking versus 69.8% ± 7.4% without tracking, in the passing rates of γ with the criterion of 3%/1 mm (p < 0.001). Although the dosimetric accuracy of IMRT without tracking showed a significant negative correlation with the 3D motion range of the target (r = - 0.59, p < 0.05), there was no significant correlation for DTT-IMRT (r = 0.03, p = 0.464). CONCLUSIONS: The developed four-axis moving phantom had sufficient accuracy to reproduce patient respiratory motions, allowing patient-specific QA of the surrogate signal-based DTT-IMRT under realistic conditions. Although IMRT without tracking decreased the dosimetric accuracy as the target motion increased, the DTT-IMRT achieved high dosimetric accuracy.

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

PURPOSE: Dynamic 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 METHODS: A fiducial marker was implanted near the tumor prior to treatment planning. The prescription dose at the isocenter was 48-60 Gy, 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. RESULTS: Eleven 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 1.7 mm in the left-right, cranio-caudal, and anterior-posterior directions, respectively. After a median follow-up of 11 months, the local control rate was 90%. CONCLUSIONS: Our 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.

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

PURPOSE: To 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. METHODS: The 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. RESULTS: Strong 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. CONCLUSIONS: The accuracies of CMs derived using 20 s were almost identical to those obtained over 40 s, and superior to those obtained over 10 s.

Target localization errors from fiducial markers implanted around a lung tumor for dynamic tumor tracking.

PURPOSE: To assess target localization errors (TLEs) from implanted fiducial markers by three different centers of gravity (CG) and three different multiple regression analysis (MRA) approaches. METHODS: The three-dimensional (3D) positions of the markers were detected on the fluoroscopic images of 15 lung cancer patients, and the marker closest to the tumor was then assumed to be the target (Pt). The estimated target position (Pe) was calculated from three markers adjacent to the target (Pi, 1 ≤ i ≤ 3) using the equation Pe = aP1 + bP2 + cP3 + d. Pe was then calculated using three different CGs and three different MRAs. The TLE was calculated as the root-mean-square error of the difference between Pt and Pe calculated for each fraction. First, we compared the TLE of the first fraction to assess the intrafraction TLE of the six approaches tested. Second, interfraction TLEs were calculated to evaluate the robustness of the coefficients obtained in the first fraction. The interfraction TLE was defined as the difference between the TLE of a later and the first fraction. RESULTS: The mean plus two times the standard deviation of the intrafraction TLE was up to 4.3 mm in the CG approaches, while the MRA approaches provided TLEs within 1.5 mm. The mean plus two times the standard deviation of the interfraction TLE did not exceed 1.7 mm in any direction using either approach. CONCLUSIONS: The MRA approach was superior to the CG approach in terms of estimating the target position based on the implanted fiducial markers.

A multi-centre analysis of treatment procedures and error components in dynamic tumour tracking radiotherapy.

PURPOSE: This study aimed to compare procedures for dynamic tumour tracking (DTT) using a gimbal-mounted linac between centres in Japan (KU-IBRI) and Belgium (UZB), to quantify tracking error (TE), and to estimate tumour-fiducial uncertainties and PTV margins. METHODS: Twenty-two patients were evaluated. TE was divided into components originating from the patient, fraction, segment, and residuals. RESULTS: KU-IBRI applied DTT to lung cancer, while UZB treated both the lung and liver. Patients from UZB were younger and had a higher body mass index. DTT procedures differed in the use of body fixation, correction for set-up error, type of fiducial markers, and goodness of fit of correlation model. TE was larger at UZB in the intra-fraction components, whereas the tumour-fiducial uncertainties were estimated to be larger at KU-IBRI. These results ultimately led to similar PTV margins at both centres (2.1, 4.2, and 2.6 mm for KU-IBRI; 2.4, 3.6, and 2.0 mm for UZB in LR, AP, and SI, respectively, for 99% coverage of patients). CONCLUSION: Several differences in procedures and patient characteristics were observed that affected TE and tumour-fiducial uncertainties. This analysis confirmed similar accuracy in DTT delivery and adequate PTV margins in the different centres based on their local specific workflows.

[A Survey Towards Standardization of Dosimetric Verification in Intensity-modulated Radiation Therapy].

PURPOSE: The purpose of this study was to investigate the status of the implementation of quality assurance (QA) for intensity-modulated radiation therapy (IMRT) in Japan using a questionnaire survey. METHODS: The questionnaire consisted of seven sections: (1) clinical uses of IMRT, (2) treatment planning systems, treatment machines, phantoms for verification and CT scanning, (3) absorbed dose verification, (4) dose distribution verification, (5) fluence map verification, (6) acceptance criteria for each verification, and (7) comments. RESULTS: The questionnaire was completed by 129 institutions (response rate: 76.8%). IMRT was performed for prostate cancer in 125 institutions (96.9%), followed by head and neck cancer in 83 (64.3%), and brain tumors in 69 (53.5%). Although at least three individuals were engaged in IMRT QA in 77.5% of the institutions, the number of full-time persons involved in IMRT QA was one or less in 94 institutions (72.9%). This indicated that most institutions in Japan have a staff shortage. More than 90% of the institutions verified both the absorbed dose and dose distribution. The acceptance criterion for the absorbed dose verification was set to ±3% in at least 80% of the institutions. Gafchromic film was used for the majority of dose distribution verifications. The acceptance criteria for dose distribution verification mainly involved gamma analysis and a comparison of dose profiles; however, the judgment of acceptance did not depend on the results of the gamma analysis. CONCLUSION: This survey increases our understanding of how institutions currently perform IMRT QA analysis. This understanding will help to move institutions toward more standardization of IMRT QA in Japan.

Predictive uncertainty in infrared marker-based dynamic tumor tracking with Vero4DRT.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.