Positional accuracy of novel x-ray-image-based dynamic tumor-tracking irradiation using a gimbaled MV x-ray head of a Vero4DRT (MHI-TM2000)

Population-based asymmetric margins for moving targets in real-time tumor tracking

BACKGROUND

Both geometric and dosimetric components are commonly considered when determining the margin for planning target volume (PTV). As dose distribution is shaped by controlling beam aperture in peripheral dose prescription and dose-escalated simultaneously integrated boost techniques, adjusting the margin by incorporating the variable dosimetric component into the PTV margin is inappropriate; therefore, geometric components should be accurately estimated for margin calculations.

PURPOSE

We introduced an asymmetric margin-calculation theory using the guide to the expression of uncertainty in measurement (GUM) and intra-fractional motion. The margins in fiducial marker-based real-time tumor tracking (RTTT) for lung, liver, and pancreatic cancers were calculated and were then evaluated using Monte Carlo (MC) simulations.

METHODS

A total of 74 705, 73 235, and 164 968 sets of intra- and inter-fractional positional data were analyzed for 48 lung, 48 liver, and 25 pancreatic cancer patients, respectively, in RTTT clinical trials. The 2.5th and 97.5th percentiles of the positional error were considered representative values of each fraction of the disease site. The population-based statistics of the probability distributions of these representative positional errors (PD-RPEs) were calculated in six directions. A margin covering 95% of the population was calculated using the proposed formula. The content rate in which the clinical target volume (CTV) was included in the PTV was calculated through MC simulations using the PD-RPEs.

RESULTS

The margins required for RTTT were at most 6.2, 4.6, and 3.9 mm for lung, liver, and pancreatic cancer, respectively. MC simulations revealed that the median content rates using the proposed margins satisfied 95% for lung and liver cancers and 93% for pancreatic cancer, closer to the expected rates than the margins according to van Herk's formula.

CONCLUSIONS

Our proposed formula based on the GUM and motion probability distributions (MPD) accurately calculated the practical margin size for fiducial marker-based RTTT. This was verified through MC simulations.

Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets

PURPOSE

To assess the feasibility of using the diaphragm as a surrogate for liver targets during MDTT.

METHODS

Diaphragm as surrogate for markers: a dome-shaped phantom with implanted markers was fabricated and underwent dual-orthogonal fluoroscopy sequences on the Vero4DRT linac. Ten patients participated in an IRB-approved, feasibility study to assess the MDTT workflow. All images were analyzed using an in-house program to back-project the diaphragm/markers position to the isocenter plane. ExacTrac imager log files were analyzed. Diaphragm as tracking structure for MDTT: The phantom "diaphragm" was contoured as a markerless tracking structure (MTS) and exported to Vero4DRT/ExacTrac. A single field plan was delivered to the phantom film plane under static and MDTT conditions. In the patient study, the diaphragm tracking structure was contoured on CT breath-hold-exhale datasets. The MDTT workflow was applied until just prior to MV beam-on.

RESULTS

Diaphragm as surrogate for markers: phantom data confirmed the in-house 3D back-projection program was functioning as intended. In patients, the diaphragm/marker relative positions had a mean ± RMS difference of 0.70 ± 0.89, 1.08 ± 1.26, and 0.96 ± 1.06 mm in ML, SI, and AP directions. Diaphragm as tracking structure for MDTT: Building a respiratory-correlation model using the diaphragm as surrogate for the implanted markers was successful in phantom/patients. During the tracking verification imaging step, the phantom mean ± SD difference between the image-detected and predicted "diaphragm" position was 0.52 ± 0.18 mm. The 2D film gamma (2%/2 mm) comparison (static to MDTT deliveries) was 98.2%. In patients, the mean difference between the image-detected and predicted diaphragm position was 2.02 ± 0.92 mm. The planning target margin contribution from MDTT diaphragm tracking is 2.2, 5.0, and 4.7 mm in the ML, SI, and AP directions.

CONCLUSION

In phantom/patients, the diaphragm motion correlated well with markers' motion and could be used as a surrogate. MDTT workflows using the diaphragm as the MTS is feasible using the Vero4DRT linac and could replace the need for implanted markers for liver radiotherapy.

Local control of stereotactic body radiotherapy with dynamic tumor tracking for lung tumors: a propensity score-matched analysis

Background

Dynamic tumor tracking (DTT) is a method of respiratory motion management in radiotherapy. It reduces the radiation field but risks delivering an insufficient radiation dose to the tumor. We investigated the local control of DTT-stereotactic body radiotherapy (SBRT) for lung tumors.

Methods

Patients treated with SBRT for early-stage, non-small-cell lung cancer and lung metastases (2013–18) were retrospectively reviewed. Patients with tumor motion >1 cm were treated with DTT-SBRT (DTT group); those with tumor motion ≤1 cm were treated with static-SBRT (static group). A static planning target volume for the static-SBRT plan was also created for patients in the DTT group, and planning target volume reduction relative to the planning target volume for the DTT-SBRT plan was assessed. Patients were matched in a 1:1 ratio using a propensity score predictive of the SBRT technique.

Results

Of the 245 lesions in 218 patients (median follow-up, 25.4 months), 69 were treated with DTT-SBRT and 176 with static-SBRT. The median planning target volume reduction in the DTT group was 30.3%. After propensity score matching, 124 lesions were included (62 per group). Two-year local control rates for the DTT and static groups were 94.2 and 95.9%, respectively, for all lesions (P = 0.19) and 96.3 and 94.5%, respectively, for matched lesions (P = 0.79). In univariate analysis, DTT-SBRT was not associated with local control for all lesions (hazard ratio, 2.06; P = 0.20) or matched lesions (hazard ratio, 1.22; P = 0.79). No grade 4/5 toxicities were observed.

Conclusions

DTT-SBRT for lung tumors reduced the planning target volume, but not local control rates. DTT was useful for respiratory motion management.

Tumor localization accuracy for high-precision radiotherapy during active breath-hold

BACKGROUND

Conventionally fractionated and stereotactic body radiation therapy (SBRT) for thoracoabdominal tumors may utilize breath-hold techniques. However, there are concerns that differential amounts of inspired airflow may result in unplanned tumor dislocation and underdosing. Thus, we investigated tumor localization accuracy associated with lung volume variations during breath-hold treatment via an automated-gating interface.

METHODS

Twelve patients received breath-hold treatment with the active breathing coordinator (ABC) through an automated-gating interface. All breath-hold volumes were recorded at CT simulation, setup imaging, and during treatment, and analyzed as a function of airflow rate into the ABC. The variation of breath-hold volumes was calculated for each fraction over entire course. Intrafraction target motion related to the breathing variation was investigated based on daily imaging acquired before the breath-hold treatment. Correlation between target location and breath-hold variation was statistically analyzed.

RESULTS

The air volume held by the ABC increased as the airflow rate increased on inhalation and decreased on exhalation. The mean range of airflow rate was 0.77 L/s and 0.29 L/s in the conventionally fractionated and SBRT patients, respectively. The maximum air volume difference with respect to the reference volume at the CT simulation was 1.0 L for conventional fractionation and 0.16 L for SBRT. The target dislocation caused by 0.25 L of air volume difference was 6 mm for SBRT. Three patients showed significant correlation between the target location and breath-hold variations.

CONCLUSIONS

This investigation shows that because variations in the breath-hold volume may cause target dislocation, patient-specific breath-hold setting is required to improve tumor localization accuracy.

See, Think, and Act: Real-Time Adaptive Radiotherapy

The world is embracing the information age, with real-time data at hand to assist with many decisions. Similarly, in cancer radiotherapy we are inexorably moving toward using information in a smarter and faster fashion, to usher in the age of real-time adaptive radiotherapy. The three critical steps of real-time adaptive radiotherapy, aligned with driverless vehicle technology are a continuous see, think, and act loop. See: use imaging systems to probe the patient anatomy or physiology as it evolves with time. Think: use current and prior information to optimize the treatment using the available adaptive degrees of freedom. Act: deliver the real-time adapted treatment. This paper expands upon these three critical steps for real-time adaptive radiotherapy, provides a historical context, reviews the clinical rationale, and gives a future outlook for real-time adaptive radiotherapy.

Selection of external beam radiotherapy approaches for precise and accurate cancer treatment

Physically precise external-beam radiotherapy (EBRT) technologies may not translate to the best outcome in individual patients. On the other hand, clinical considerations alone are often insufficient to guide the selection of a specific EBRT approach in patients. We examine the ways in which to compare different EBRT approaches based on physical, biological and clinical considerations, and how they can be enhanced with the addition of biophysical models and machine-learning strategies. The process of selecting an EBRT modality is expected to improve in tandem with knowledge-based treatment planning.

Dose calculation and verification of the Vero gimbal tracking treatment delivery

The Vero linear accelerator delivers dynamic tumor tracking (DTT) treatment using a gimbal motion. However, the availability of treatment planning systems (TPS) to simulate DTT is limited. This study aims to implement and verify the gimbal tracking beam geometry in the dose calculation. Gimbal tracking was implemented by rotating the reference CT outside the TPS according to the ring, gantry, and gimbal tracking position obtained from the tracking log file. The dose was calculated using these rotated CTs. The geometric accuracy was verified by comparing calculated and measured film response using a ball bearing phantom. The dose was verified by comparing calculated 2D dose distributions and film measurements in a ball bearing and a homogeneous phantom using a gamma criterion of 2%/2 mm. The effect of implementing the gimbal tracking beam geometry in a 3D patient data dose calculation was evaluated using dose volume histograms (DVH). Geometrically, the gimbal tracking implementation accuracy was  <0.94 mm. The isodose lines agreed with the film measurement. The largest dose difference of 9.4% was observed at maximum tilt positions with an isocenter and target separation of 17.51 mm. Dosimetrically, gamma passing rates were  >98.4%. The introduction of the gimbal tracking beam geometry in the dose calculation shifted the DVH curves by 0.05%-1.26% for the phantom geometry and by 5.59% for the patient CT dataset. This study successfully demonstrates a method to incorporate the gimbal tracking beam geometry into dose calculations. By combining CT rotation and MU distribution according to the log file, the TPS was able to simulate the Vero tracking treatment dose delivery. The DVH analysis from the gimbal tracking dose calculation revealed changes in the dose distribution during gimbal DTT that are not visible with static dose calculations.

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 ± 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.

A dosimetric comparison of real-time adaptive and non-adaptive radiotherapy: A multi-institutional study encompassing robotic, gimbaled, multileaf collimator and couch tracking

PURPOSE

A study of real-time adaptive radiotherapy systems was performed to test the hypothesis that, across delivery systems and institutions, the dosimetric accuracy is improved with adaptive treatments over non-adaptive radiotherapy in the presence of patient-measured tumor motion.

METHODS AND MATERIALS

Ten institutions with robotic(2), gimbaled(2), MLC(4) or couch tracking(2) used common materials including CT and structure sets, motion traces and planning protocols to create a lung and a prostate plan. For each motion trace, the plan was delivered twice to a moving dosimeter; with and without real-time adaptation. Each measurement was compared to a static measurement and the percentage of failed points for γ-tests recorded.

RESULTS

For all lung traces all measurement sets show improved dose accuracy with a mean 2%/2mm γ-fail rate of 1.6% with adaptation and 15.2% without adaptation (p<0.001). For all prostate the mean 2%/2mm γ-fail rate was 1.4% with adaptation and 17.3% without adaptation (p<0.001). The difference between the four systems was small with an average 2%/2mm γ-fail rate of <3% for all systems with adaptation for lung and prostate.

CONCLUSIONS

The investigated systems all accounted for realistic tumor motion accurately and performed to a similar high standard, with real-time adaptation significantly outperforming non-adaptive delivery methods.

Accuracy of image guidance using free-breathing cone-beam computed tomography for stereotactic lung radiotherapy

Movement of the target object during cone-beam computed tomography (CBCT) leads to motion blurring artifacts. The accuracy of manual image matching in image-guided radiotherapy depends on the image quality. We aimed to assess the accuracy of target position localization using free-breathing CBCT during stereotactic lung radiotherapy. The Vero4DRT linear accelerator device was used for the examinations. Reference point discrepancies between the MV X-ray beam and the CBCT system were calculated using a phantom device with a centrally mounted steel ball. The precision of manual image matching between the CBCT and the averaged intensity (AI) images restructured from four-dimensional CT (4DCT) was estimated with a respiratory motion phantom, as determined in evaluations by five independent operators. Reference point discrepancies between the MV X-ray beam and the CBCT image-guidance systems, categorized as left-right (LR), anterior-posterior (AP), and superior-inferior (SI), were 0.33 ± 0.09, 0.16 ± 0.07, and 0.05 ± 0.04 mm, respectively. The LR, AP, and SI values for residual errors from manual image matching were -0.03 ± 0.22, 0.07 ± 0.25, and -0.79 ± 0.68 mm, respectively. The accuracy of target position localization using the Vero4DRT system in our center was 1.07 ± 1.23 mm (2 SD). This study experimentally demonstrated the sufficient level of geometric accuracy using the free-breathing CBCT and the image-guidance system mounted on the Vero4DRT. However, the inter-observer variation and systematic localization error of image matching substantially affected the overall geometric accuracy. Therefore, when using the free-breathing CBCT images, careful consideration of image matching is especially important.

Geometric and dosimetric accuracy and imaging dose of the real-time tumour tracking system of a gimbal mounted linac

PURPOSE

To suggest a comprehensive testing scheme to evaluate the geometric and dosimetric accuracy and the imaging dose of the VERO dynamic tumour tracking (DTT) for its clinical implementation.

METHODS

Geometric accuracy was evaluated for gantry 0° and 90° in terms of prediction (EP), mechanical (EM) and tracking (ET) errors for sinusoidal patterns with 10 and 20 mm amplitudes, 2-6 s periods and phase shift up to 1 s and for 3 patient patterns. The automatic 4D model update was investigated simulating changes in the breathing pattern during treatment. Dosimetric accuracy was evaluated with gafchromic films irradiated in static and moving phantom with and without DTT. The entrance skin dose (ESD) was assessed using a solid state detector and gafchromic films.

RESULTS

The RMS of EP, EM, and ET were up to 0.8, 0.5 and 0.9 mm for all non phased-shifted motion patterns while for the phased-shifted ones, EP and ET increased to 2.2 and 2.6 mm. Up to 4 updates are necessary to restore a good correlation model, according to type of change. For 100 kVp and 1 mA s X-ray beam, the ESD per portal due to 20 s fluoroscopy was 16.6 mGy, while treatment verification at a frequency of 1 Hz contributed with 4.2 mGy/min.

CONCLUSIONS

The proposed testing scheme highlighted that the VERO DTT system tracks a moving target with high accuracy. The automatic update of the 4D model is a powerful tool to guarantee the accuracy of tracking without increasing the imaging dose.

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

Commissioning and initial stereotactic ablative radiotherapy experience with Vero

The purpose of this study is to describe the comprehensive commissioning process and initial clinical performance of the Vero linear accelerator, a new radiotherapy device recently installed at UT Southwestern Medical Center specifically developed for delivery of image‐guided stereotactic ablative radiotherapy (SABR). The Vero system utilizes a ring gantry to integrate a beam delivery platform with image guidance systems. The ring is capable of rotating ± 60° about the vertical axis to facilitate noncoplanar beam arrangements ideal for SABR delivery. The beam delivery platform consists of a 6 MV C‐band linac with a 60 leaf MLC projecting a maximum field size of 15×15 cm2 at isocenter. The Vero planning and delivery systems support a range of treatment techniques, including fixed beam conformal, dynamic conformal arcs, fixed gantry IMRT in either SMLC (step‐and‐shoot) or DMLC (dynamic) delivery, and hybrid arcs, which combines dynamic conformal arcs and fixed beam IMRT delivery. The accelerator and treatment head are mounted on a gimbal mechanism that allows the linac and MLC to pivot in two dimensions for tumor tracking. Two orthogonal kV imaging subsystems built into the ring facilitate both stereoscopic and volumetric (CBCT) image guidance. The system is also equipped with an always‐active electronic portal imaging device (EPID). We present our commissioning process and initial clinical experience focusing on SABR applications with the Vero, including: (1) beam data acquisition; (2) dosimetric commissioning of the treatment planning system, including evaluation of a Monte Carlo algorithm in a specially‐designed anthropomorphic thorax phantom; (3) validation using the Radiological Physics Center thorax, head and neck (IMRT), and spine credentialing phantoms; (4) end‐to‐end evaluation of IGRT localization accuracy; (5) ongoing system performance, including isocenter stability; and (6) clinical SABR applications.

PACS number: 87.53.Ly

Effect of audio instruction on tracking errors using a four-dimensional image-guided radiotherapy system

The Vero4DRT (MHI‐TM2000) is capable of performing X‐ray image‐based tracking (X‐ray Tracking) that directly tracks the target or fiducial markers under continuous kV X‐ray imaging. Previously, we have shown that irregular respiratory patterns increased X‐ray Tracking errors. Thus, we assumed that audio instruction, which generally improves the periodicity of respiration, should reduce tracking errors. The purpose of this study was to assess the effect of audio instruction on X‐ray Tracking errors. Anterior‐posterior abdominal skin‐surface displacements obtained from ten lung cancer patients under free breathing and simple audio instruction were used as an alternative to tumor motion in the superior‐inferior direction. First, a sequential predictive model based on the Levinson‐Durbin algorithm was created to estimate the future three‐dimensional (3D) target position under continuous kV X‐ray imaging while moving a steel ball target of 9.5 mm in diameter. After creating the predictive model, the future 3D target position was sequentially calculated from the current and past 3D target positions based on the predictive model every 70 ms under continuous kV X‐ray imaging. Simultaneously, the system controller of the Vero4DRT calculated the corresponding pan and tilt rotational angles of the gimbaled X‐ray head, which then adjusted its orientation to the target. The calculated and current rotational angles of the gimbaled X‐ray head were recorded every 5 ms. The target position measured by the laser displacement gauge was synchronously recorded every 10 msec. Total tracking system errors (ET) were compared between free breathing and audio instruction. Audio instruction significantly improved breathing regularity (p < 0.01). The mean ± standard deviation of the 95th percentile of ET (E95T) was 1.7 ± 0.5 mm (range: 1.1–2.6 mm) under free breathing (E95T,FB) and 1.9 ± 0.5 mm (range: 1.2–2.7 mm) under audio instruction (E95T,AI). E95T,AI was larger than E95T,FB for five patients; no significant difference was found between E95T,FB and ET,AI95(p = 0.21). Correlation analysis revealed that the rapid respiratory velocity significantly increased E95T. Although audio instruction improved breathing regularity, it also increased the respiratory velocity, which did not necessarily reduce tracking errors.

PACS number: 87.55.ne, 87.57.N‐, 87.59.C‐,