Dynamic Tumor Tracking Research Articles

Dynamic tumor tracking using the Elekta Agility MLC.

To evaluate the performance of the Elekta Agility multileaf collimator (MLC) for dynamic real-time tumor tracking. The authors have developed a new control software which interfaces to the Agility MLC to dynamically program the movement of individual leaves, the dynamic leaf guides (DLGs), and the Y collimators ("jaws") based on the actual target trajectory. A motion platform was used to perform dynamic tracking experiments with sinusoidal trajectories. The actual target positions reported by the motion platform at 20, 30, or 40 Hz were used as shift vectors for the MLC in beams-eye-view. The system latency of the MLC (i.e., the average latency comprising target device reporting latencies and MLC adjustment latency) and the geometric tracking accuracy were extracted from a sequence of MV portal images acquired during irradiation for the following treatment scenarios: leaf-only motion, jaw + leaf motion, and DLG + leaf motion. The portal imager measurements indicated a clear dependence of the system latency on the target position reporting frequency. Deducting the effect of the target frequency, the leaf adjustment latency was measured to be 38 ± 3 ms for a maximum target speed v of 13 mm/s. The jaw + leaf adjustment latency was 53 ± 3 at a similar speed. The system latency at a target position frequency of 30 Hz was in the range of 56-61 ms for the leaves (v ≤ 31 mm/s), 71-78 ms for the jaw + leaf motion (v ≤ 25 mm/s), and 58-72 ms for the DLG + leaf motion (v ≤ 59 mm/s). The tracking accuracy showed a similar dependency on the target position frequency and the maximum target speed. For the leaves, the root-mean-squared error (RMSE) was between 0.6-1.5 mm depending on the maximum target speed. For the jaw + leaf (DLG + leaf) motion, the RMSE was between 0.7-1.5 mm (1.9-3.4 mm). The authors have measured the latency and geometric accuracy of the Agility MLC, facilitating its future use for clinical tracking applications.

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.

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.

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.

Improving the Target Estimation Accuracy in Infrared Marker-Based Dynamic Tumor Tracking With a Gimbaled Linac

Improving the Target Estimation Accuracy in Infrared Marker-Based Dynamic Tumor Tracking With a Gimbaled Linac

Evaluation of dynamic tumour tracking radiotherapy with real-time monitoring for lung tumours using a gimbal mounted linac

PurposeTo evaluate feasibility and acute toxicities after dynamic tumour tracking (DTT) irradiation with real-time monitoring for lung tumours using a gimbal mounted linac. Materials and methodsSpherical gold markers were placed around the tumour using a bronchoscope prior to treatment planning. Prescription dose at the isocentre was 56Gy in 4 fractions for T2a lung cancer and metastatic tumour, and 48Gy in 4 fractions for the others. Dose-volume metrics were compared between DTT and conventional static irradiation using in-house developed software. ResultsOf twenty-two patients enrolled, DTT radiotherapy was successfully performed for 16 patients, except 4 patients who coughed out the gold markers, one who showed spontaneous tumour regression, and one where the abdominal wall motion did not correlate with the tumour motion. Dose covering 95% volume of GTV was not different between the two techniques, while normal lung volume receiving 20Gy or more was reduced by 20%. A mean treatment time per fraction was 36min using DTT. With a median follow-up period of 13.2months, no severe toxicity grade 3 or worse was observed. ConclusionsDTT radiotherapy using a gimbal mounted linac was clinically feasible for lung treatment without any severe acute toxicity.

WE-G-BRF-06: Positron Emission Tomography (PET)-Guided Dynamic Lung Tumor Tracking for Cancer Radiotherapy: First Patient Simulations

Purpose: PET-guided dynamic tumor tracking is a novel concept of biologically targeted image guidance for radiotherapy. A dynamic tumor tracking algorithm based on list-mode PET data has been developed and previously tested on dynamic phantom data. In this study, we investigate if dynamic tumor tracking is clinically feasible by applying the method to lung cancer patient PET data. Methods: PET-guided tumor tracking estimates the target position of a segmented volume in PET images reconstructed continuously from accumulated coincidence events correlated with external respiratory motion, simulating real-time applications, i.e., only data up to the current time point is used to estimate the target position. A target volume is segmented with a 50% threshold, consistently, of the maximum intensity in the predetermined volume of interest. Through this algorithm, the PET-estimated trajectories are quantified from four lung cancer patients who have distinct tumor location and size. The accuracy of the PET-estimated trajectories is evaluated by comparing to external respiratory motion because the ground-truth of tumor motion is not known in patients; however, previous phantom studies demonstrated sub-2mm accuracy using clinically derived 3D tumor motion. Results: The overall similarity of motion patterns between the PET-estimated trajectories and the external respiratory traces implies that the PET-guided tracking algorithm can provide an acceptable level of targeting accuracy. However, there are variations in the tracking accuracy between tumors due to the quality of the segmentation which depends on target-to-background ratio, tumor location and size. Conclusion: For the first time, a dynamic tumor tracking algorithm has been applied to lung cancer patient PET data, demonstrating clinical feasibility of real-time tumor tracking for integrated PET-linacs. The target-to-background ratio is a significant factor determining accuracy: screening during treatment planning would enable appropriate patient selection for PET-guided dynamic tumor tracking in radiotherapy.

SU‐E‐T‐465: Dose Calculation Method for Dynamic Tumor Tracking Using a Gimbal‐Mounted Linac

Purpose:Dynamic tumor tracking using the gimbal‐mounted linac (Vero4DRT, Mitsubishi Heavy Industries, Ltd., Japan) has been available when respiratory motion is significant. The irradiation accuracy of the dynamic tumor tracking has been reported to be excellent. In addition to the irradiation accuracy, a fast and accurate dose calculation algorithm is needed to validate the dose distribution in the presence of respiratory motion because the multiple phases of it have to be considered. A modification of dose calculation algorithm is necessary for the gimbal‐mounted linac due to the degrees of freedom of gimbal swing. The dose calculation algorithm for the gimbal motion was implemented using the linear transformation between coordinate systems.Methods:The linear transformation matrices between the coordinate systems with and without gimbal swings were constructed using the combination of translation and rotation matrices. The coordinate system where the radiation source is at the origin and the beam axis along the z axis was adopted. The transformation can be divided into the translation from the radiation source to the gimbal rotation center, the two rotations around the center relating to the gimbal swings, and the translation from the gimbal center to the radiation source. After operating the transformation matrix to the phantom or patient image, the dose calculation can be performed as the no gimbal swing. The algorithm was implemented in the treatment planning system, PlanUNC (University of North Carolina, NC). The convolution/superposition algorithm was used. The dose calculations with and without gimbal swings were performed for the 3 × 3 cm2 field with the grid size of 5 mm.Results:The calculation time was about 3 minutes per beam. No significant additional time due to the gimbal swing was observed.Conclusions:The dose calculation algorithm for the finite gimbal swing was implemented. The calculation time was moderate.

TH‐E‐17A‐10: Markerless Lung Tumor Tracking Based On Beams Eye View EPID Images

Purpose:Dynamic tumor tracking or motion compensation techniques have proposed to modify beam delivery following lung tumor motion on the flight. Conventional treatment plan QA could be performed in advance since every delivery may be different. Markerless lung tumor tracking using beams eye view EPID images provides a best treatment evaluation mechanism. The purpose of this study is to improve the accuracy of the online markerless lung tumor motion tracking method.Methods:The lung tumor could be located on every frame of MV images during radiation therapy treatment by comparing with corresponding digitally reconstructed radiograph (DRR). A kV‐MV CT corresponding curve is applied on planning kV CT to generate MV CT images for patients in order to enhance the similarity between DRRs and MV treatment images. This kV‐MV CT corresponding curve was obtained by scanning a same CT electron density phantom by a kV CT scanner and MV scanner (Tomotherapy) or MV CBCT. Two sets of MV DRRs were then generated for tumor and anatomy without tumor as the references to tracking the tumor on beams eye view EPID images.Results:Phantom studies were performed on a Varian TrueBeam linac. MV treatment images were acquired continuously during each treatment beam delivery at 12 gantry angles by iTools. Markerless tumor tracking was applied with DRRs generated from simulated MVCT. Tumors were tracked on every frame of images and compared with expected positions based on programed phantom motion. It was found that the average tracking error were 2.3 mm.Conclusion:This algorithm is capable of detecting lung tumors at complicated environment without implanting markers. It should be noted that the CT data has a slice thickness of 3 mm. This shows the statistical accuracy is better than the spatial accuracy.This project has been supported by a Varian Research Grant.

SU‐E‐J‐23: An Accurate Algorithm to Match Imperfectly Matched Images for Lung Tumor Detection Without Markers

Purpose:In order to locate lung tumors on projection images without internal markers, digitally reconstructed radiograph (DRR) is created and compared with projection images. Since lung tumors always move and their locations change on projection images while they are static on DRRs, a special DRR (background DRR) is generated based on modified anatomy from which lung tumors are removed. In addition, global discrepancies exist between DRRs and projections due to their different image originations, scattering, and noises. This adversely affects comparison accuracy. A simple but efficient comparison algorithm is reported.Methods:This method divides global images into a matrix of small tiles and similarities will be evaluated by calculating normalized cross correlation (NCC) between corresponding tiles on projections and DRRs. The tile configuration (tile locations) will be automatically optimized to keep the tumor within a single tile which has bad matching with the corresponding DRR tile. A pixel based linear transformation will be determined by linear interpolations of tile transformation results obtained during tile matching. The DRR will be transformed to the projection image level and subtracted from it. The resulting subtracted image now contains only the tumor. A DRR of the tumor is registered to the subtracted image to locate the tumor.Results:This method has been successfully applied to kV fluoro images (about 1000 images) acquired on a Vero (Brainlab) for dynamic tumor tracking on phantom studies. Radiation opaque markers are implanted and used as ground truth for tumor positions. Although, other organs and bony structures introduce strong signals superimposed on tumors at some angles, this method accurately locates tumors on every projection over 12 gantry angles. The maximum error is less than 2.6 mm while the total average error is 1.0 mm.Conclusion:This algorithm is capable of detecting tumor without markers despite strong background signals.

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

Intrafractional tracking accuracy in infrared marker-based hybrid dynamic tumour-tracking irradiation with a gimballed linac

PurposeTo verify the intrafractional tracking accuracy in infrared (IR) marker-based hybrid dynamic tumour tracking irradiation (“IR Tracking”) with the Vero4DRT. Materials and methodsThe gimballed X-ray head tracks a moving target by predicting its future position from displacements of IR markers in real-time. Ten lung cancer patients who underwent IR Tracking were enrolled. The 95th percentiles of intrafractional mechanical (iEM95), prediction (iEP95), and overall targeting errors (iET95) were calculated from orthogonal fluoroscopy images acquired during tracking irradiation and from the synchronously acquired log files. ResultsAveraged intrafractional errors were (left–right, cranio-caudal [CC], anterior–posterior [AP])=(0.1mm, 0.4mm, 0.1mm) for iEM95, (1.2mm, 2.7mm, 2.1mm) for iEP95, and (1.3mm, 2.4mm, 1.4mm) for iET95. By correcting systematic prediction errors in the previous field, the iEP95 was reduced significantly, by an average of 0.4mm in the CC (p<0.05) and by 0.3mm in the AP (p<0.01) directions. ConclusionsPrediction errors were the primary cause of overall targeting errors, whereas mechanical errors were negligible. Furthermore, improvement of the prediction accuracy could be achieved by correcting systematic prediction errors in the previous field.

Geometric and dosimetric accuracy of dynamic tumor‐tracking conformal arc irradiation with a gimbaled x‐ray head

The Vero4DRT system has the capability for dynamic tumor-tracking (DTT) stereotactic irradiation using a unique gimbaled x-ray head. The purposes of this study were to develop DTT conformal arc irradiation and to estimate its geometric and dosimetric accuracy. The gimbaled x-ray head, supported on an O-ring gantry, was moved in the pan and tilt directions during O-ring gantry rotation. To evaluate the mechanical accuracy, the gimbaled x-ray head was moved during the gantry rotating according to input command signals without a target tracking, and a machine log analysis was performed. The difference between a command and a measured position was calculated as mechanical error. To evaluate beam-positioning accuracy, a moving phantom, which had a steel ball fixed at the center, was driven based on a sinusoidal wave (amplitude [A]: 20 mm, time period [T]: 4 s), a patient breathing motion with a regular pattern (A: 16 mm, average T: 4.5 s), and an irregular pattern (A: 7.2-23.0 mm, T: 2.3-10.0 s), and irradiated with DTT during gantry rotation. The beam-positioning error was evaluated as the difference between the centroid position of the irradiated field and the steel ball on images from an electronic portal imaging device. For dosimetric accuracy, dose distributions in static and moving targets were evaluated with DTT conformal arc irradiation. The root mean squares (RMSs) of the mechanical error were up to 0.11 mm for pan motion and up to 0.14 mm for tilt motion. The RMSs of the beam-positioning error were within 0.23 mm for each pattern. The dose distribution in a moving phantom with tracking arc irradiation was in good agreement with that in static conditions. The gimbal positional accuracy was not degraded by gantry motion. As in the case of a fixed port, the Vero4DRT system showed adequate accuracy of DTT conformal arc irradiation.

The potential of positron emission tomography for intratreatment dynamic lung tumor tracking: A phantom study

This study aims to evaluate the potential and feasibility of positron emission tomography for dynamic lung tumor tracking during radiation treatment. The authors propose a center of mass (CoM) tumor tracking algorithm using gated-PET images combined with a respiratory monitor and investigate the geometric accuracy of the proposed algorithm. The proposed PET dynamic lung tumor tracking algorithm estimated the target position information through the CoM of the segmented target volume on gated PET images reconstructed from accumulated coincidence events. The information was continuously updated throughout a scan based on the assumption that real-time processing was supported (actual processing time at each frame ≈ 10 s). External respiratory motion and list-mode PET data were acquired from a phantom programmed to move with measured respiratory traces (external respiratory motion and internal target motion) from human subjects, for which the ground truth target position was known as a function of time. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The measured respiratory traces consisted of two sets: (1) 1D-measured motion from ten healthy volunteers and (2) 3D-measured motion from four lung cancer patients. The authors evaluated the geometric accuracy of the proposed algorithm by quantifying estimation errors (Euclidean distance) between the actual motion of targets (1D-motion and 3D-motion traces) and CoM trajectories estimated by the proposed algorithm as a function of time. The time-averaged error of 1D-motion traces over all trajectories of all targets was 1.6 mm. The error trajectories decreased with time as coincidence events were accumulated. The overall error trajectory of 1D-motion traces converged to within 2 mm in approximately 90 s. As expected, more accurate results were obtained for larger targets. For example, for the 37 mm target, the average error over all 1D-motion traces was 1.1 mm; and for the 10 mm target, the average error over all 1D-motion traces was 2.8 mm. The overall time-averaged error of 3D-motion traces was 1.6 mm, which was comparable to that of the 1D-motion traces. There were small variations in the errors between the 3D-motion traces, although the motion trajectories were very different. The accuracy of the estimates was consistent for all targets except for the smallest. The authors developed an algorithm for dynamic lung tumor tracking using list-mode PET data and a respiratory motion signal, and demonstrated proof-of-principle for PET-guided lung tumor tracking. The overall tracking error in phantom studies is less than 2 mm.

OC-0500: A comparison of two clinical correlation models for dynamic tumor tracking with a focus on geometrical accuracy

OC-0500: A comparison of two clinical correlation models for dynamic tumor tracking with a focus on geometrical accuracy

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

EP-1276: First implementation of intensity-modulated dynamic tumor-tracking RT in pancreatic cancer using a gimbaled linac

EP-1276: First implementation of intensity-modulated dynamic tumor-tracking RT in pancreatic cancer using a gimbaled linac

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.

TH-A-WAB-09: The Potential of Positron Emission Tomography (PET) for Intra-Treatment Dynamic Tumor Tracking During Radiotherapy: A Phantom Study

Purpose: This study aimed to evaluate the potential and feasibility of positron emission tomography (PET) for dynamic tumor tracking during radiation treatment. We have proposed a center of mass (CoM) tumor tracking algorithm and investigated the geometric accuracy of the algorithm. Methods: The proposed PET dynamic tumor tracking algorithm estimated the target position information through the CoM of the segmented target volume on gated PET images which were continuously updated throughout a scan. External respiratory motion and list‐mode PET data were acquired from a phantom programmed to move with measured respiratory traces. The phantom was cylindrical with six sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). To test the algorithm on respiratory motion with clinical variability, respiratory traces of higher than average magnitudes and complexity were selected from a large database. The traces were 3D‐measured motion representing typical motion (T1), baseline variations (T2), predominantly left‐right motion (T3), and high‐frequency breathing (T4). Estimation errors were quantified through Euclidean distances between the motion traces and estimated CoM trajectories. Results: The overall time‐averaged error was 1.6 mm over all traces (T1‐T4) of all targets. There were small variations in the errors between the traces, although they were very distinct. The overall time‐averaged error of each trace (T1‐T4) was 1.3, 1.3, 1.7, and 2.2 mm, respectively. The accuracy of the estimates was consistent for all targets except the smallest one: For the 17 mm target, the overall time‐averaged errors of T1‐T4 were 1.2, 1.2, 1.4, and 1.7 mm, respectively; For the 10 mm target, those of T1‐T4 were 2.3, 2.2, 2.6, and 3.9 mm, respectively. Conclusion: We developed an algorithm using list‐mode PET data and external respiratory motion data for dynamic tumor tracking in a combined PET‐linac system. The overall tracking error in phantom studies was less than 2 mm. Research supported by the Kwanjeong Educational Foundation, NIH/NCI R01 93626, Stanford Bio‐X, NHMRC Australia Fellowship, NIH/NCI through SBIR with RefleXion Medical R43CA153466.