External Surrogate Signal Research Articles

Target localization during respiration motion based on LSTM: A pilot study on robotic puncture system

In the needle biopsy, the respiratory motion causes the displacement of thoracic-abdominal soft tissues, which brings great difficulty to accurate localization. Based on internal target motion and external marker motion, the existing methods need to establish a correlation model or a prediction model to compensate the respiratory movement, which can hardly achieve required accuracy in clinic use due to the complexity of the internal tissue motion. In order to improve the tracking accuracy and reduce the number of models, we propose a framework for target localization based on long short-term memory (LSTM) method. Combined with the correlation model and the prediction model by using LSTM, we adopted the principal component of time-series features of external surrogate signals to predict the trajectory of the internal tumour target. Additionally, based on the electromagnetic tracking system and Universal Robots 3 robotic arm, we applied the proposed approach to a prototype of robotic puncture system for real-time tumour tracking. To verify the proposed method, experiments on both public datasets and customized motion phantom for respiratory simulation were performed. In the public dataset study, an average mean absolute error, and an average root-mean-square error of predictive results of 0.44 and 0.58 mm were achieved, respectively. In the motion phantom study, an average root mean square of puncturing error resulted in 0.65 mm. The experimental results demonstrate the proposed method improves the accuracy of target localization during respiratory movement and appeals the potentials applying to clinical application.

Tumor phase recognition using cone-beam computed tomography projections and external surrogate information.

Directly extracting the respiratory phase pattern of the tumor using cone-beam computed tomography (CBCT) projections is challenging due to the poor tumor visibility caused by the obstruction of multiple anatomic structures on the beam's eye view. Predicting tumor phase information using external surrogate also has intrinsic difficulties as the phase patterns between surrogates and tumors are not necessary to be congruent. In this work, we developed an algorithm to accurately recover the primary oscillation components of tumor motion using the combined information from both CBCT projections and external surrogates. The algorithm involved two steps. First, a preliminary tumor phase pattern was acquired by applying local principal component analysis (LPCA) on the cropped Amsterdam Shroud (AS) images. In this step, only the cropped image of the tumor region was used to extract the tumor phase pattern in order to minimize the impact of pattern recognition from other anatomic structures. Second, by performing multivariate singular spectrum analysis (MSSA) on the combined information containing both external surrogate signal and the original waveform acquired in the first step, the primary component of the tumor phase oscillation was recovered. For the phantom study, a QUASAR respiratory motion phantom with a removable tumor-simulator insert was employed to acquire CBCT projection images. A comparison between LPCA only and our method was assessed by power spectrum analysis. Also, the motion pattern was simulated under the phase shift or various amplitude conditions to examine the robustness of our method. Finally, anatomic obstruction scenarios were simulated by attaching a heart model, PVC tubes, and RANDO® phantom slabs to the phantom, respectively. Each scenario was tested with five real-patient breathing patterns to mimic real clinical situations. For the patient study, eight patients with various tumor locations were selected. The performance of our method was then evaluated by comparing the reference waveform with the extracted signal for overall phase discrepancy, expiration phase discrepancy, peak, and valley accuracy. In tests of phase shifts and amplitude variations, the overall peak and valley accuracy was -0.009±0.18sec, and no time delay was found compared to the reference. In anatomical obstruction tests, the extracted signal had 1.6±1.2 % expiration phase discrepancy, -0.12±0.28sec peak accuracy, and 0.01±0.15sec valley accuracy. For patient studies, the extracted signal using our method had -1.05±3.0 % overall phase discrepancy, -1.55±1.45% expiration phase discrepancy, 0.04±0.13sec peak accuracy, and -0.01±0.15sec valley accuracy, compared to the reference waveforms. An innovative method capable of accurately recognizing tumor phase information was developed. With the aid of extra information from the external surrogate, an improvement in prediction accuracy, as compared with traditional statistical methods, was obtained. It enables us to employ it as the ground truth for 4D-CBCT reconstruction, gating treatment, and other clinic implementations that require accurate tumor phase information.

A Kalman-Based Approach With EM Optimization for Respiratory Motion Modeling in Medical Imaging

Respiratory motion degrades quantitative and qualitative analysis of medical images. Estimation and, hence, correction of motion commonly uses static correspondence models between an external surrogate signal and internal motion. This paper presents a patient specific respiratory motion model with the ability to adapt in the presence of irregular motion via a Kalman filter with expectation maximization for parameter estimation. The adaptive approach introduces generalizability allowing the model to account for a broader variety of motion. This may be required in the presence of irregular breathing and with different sensors monitoring the external surrogate signal. The motion model framework utilizing an adaptive Kalman filter approach is tested on dynamic magnetic resonance imaging data of nine volunteers and compared to a state-of-the-art static total least squares approach. Results demonstrate the framework is capable of reducing motion to the order of ${p} ) more effective in the presence of irregular motion, assessed using the ${F}$ -test for model comparison. Utilizing the total sum of squares of estimated vector field error from the calculated ground truth, we observe approximately a fifty percent reduction in root mean square error and thirty percent reduction in standard deviation utilizing the Kalman model (EKF) in comparison to a static counterpart.

Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.

Clinical workflow optimization to improve 4DCT reconstruction for Toshiba Aquilion CT scanners

Respiratory motion remains a source of major uncertainties in radiotherapy. Respiratory correlated computed tomography (referred to as 4DCT) serves as one way of reducing breathing artifacts in 3D-CTs and allows the investigation of tumor motion over time. The quality of the 4DCT images depends on the data acquisition scheme, which in turn is dependent on the vendor. Specifically, the only way Toshiba Aquilion LB CT scanners can reconstruct 4DCTs is a cycle-based reconstruction using triggers provided by an external surrogate signal. The accuracy is strongly dependent on the method of trigger generation. Two consecutive triggers are used to define a breathing cycle which is divided into respiratory phases of equal duration. The goal of this study is to identify if there are advantages in the usage of local-amplitude based sorting (LAS) of the respiration motion states, in order to reduce image artifacts and improve 4DCT quality. Furthermore, this study addresses the generation and optimization of a clinical workflow using as surrogate motion monitoring system the Sentinel™ (C-RAD AB, Sweden) optical surface scanner in combination with a Toshiba Aquilion LB CT scanner. For that purpose, a phantom study using 10 different breathing waveforms and a retrospective patient study using the 4DCT reconstructions of 10 different patients has been conducted. The error in tumor volume has been reduced from 2.9±3.7% to 2.7±2.6% using optimal cycle-based triggers (manipulated CBS) and to 2.7±2.2% using LAS in the phantom study. Moreover, it was possible to decrease the tumor volume variability from 5.0±3.6% using the original cycle-based triggers (original CBS) to 3.5±2.5% using the optimal triggers and to 3.7±2.7% using LAS in the patient data analysis. We therefore propose the usage of the manipulated CBS, also with regard to an accurate and safe clinical workflow.

Evaluation of lung tumor motion management in radiation therapy with dynamic MRI.

Surrogate-based tumor motion estimation and tracing methods are commonly used in radiotherapy despite the lack of continuous real time 3D tumor and surrogate data. In this study, we propose a method to simultaneously track the tumor and external surrogates with dynamic MRI, which allows us to evaluate their reproducible correlation. Four MRI-compatible fiducials are placed on the patient's chest and upper abdomen, and multi-slice 2D cine MRIs are acquired to capture the lung and whole tumor, followed by two-slice 2D cine MRIs to simultaneously track the tumor and fiducials, all in sagittal orientation. A phase-binned 4D-MRI is first reconstructed from multi-slice MR images using body area as a respiratory surrogate and group-wise registration. The 4D-MRI provides 3D template volumes for different breathing phases. 3D tumor position is calculated by 3D-2D template matching in which 3D tumor templates in 4D-MRI reconstruction and the 2D cine MRIs from the two-slice tracking dataset are registered. 3D trajectories of the external surrogates are derived via matching a 3D geometrical model to the fiducial segmentations on the 2D cine MRIs. We tested our method on five lung cancer patients. Internal target volume from 4D-CT showed average sensitivity of 86.5% compared to the actual tumor motion for 5 min. 3D tumor motion correlated with the external surrogate signal, but showed a noticeable phase mismatch. The 3D tumor trajectory showed significant cycle-to-cycle variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, there was significant phase mismatch between surrogate signals obtained from fiducials at different locations.

Correction of hysteretic respiratory motion in SPECT myocardial perfusion imaging: Simulation and patient studies.

Amplitude-based respiratory gating is known to capture the extent of respiratory motion (RM) accurately but results in residual motion in the presence of respiratory hysteresis. In our previous study, we proposed and developed a novel approach to account for respiratory hysteresis by applying the Bouc-Wen (BW) model of hysteresis to external surrogate signals of anterior/posterior motion of the abdomen and chest with respiration. In this work, using simulated and clinical SPECT myocardial perfusion imaging (MPI) studies, we investigate the effects of respiratory hysteresis and evaluate the benefit of correcting it using the proposed BW model in comparison with the abdomen signal typically employed clinically. The MRI navigator data acquired in free-breathing human volunteers were used in the specially modified 4D NCAT phantoms to allow simulating three types of respiratory patterns: monotonic, mild hysteresis, and strong hysteresis with normal myocardial uptake, and perfusion defects in the anterior, lateral, inferior, and septal locations of the mid-ventricular wall. Clinical scans were performed using a Tc-99m sestamibi MPI protocol while recording respiratory signals from thoracic and abdomen regions using a visual tracking system (VTS). The performance of the correction using the respiratory signals was assessed through polar map analysis in phantom and 10 clinical studies selected on the basis of having substantial RM. In phantom studies, simulations illustrating normal myocardial uptake showed significant differences (P<0.001) in the uniformity of the polar maps between the RM uncorrected and corrected. No significant differences were seen in the polar map uniformity across the RM corrections. Studies simulating perfusion defects showed significantly decreased errors (P<0.001) in defect severity and extent for the RM corrected compared to the uncorrected. Only for the strong hysteretic pattern, there was a significant difference (P<0.001) among the RM corrections. The errors in defect severity and extent for the RM correction using abdomen signal were significantly higher compared to that of the BW (severity=-4.0%, P<0.001; extent=-65.4%, P<0.01) and chest (severity=-4.1%, P<0.001; extent=-52.5%, P<0.01) signals. In clinical studies, the quantitative analysis of the polar maps demonstrated qualitative and quantitative but not statistically significant differences (P=0.73) between the correction methods that used the BW signal and the abdominal signal. This study shows that hysteresis in respiration affects the extent of residual motion left in the RM-binned data, which can impact wall uniformity and the visualization of defects. Thus, there appears to be the potential for improved accuracy in reconstruction in the presence of hysteretic RM with the BW model method providing a possible step in the direction of improvement.

Evaluation of residual abdominal tumour motion in carbon ion gated treatments through respiratory motion modelling

At the Italian National Centre for Oncologic Hadrontherapy (CNAO) patients with upper-abdominal tumours are being treated with carbon ion therapy, adopting the respiratory gating technique in combination with layered rescanning and abdominal compression to mitigate organ motion. Since online imaging of the irradiated volume is not feasible, this study proposes a modelling approach for the estimation of residual motion of the target within the gating window. The model extracts a priori respiratory motion information from the planning 4DCT using deformable image registration (DIR), then combines such information with the external surrogate signal recorded during dose delivery. This provides estimation of a CT volume corresponding to any given respiratory phase measured during treatment. The method was applied for the retrospective estimation of tumour residual motion during irradiation, considering 16 patients treated at CNAO with the respiratory gating protocol. The estimated tumour displacement, calculated with respect to the reference end-exhale position, was always limited (average displacement is 0.32±0.65mm over all patients) and below the maximum motion defined in the treatment plan. This supports the hypothesis of target position reproducibility, which is the crucial assumption in the gating approach. We also demonstrated the use of the model as a simulation tool to establish a patient-specific relationship between residual motion and the width of the gating window. In conclusion, the implemented method yields an estimation of the repeatability of the internal anatomy configuration during gated treatments, which can be used for further studies concerning the dosimetric impact of the estimated residual organ motion.

TH-CD-207A-01: Evaluation of Lung Tumor Motion Management Strategy with Dynamic MRI

Purpose: The purpose of this study is to develop a method to track and examine the correlation between the 3D motion of a lung tumor and an external surrogate with dynamic MRI. Methods: Dynamic MRI was obtained from lung cancer patients. To examine the motion correlation between external surrogates and the tumor, we placed four fiducials on the patient's chest at different locations. We acquired a contiguous multi-slice 2D cine MRI (sagittal) to capture the lung and whole tumor, followed by a two-slice 2D cine MRI (sagittal) to simultaneously track the tumor and fiducials. To extract real-time motion, we first reconstructed a phase-binned 4D-MRI from the multi-slice dataset using body area as the respiratory surrogate and a groupwise registration technique. The reconstructed 4D-MRI provided 3D template tumor volumes. Real-time 3D tumor position was calculated by 3D-2D template matching, registering 3D tumor templates and the cine 2D frames from the two-slice tracking dataset. External surrogate 3D trajectories were derived via matching a 3D geometrical model of the fiducial to image features on the 2D cine (two-slice) tracking datasets. Thus, we could analyze the correlation between the 3D trajectories of the tumor and external fiducials. Results: We tested our method on four lung cancer patients. 3D tumor motion correlated with the external surrogate signal, but showed a noticeable phase mismatch. The 3D tumor trajectory showed significant cycle-to-cycle variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, surrogate signals obtained from fiducials at different locations showed noticeable phase mismatch. Conclusion: Our preliminary data show that external surrogate motion has significant variance in relation to tumor motion. Consequently, surrogate-based therapy should be used with caution. Quantitative evaluation of conventional tumor motion management methods such as the internal-target-volume-based approach as well as external-surrogate-based gating, are underway. This work was supported by NIH/NCI under grant R21CA178455.

Respiratory gating based on internal electromagnetic motion monitoring during stereotactic liver radiation therapy: First results

ABSTRACTBackground. Intrafraction motion may compromise the target dose in stereotactic body radiation therapy (SBRT) of tumors in the liver. Respiratory gating can improve the treatment delivery, but gating based on an external surrogate signal may be inaccurate. This is the first paper reporting on respiratory gating based on internal electromagnetic monitoring during liver SBRT.Material and methods. Two patients with solitary liver metastases were treated with respiratory-gated SBRT guided by three implanted electromagnetic transponders. The treatment was delivered in end-exhale with beam-on when the centroid of the three transponders deviated less than 3 mm [left-right (LR) and anterior-posterior (AP) directions] and 4mm [cranio-caudal (CC)] from the planned position. For each treatment fraction, log files were used to determine the transponder motion during beam-on in the actual gated treatments and in simulated treatments without gating. The motion was used to reconstruct the dose to the clinical target volume (CTV) with and without gating. The reduction in D95 (minimum dose to 95% of the CTV) relative to the plan was calculated for both treatment courses.Results. With gating the maximum course mean (standard deviation) geometrical error in any direction was 1.2 mm (1.8 mm). Without gating the course mean error would mainly increase for Patient 1 [to -2.8 mm (1.6 mm) (LR), 7.1 mm (5.8 mm) (CC), −2.6 mm (2.8mm) (AP)] due to a large systematic cranial baseline drift at each fraction. The errors without gating increased only slightly for Patient 2. The reduction in CTV D95 was 0.5% (gating) and 12.1% (non-gating) for Patient 1 and 0.3% (gating) and 1.7% (non-gating) for Patient 2. The mean duty cycle was 55%.Conclusion. Respiratory gating based on internal electromagnetic motion monitoring was performed for two liver SBRT patients. The gating added robustness to the dose delivery and ensured a high CTV dose even in the presence of large intrafraction motion.

WE-D-303-02: Applications of Volumetric Images Generated with a Respiratory Motion Model Based On An External Surrogate Signal

Purpose: Respiratory motion can vary significantly over the course of simulation and treatment. Our goal is to use volumetric images generated with a respiratory motion model to improve the definition of the internal target volume (ITV) and the estimate of delivered dose. Methods: Ten irregular patient breathing patterns spanning 35 seconds each were incorporated into a digital phantom. Ten images over the first five seconds of breathing were used to emulate a 4DCT scan, build the ITV, and generate a patient-specific respiratory motion model which correlated the measured trajectories of markers placed on the patients’ chests with the motion of the internal anatomy. This model was used to generate volumetric images over the subsequent thirty seconds of breathing. The increase in the ITV taking into account the full 35 seconds of breathing was assessed with ground-truth and model-generated images. For one patient, a treatment plan based on the initial ITV was created and the delivered dose was estimated using images from the first five seconds as well as ground-truth and model-generated images from the next 30 seconds. Results: The increase in the ITV ranged from 0.2 cc to 6.9 cc for the ten patients based on ground-truth information. The model predicted this increase in the ITV with an average error of 0.8 cc. The delivered dose to the tumor (D95) changed significantly from 57 Gy to 41 Gy when estimated using 5 seconds and 30 seconds, respectively. The model captured this effect, giving an estimated D95 of 44 Gy. Conclusion: A respiratory motion model generating volumetric images of the internal patient anatomy could be useful in estimating the increase in the ITV due to irregular breathing during simulation and in assessing delivered dose during treatment. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc. and Radiological Society of North America Research Scholar Grant #RSCH1206.

Reconstruction of four-dimensional computed tomography lung images by applying spatial and temporal anatomical constraints using a Bayesian model.

Current four-dimensional computed tomography (4-D CT) lung image reconstruction methods rely on respiratory gating, such as surrogate, to sort the large number of axial images captured during multiple breathing cycles into serial three-dimensional CT images of different respiratory phases. Such sorting methods may be subject to external surrogate signal noises due to poor reproducibility of breathing cycles. New image-matching-based reconstruction algorithms refine the 4-D CT reconstruction by matching neighboring image slices, and they generally work better for the cine mode of 4-D CT acquisition than the helical mode due to different table positions of axial images in the helical mode. We propose a Bayesian model (BM) based automated 4-D CT lung image reconstruction for helical mode scans. BM allows for applying new spatial and temporal anatomical constraints in the optimization procedure. Using an iterative optimization procedure, each axial image is assigned to a respiratory phase to make sure the anatomical structures are spatially and temporally smooth based on the BM framework. In experiments, we visually and quantitatively compared the results of the proposed BM-based 4-D CT reconstruction with the respiratory surrogate and the normalized cross-correlation based image matching method using both simulated and actual 4-D patient scans. The results indicated that the proposed algorithm yielded more accurate reconstruction and fewer artifacts in the 4-D CT image series.

Generation of fluoroscopic 3D images with a respiratory motion model based on an external surrogate signal

Respiratory motion during radiotherapy can cause uncertainties in definition of the target volume and in estimation of the dose delivered to the target and healthy tissue. In this paper, we generate volumetric images of the internal patient anatomy during treatment using only the motion of a surrogate signal. Pre-treatment four-dimensional CT imaging is used to create a patient-specific model correlating internal respiratory motion with the trajectory of an external surrogate placed on the chest. The performance of this model is assessed with digital and physical phantoms reproducing measured irregular patient breathing patterns. Ten patient breathing patterns are incorporated in a digital phantom. For each patient breathing pattern, the model is used to generate images over the course of thirty seconds. The tumor position predicted by the model is compared to ground truth information from the digital phantom. Over the ten patient breathing patterns, the average absolute error in the tumor centroid position predicted by the motion model is 1.4 mm. The corresponding error for one patient breathing pattern implemented in an anthropomorphic physical phantom was 0.6 mm. The global voxel intensity error was used to compare the full image to the ground truth and demonstrates good agreement between predicted and true images. The model also generates accurate predictions for breathing patterns with irregular phases or amplitudes.

Using an external surrogate for predictor model training in real-time motion management of lung tumors.

Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable to overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal-external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz). A linear prediction model can reduce latency induced tracking errors by an average of about 50% in real-time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.

Adaptation of the modified Bouc-Wen model to compensate for hysteresis in respiratory motion for the list-mode binning of cardiac SPECT and PET acquisitions: testing using MRI.

Binning list-mode acquisitions as a function of a surrogate signal related to respiration has been employed to reduce the impact of respiratory motion on image quality in cardiac emission tomography (SPECT and PET). Inherent in amplitude binning is the assumption that there is a monotonic relationship between the amplitude of the surrogate signal and respiratory motion of the heart. This assumption is not valid in the presence of hysteresis when heart motion exhibits a different relationship with the surrogate during inspiration and expiration. The purpose of this study was to investigate the novel approach of using the Bouc-Wen (BW) model to provide a signal accounting for hysteresis when binning list-mode data with the goal of thereby improving motion correction. The study is based on the authors' previous observations that hysteresis between chest and abdomen markers was indicative of hysteresis between abdomen markers and the internal motion of the heart. In 19 healthy volunteers, they determined the internal motion of the heart and diaphragm in the superior-inferior direction during free breathing using MRI navigators. A visual tracking system (vts) synchronized with MRI acquisition tracked the anterior-posterior motions of external markers placed on the chest and abdomen. These data were employed to develop and test the Bouc-Wen model by inputting the vts derived chest and abdomen motions into it and using the resulting output signals as surrogates for cardiac motion. The data of the volunteers were divided into training and testing sets. The training set was used to obtain initial values for the model parameters for all of the volunteers in the set, and for set members based on whether they were or were not classified as exhibiting hysteresis using a metric derived from the markers. These initial parameters were then employed with the testing set to estimate output signals. Pearson's linear correlation coefficient between the abdomen, chest, average of chest and abdomen markers, and Bouc-Wen derived signals versus the true internal motion of the heart from MRI was used to judge the signals match to the heart motion. The results show that the Bouc-Wen model generated signals demonstrated strong correlation with the heart motion. This correlation was slightly larger on average than that of the external surrogate signals derived from the abdomen marker, and average of the abdomen and chest markers, but was not statistically significantly different from them. The results suggest that the proposed model has the potential to be a unified framework for modeling hysteresis in respiratory motion in cardiac perfusion studies and beyond.

Investigation of a Respiratory Motion Model Based on an External Surrogate Signal With a Digital Phantom and Realistic Patient Breathing

Investigation of a Respiratory Motion Model Based on an External Surrogate Signal With a Digital Phantom and Realistic Patient Breathing

SU-E-J-228: Dose Accumulation Studies with a Dynamic Physical Anthropomorphic Phantom and An External Surrogate-Based Motion Model

Purpose: To estimate the dose delivered to a physical anthropomorphic phantom based on: 1) CT scans representing each phase of respiration; and 2) 3D images generated from a respiratory motion model and an external surrogate signal. OSLDs are used to measure doses delivered to the phantom. Methods: A commercially available physical phantom was modified, replacing the lung system with foam rubber of lung-equivalent density and placing a tumor made of bolus within the lung. A wooden diaphragm driven by a programmable motor compressed the foam with a realistic breathing pattern based on patient measurements. CT scans of the phantom were taken at several phases of a breathing cycle, and the dose delivered by a nine-field treatment at each phase was calculated with Monte Carlo. Dose distributions at each phase were mapped to a reference phase with vectors from deformable image registration performed on the original CT images. A second estimate of the delivered dose was performed replacing the CT scans and associated vectors with images and deformations generated by a motion model. Finally, with a one-field treatment plan, the estimated delivered dose was compared to measurements with OSLDs placed in the phantom. Results: The estimated dose delivered to the tumor using CT scans agreed with the estimate using model-generated images, and the difference in the D95 for the two approaches was less than 2%. This demonstrates that images generated by the motion model can be used for dose estimates. Dose measured with OSLDs at nine points within the tumor and foam lung of the phantom agreed with predictions within measurement uncertainties. Conclusion: The images generated from a motion model based on an external surrogate trace can be used to estimate dose delivered during treatment. Dose estimates were validated with measurements.

SU‐E‐J‐73: Generation of Volumetric Images with a Respiratory Motion Model Based On An External Surrogate Signal

Purpose:Respiratory motion during radiotherapy treatment can differ significantly from motion observed during imaging for treatment planning. Our goal is to use an initial 4DCT scan and the trace of an external surrogate marker to generate 3D images of patient anatomy during treatment.Methods:Deformable image registration is performed on images from an initial 4DCT scan. The deformation vectors are used to develop a patient‐specific linear relationship between the motion of each voxel and the trajectory of an external surrogate signal. Correlations in motion are taken into account with principal component analysis, reducing the number of free parameters. This model is tested with digital phantoms reproducing the breathing patterns of ten measured patient tumor trajectories, using five seconds of data to develop the model and the subsequent thirty seconds to test its predictions. The model is also tested with a breathing physical anthropomorphic phantom programmed to reproduce a patient breathing pattern.Results:The error (mean absolute, 95th percentile) over 30 seconds in the predicted tumor centroid position ranged from (0.8, 1.3) mm to (2.2, 4.3) mm for the ten patient breathing patterns. The model reproduced changes in both phase and amplitude of the breathing pattern. Agreement between prediction and truth over the entire image was confirmed by assessing the global voxel intensity RMS error. In the physical phantom, the error in the tumor centroid position was less than 1 mm for all images.Conclusion:We are able to reconstruct 3D images of patient anatomy with a model correlating internal respiratory motion with motion of an external surrogate marker, reproducing the expected tumor centroid position with an average accuracy of 1.4 mm. The images generated by this model could be used to improve dose calculations for treatment planning and delivered dose estimates.This work was partially funded by a research grant from Varian Medical Systems.

SU‐E‐J‐25: Analysis of Commercial 4DCT Flaws and the Potential Benefits of a New Technique for Irregular Breathing Patients

Purpose:To determine the distribution of inhalation to exhalation breathing amplitudes as measured using a commercial 4DCT and a proposed 4DCT protocol for irregular breathing patients.Methods:External breathing surrogate signals correlating with lung tidal volume were collected using an abdominal bellows for a set of 50 patients. Breathing traces over 5‐minute durations were evaluated and the peak inhalation and exhalation peaks identified. An arbitrary time point was identified and the nearest exhalation peak located within a ±180° gantry rotation period. The times at which the follow‐up inhalation scans occurred were correlated with the couch velocity to determine whether the tumor would be within 180° of that specific peak inhalation given the CT scanner coverage, pitch, rotation period, tumor size, and tumor motion amplitude (defined in units of centimeters per bellows signal). The imaged tumor motion amplitude was based on which inhalation peak would be imaged and the couch location at that time relative to the exhalation peak. This analysis provided a series of measured breathing motion amplitudes, which were compared against the 85th percentile motion, consistent with the proposed 4DCT protocol. Because the 4DCT scans would yield a single amplitude determination for each patient, errors in the amplitude measurement were considered systematic. Therefore, the largest 5% and smallest 5% of the measured amplitudes were selected to identify over‐and under‐determined motion amplitudes.Results:30% of the patients exhibited breathing amplitude overestimations of at least 27%, while 30% showed underestimations of at least 34%.Conclusion:For the majority of patients, the current system provided a high probability of correct breathing motion amplitude measurements. 30% had a significant chance of a systematic over‐ or under‐estimation of tumor motion. The proposed protocol will be immune to such irregularities because the entire breathing trace is used for breathing motion amplitude assessments.

Actively triggered 4d cone‐beam CT acquisition

4d cone-beam computed tomography (CBCT) scans are usually reconstructed by extracting the motion information from the 2d projections or an external surrogate signal, and binning the individual projections into multiple respiratory phases. In this "after-the-fact" binning approach, however, projections are unevenly distributed over respiratory phases resulting in inefficient utilization of imaging dose. To avoid excess dose in certain respiratory phases, and poor image quality due to a lack of projections in others, the authors have developed a novel 4d CBCT acquisition framework which actively triggers 2d projections based on the forward-predicted position of the tumor. The forward-prediction of the tumor position was independently established using either (i) an electromagnetic (EM) tracking system based on implanted EM-transponders which act as a surrogate for the tumor position, or (ii) an external motion sensor measuring the chest-wall displacement and correlating this external motion to the phase-shifted diaphragm motion derived from the acquired images. In order to avoid EM-induced artifacts in the imaging detector, the authors devised a simple but effective "Faraday" shielding cage. The authors demonstrated the feasibility of their acquisition strategy by scanning an anthropomorphic lung phantom moving on 1d or 2d sinusoidal trajectories. With both tumor position devices, the authors were able to acquire 4d CBCTs free of motion blurring. For scans based on the EM tracking system, reconstruction artifacts stemming from the presence of the EM-array and the EM-transponders were greatly reduced using newly developed correction algorithms. By tuning the imaging frequency independently for each respiratory phase prior to acquisition, it was possible to harmonize the number of projections over respiratory phases. Depending on the breathing period (3.5 or 5 s) and the gantry rotation time (4 or 5 min), between ∼90 and 145 projections were acquired per respiratory phase resulting in a dose of ∼1.7-2.6 mGy per respiratory phase. Further dose savings and decreases in the scanning time are possible by acquiring only a subset of all respiratory phases, for example, peak-exhale and peak-inhale only scans. This study is the first experimental demonstration of a new 4d CBCT acquisition paradigm in which imaging dose is efficiently utilized by actively triggering only those projections that are desired for the reconstruction process.