A feasibility study on the prediction of tumour location in the lung from skin motion

Review of robotic systems for thoracoabdominal puncture interventional surgery

Cancer, with high morbidity and high mortality, is one of the major burdens threatening human health globally. Intervention procedures via percutaneous puncture have been widely used by physicians due to its minimally invasive surgical approach. However, traditional manual puncture intervention depends on personal experience and faces challenges in terms of precisely puncture, learning-curve, safety and efficacy. The development of puncture interventional surgery robotic (PISR) systems could alleviate the aforementioned problems to a certain extent. This paper attempts to review the current status and prospective of PISR systems for thoracic and abdominal application. In this review, the key technologies related to the robotics, including spatial registration, positioning navigation, puncture guidance feedback, respiratory motion compensation, and motion control, are discussed in detail.

Association of Internal and External Motion Based on Cine MR Images Acquired During Real-Time Treatment on MRI-Guided Linear Accelerator for Patients With Lung Cancer

Purpose

With the recent clinical implementation of magnetic resonance imaging (MRI)-guided linear accelerators, a large number of real-time planar MR images has been acquired during lung cancer treatment as a standard of care. In this study, associations among lung tumor, diaphragm, and external skin movement were studied based on MR cine imaging during the entire duration of each treatment fraction.

Methods and Materials

This retrospective study used 181,798 planar MRI frames acquired over 55 treatment/imaging sessions of 13 patients with lung cancer treated on 2 MRI-guided linear accelerators. From each planar MR image frame, in-house software automatically extracted 9 features: the superior-interior (SI) and posterior-anterior (PA) positions of a lung tumor; the area of the lung (Lung_Area); the posterior (Dia_Post), dome/apex (Dia_Dome), and anterior (Dia_Ant) points of a diaphragmatic curve; the diaphragm curve point (Dia_Max); and the chest (Chest) and belly (Belly) skin points experienced the maximum range of motions. Correlation analyses were performed among the 9 features for every session. Lung tumor motion range and standard deviations were calculated based on positions obtained in cine images and compared with motion ranges obtained from 4-dimensional computed tomography images.

Results

In the study, 177,009 frames of images were successfully analyzed. For all patients, correlation coefficients were as follows: 0.91 ± 0.10 between any 2 features among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; 0.82 ± 0.21 between SI and any feature among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; 0.75 ± 0.24 between SI and Belly. Six of 13 patients were considered large amplitude motion (patients with lung tumor SI motion standard deviation >5 mm). Furthermore, 92,956 frames of images were analyzed for the 6 large-amplitude motion patients. For this set, correlation coefficients were 0.93 ± 0.07 between any 2 features among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; 0.94 ± 0.06 between SI and any feature among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; and 0.90 ± 0.09 between SI and Belly.

Conclusions

Both belly and diaphragmatic motions as assessed by cine MRI are highly correlated with large amplitude lung tumor motion in the longitudinal axis.

A Patient-Specific correspondence model to track tumor location in thorax during radiation therapy

PURPOSE

We present a patient-specific model to estimate tumor location in the thorax during radiation therapy using chest surface displacement as the surrogate signal.

METHODS

Two types of data are used for model construction: Four-dimensional computed tomography (4D-CT) images of the patient and the displacement of two points on the patient's skin on the thoracic area. Principal component analysis is used to fit the correspondence model. This model incorporates the recorded surrogate signals during radiation delivery as input and delivers the 3D trajectory of the tumor as output. We evaluated the accuracy of the proposed model on a respiratory phantom and five lung cancer patients.

RESULTS

For the respiratory phantom, the location of the center of the sphere during treatment was calculated in three directions: Left-Right (LR), Anterior-Posterior (AP) and, Superior-Inferior (SI). The error of localization was less than 1 mm in the LR and AP directions and less than 2 mm in the SI direction. The location of the tumor center for two of the patients, and the location of the apex of the diaphragm for the other three, was calculated in three directions. For all patients, the localization error in the LR and AP directions was less than 1.1 mm for two fractions and the maximum localization error in the SI direction was 6.4 mm.

CONCLUSIONS

This work presents a feasibility study of utilizing surface displacement data to locate the tumor in the thorax during radiation treatment. Future work will validate the model on a larger patient population.

X-ray source arrays for volumetric imaging during radiotherapy treatment

This work presents a novel hardware configuration for radiotherapy systems to enable fast 3D X-ray imaging before and during treatment delivery. Standard external beam radiotherapy linear accelerators (linacs) have a single X-ray source and detector located at ± 90° from the treatment beam respectively. The entire system can be rotated around the patient acquiring multiple 2D X-ray images to create a 3D cone-beam Computed Tomography (CBCT) image before treatment delivery to ensure the tumour and surrounding organs align with the treatment plan. Scanning with a single source is slow relative to patient respiration or breath holds and cannot be performed during treatment delivery, limiting treatment delivery accuracy in the presence of patient motion and excluding some patients from concentrated treatment plans that would be otherwise expected to have improved outcomes. This simulation study investigated whether recent advances in carbon nanotube (CNT) field emission source arrays, high frame rate (60 Hz) flat panel detectors and compressed sensing reconstruction algorithms could circumvent imaging limitations of current linacs. We investigated a novel hardware configuration incorporating source arrays and high frame rate detectors into an otherwise standard linac. We investigated four potential pre-treatment scan protocols that could be achieved in a 17 s breath hold or 2-10 1 s breath holds. Finally, we demonstrated for the first time volumetric X-ray imaging during treatment delivery by using source arrays, high frame rate detectors and compressed sensing. Image quality was assessed quantitatively over the CBCT geometric field of view as well as across each axis through the tumour centroid. Our results demonstrate that source array imaging enables larger volumes to be imaged with acquisitions as short as 1 s albeit with reduced image quality arising from lower photon flux and shorter imaging arcs.

Association Between Internal Organ/Liver Tumor and External Surface Motion From Cine MR Images on an MRI-Linac

Purposes/Objectives

Historically, motion correlation between internal tumor and external surrogates have been based on limited sets of X-ray or magnetic resonance (MR) images. With the recent clinical implementation of MR-guided linear accelerators, a vast quantity of continuous planar real-time MR imaging data is acquired. In this study, information was extracted from MR cine imaging during liver cancer treatments to establish associations between internal tumor/diaphragm and external surface/skin movement.

Methods and Materials

This retrospective study used 305,644 MR image frames acquired over 118 treatment/imaging sessions of the first 23 liver cancer patients treated on an MRI-linac. 9 features were automatically determined on each MR image frame: Lung_Area, the posterior (Dia_Post), dome (Dia_Dome), and anterior (Dia_Ant) points of a diaphragmatic curve and the diaphragm curve point (Dia_Max), the chest (Chest) and the belly (Belly) skin points experiencing the maximum motion ranges; the superior-interior (SI) and posterior-anterior (PA) positions of a target. For every session, correlation analyses were performed twice among the 9 features: 1) over a breath-hold (BH) set and 2) on a pseudo free-breathing (PFB) generated by removing breath-holding frames.

Results

303,123 frames of images were successfully analyzed. For BH set analysis, correlation coefficients were as follows: 0.94 ± 0.07 between any two features among Dia_Post, Dia_Dome, Dia_Max, and Lung_Area; 0.95 ± 0.06 between SI and any feature among Dia_Post, Dia_Dome, Dia_Max, or Lung_Area; 0.76 ± 0.29 between SI and Belly (with 50% of correlations ≥ 0.87). The PFB set had 142,862 frames of images. For this set, correlation coefficients were 0.96 ± 0.06 between any two features among Dia_Post, Dia_Dome, Dia_Max, and Lung_Area; 0.95 ± 0.06 between SI and any feature among Dia_Post, Dia_Dome, Dia_Max, or Lung_Area; 0.80 ± 0.26 between SI and Belly (with 50% of correlations ≥ 0.91).

Conclusion

Diaphragmatic motion as assessed by cine MR imaging is highly correlated with liver tumor motion. Belly vertical motion is highly correlated with liver tumor longitudinal motion in approximately half of the cases. More detailed analyses of those cases displaying weak correlations are in progress.

Tracking target/chest relationship changes during motion‐synchronized tomotherapy treatments

Background

Radixact Synchrony® is an intrafraction motion tracking system for helical tomotherapy treatments that uses kV radiographs of the target and LEDs on the patient's chest to synchronize the movement of the radiation beam with the respiratory motion of the target. Several works have demonstrated Synchrony's ability to track target motion when the chest and target motions are perfectly correlated.

Purpose

The purpose of this work was to determine Synchrony's ability to accurately adapt to scenarios with a changing target/chest correlation.

Methods

A custom ion chamber mimicking plug with embedded fiducials was placed inside a Delta4 Phantom+ and used as the tracking object. A separate motion stage was programmed to mimic chest motion. The target and chest surrogate phantom were programmed to move sinusoidally and two types of target/chest relationship changes were introduced: rigid shifts and linear drifts of the target position but not surrogate position. Tracking analysis was performed by comparing programmed phantom motion to log files of the Synchrony‐modeled motion. No dosimetry was performed in this work.

Results

At the fastest imaging rate of 2 s/img, Synchrony accurately adapted for gradual drifts in the target location (up to 5 mm/min) with minor increases in tracking errors and adapted for an abrupt 5 mm shift after about 30 s (with an auto‐pause threshold at 60 s). When the imaging period was longer (> 4 s/img), larger tracking errors (> 5 mm) were observed, and the treatment would be paused. The measured delta (MD) parameter (2D target localization error on the most recent image) was found to be a more responsive indicator of tracking errors than the potential difference (PD) parameter (3D estimator of tracking error based on all images in the model). Lastly, the effect of a recent update to the tracking algorithm was found to improve the ability of Synchrony to track target/chest relationship changes.

Conclusions

This work demonstrated that Synchrony can adapt to gradual changes (drifts) in the target/chest relationship, but it takes a finite amount of time to adapt to abrupt shifts. Ability to adapt to these changes increases with increasing imaging frequency. Larger tracking errors were observed in this work than others have reported in the literature due to the introduction of target/chest correlation changes in this work. Future work needs to be performed investigating what type and magnitude of target/chest miscorrelations occur in patients. Lastly, users should ensure they are using the most recent software (3.0.1 or newer) to improve the ability of Synchrony to track these movements.

Development and Performance Evaluation of Wearable Respiratory Self-Training System Using Patch Type Magnetic Sensor

Purpose

Respiratory training system that can be used by patients themselves was developed with a micro-electro-mechanical-system (MEMS)-based patch-type magnetic sensor. We conducted a basic function test and clinical usability evaluation to determine the system’s clinical applicability.

Methods

The system is designed with a sensor attached to the patient’s chest and a magnet on the back to monitor the patient’s respiration by measuring changes in magnetic intensity related to respiratory movements of the thoracic surface. The system comprises a MEMS-based patch-type magnetic sensor capable of wireless communication and being applied to measurement magnets and mobile applications. System performance was evaluated by the level of systemic noise, the precision of the sensor in various breathing patterns, how measurement signals change for varying distances, or the presence or absence of material between the sensor and the magnet. Various breathing patterns were created using the QUASAR respiratory motion phantom; the data obtained were analyzed using the fitting and peak value analysis methods.

Results

The sensor had a noise ratio of <0.54% of the signal; the average errors in signal amplitude and period for breathing patterns were 78.87 um and 72 ms, respectively. The signal could be measured consistently when the sensor–magnet distance was 10–25 cm. The signal difference was 1.89% for the presence or absence of a material, indicating that its influence on the measurement signal is relatively small.

Conclusion

The potential of our MEMS-based patch-type wearable respiratory self-training system was confirmed via basic function tests and clinical usability evaluations. We believe that the training system could provide thorough respiratory training for patients after a clinical trial with actual patients confirming its clinical efficacy and usability.

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

Purpose.Respiration-induced motion introduces significant positioning uncertainties in radiotherapy treatments for thoracic sites. Accounting for this motion is a non-trivial task commonly addressed with surrogate-based strategies and latency compensating techniques. This study investigates the potential of a new unified probabilistic framework to predict both future target motion in real-time from a surrogate signal and associated uncertainty.Method.A Bayesian approach is developed, based on a Kalman filter theory adapted specifically for surrogate measurements. Breathing motions are collected simultaneously from a lung target, two external surrogates (abdominal and thoracic markers) and an internal surrogate (liver structure) for 9 volunteers during 4 min, in which severe breathing changes occur to assess the robustness of the method. A comparison with an artificial non-linear neural network (NN) is performed, although no confidence interval prediction is provided. A static worst-case scenario and a simple static design are investigated.Results.Although the NN can reduce the prediction errors from thoracic surrogate in some cases, the Bayesian framework outperforms in most cases the NN when using the other surrogates: bias on predictions is reduced by 38% and 16% on average when using respectively the liver and the abdomen for the simple scenario, and by respectively 40% and 31% for the worst-case scenario. The standard deviation of residuals is reduced on average by up to 42%. The Bayesian method is also found to be more robust to increasing latencies. The thoracic marker appears to be less reliable to predict the target position, while the liver shows to be a better surrogate. A statistical test confirms the significance of both observations.Conclusion.The proposed framework predicts both the future target position and the associated uncertainty, which can be valuably used to further assist motion management decisions. Further investigation is required to improve the predictions by using an adaptive version of the proposed framework.

Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part I: Intra-fraction breathing motion management

PURPOSE

The POP-ART RT study aims to determine to what extent and how intra-fractional real-time respiratory motion management (RRMM) and plan adaptation for inter-fractional anatomical changes (ART), are used in clinical practice and to understand barriers to implementation. Here we report on part I: RRMM.

MATERIAL AND METHODS

A questionnaire was distributed worldwide to assess current clinical practice, wishes for expansion or new implementation and barriers to implementation. RRMM was defined as inspiration/expiration gating in free-breathing or breath-hold, or tracking where the target and the beam are continuously realigned.

RESULTS

The questionnaire was completed by 200 centres from 41 countries. RRMM was used by 68% of respondents ('users') for a median (range) of 2 (1-6) tumour sites. Eighty-one percent of users applied inspiration breath-hold in at least one tumour site (breast: 96%). External marker was used to guide RRMM by 61% of users. KV/MV imaging was frequently used for liver and pancreas (with fiducials) and for lung (with or without fiducials). Tracking was mainly performed on robotic linacs with hybrid internal-external monitoring. For breast and lung, approximately 75% of respondents used or wished to implement RRMM, which was lower for liver (44%) and pancreas (27%). Seventy-one percent of respondents wished to implement RRMM for a new tumour site. Main barriers were human/financial resources and capacity on the machine.

CONCLUSION

Sixty-eight percent of respondents used RRMM and 71% wished to implement RRMM for a new tumour site. The main barriers to implementation were human/financial resources and capacity on treatment machines.

Technical Note: Correction for the effect of breathing variations in CT pulmonary ventilation imaging

PURPOSE

The accuracy and precision of computed tomography (CT) pulmonary ventilation imaging with conventional CT scanners are limited by breathing variations. We propose a method to correct for the effect of breathing variations in CT ventilation imaging based on external respiratory signals acquired throughout a scan.

METHODS

The proposed method is based on: (a) calculating voxel-by-voxel abdominal surface motion ranges using four-dimensional (4D) CT image datasets spatiotemporally correlated with external respiratory monitor data, and (b) applying the correction factor, which is defined as the ratio of the overall mean of the abdominal surface motion range in the lungs to that of each voxel, to the CT ventilation value. The performance of the proposed method was investigated by comparing voxel-wise correlations of the uncorrected and corrected CT ventilation images with single-photon emission CT (SPECT) ventilation images as a ground truth for nine patients. CT ventilation images were calculated by deformable image registration of the 4D-CT image datasets, followed by calculation of regional volume changes. A Steiger's Z-test was used to determine the statistical significance of the difference between the correlations for the uncorrected and corrected CT ventilation images.

RESULTS

The proposed correction method resulted in significant increases (P < 0.05) in the correlation between CT and SPECT ventilation in three patients, trends toward significant increase (P: 0.13-0.18) in two patients, no significant differences in three patients, and a significantly decreased correlation in one patient. The average standard deviation of the abdominal surface motion range in three patients showing significant increases was 0.27 (range 0.10-0.37), which was greater than 0.17 (range 0.07-0.38) in the other six patients.

CONCLUSIONS

The proposed method to correct for the effect of breathing variations could be readily implemented and has the potential to improve the accuracy of CT ventilation imaging as demonstrated in a nine-patient study.

Motion management strategies and technical issues associated with stereotactic body radiotherapy of thoracic and upper abdominal tumors: A review from NRG oncology

The efficacy of stereotactic body radiotherapy (SBRT) has been well demonstrated. However, it presents unique challenges for accurate planning and delivery especially in the lungs and upper abdomen where respiratory motion can be significantly confounding accurate targeting and avoidance of normal tissues. In this paper, we review the current literature on SBRT for lung and upper abdominal tumors with particular emphasis on addressing respiratory motion and its affects. We provide recommendations on strategies to manage motion for different, patient-specific situations. Some of the recommendations will potentially be adopted to guide clinical trial protocols.

A new scheme for real-time high-contrast imaging in lung cancer radiotherapy: a proof-of-concept study

Visualization of anatomy in real time is of critical importance for motion management in lung cancer radiotherapy. To achieve real-time, and high-contrast in-treatment imaging, we propose a novel scheme based on the measurement of Compton scatter photons. In our method, a slit x-ray beam along the superior-inferior direction is directed to the patient, (intersecting the lung region at a 2D plane) containing most of the tumor motion trajectory. X-ray photons are scattered off this plane primarily due to the Compton interaction. An imager with a pinhole or a slat collimator is placed at one side of the plane to capture the scattered photons. The resulting image, after correcting for incoming fluence inhomogeneity, x-ray attenuation, scatter angle variation, and outgoing beam geometry, represents the linear attenuation coefficient of Compton scattering. This allows the visualization of the anatomy on this plane. We performed Monte Carlo simulation studies both on a phantom and a patient for proof-of-principle purposes. In the phantom case, a small tumor-like structure could be clearly visualized. The contrast-resolution calculated using tumor/lung as foreground/background for kV fluoroscopy, cone beam computed tomography (CBCT), and scattering image were 0.037, 0.70, and 0.54, respectively. In the patient case, tumor motion could be clearly observed in the scatter images. Imaging dose to the voxels directly exposed by the slit beam was ~0.4 times of that under a single CBCT projection. These studies demonstrated the potential feasibility of the proposed imaging scheme to capture the instantaneous anatomy of a patient on a 2D plane with a high image contrast. Clear visualization of the tumor motion may facilitate marker-less tumor tracking.

Technical aspects of real time positron emission tracking for gated radiotherapy

PURPOSE

Respiratory motion can lead to treatment errors in the delivery of radiotherapy treatments. Respiratory gating can assist in better conforming the beam delivery to the target volume. We present a study of the technical aspects of a real time positron emission tracking system for potential use in gated radiotherapy.

METHODS

The tracking system, called PeTrack, uses implanted positron emission markers and position sensitive gamma ray detectors to track breathing motion in real time. PeTrack uses an expectation-maximization algorithm to track the motion of fiducial markers. A normalized least mean squares adaptive filter predicts the location of the markers a short time ahead to account for system response latency. The precision and data collection efficiency of a prototype PeTrack system were measured under conditions simulating gated radiotherapy. The lung insert of a thorax phantom was translated in the inferior-superior direction with regular sinusoidal motion and simulated patient breathing motion (maximum amplitude of motion ±10 mm, period 4 s). The system tracked the motion of a (22)Na fiducial marker (0.34 MBq) embedded in the lung insert every 0.2 s. The position of the was marker was predicted 0.2 s ahead. For sinusoidal motion, the equation used to model the motion was fitted to the data. The precision of the tracking was estimated as the standard deviation of the residuals. Software was also developed to communicate with a Linac and toggle beam delivery. In a separate experiment involving a Linac, 500 monitor units of radiation were delivered to the phantom with a 3 × 3 cm photon beam and with 6 and 10 MV accelerating potential. Radiochromic films were inserted in the phantom to measure spatial dose distribution. In this experiment, the period of motion was set to 60 s to account for beam turn-on latency. The beam was turned off when the marker moved outside of a 5-mm gating window.

RESULTS

The precision of the tracking in the IS direction was 0.53 mm for a sinusoidally moving target, with an average count rate ∼250 cps. The average prediction error was 1.1 ± 0.6 mm when the marker moved according to irregular patient breathing motion. Across all beam deliveries during the radiochromic film measurements, the average prediction error was 0.8 ± 0.5 mm. The maximum error was 2.5 mm and the 95th percentile error was 1.5 mm. Clear improvement of the dose distribution was observed between gated and nongated deliveries. The full-width at halfmaximum of the dose profiles of gated deliveries differed by 3 mm or less than the static reference dose distribution. Monitoring of the beam on/off times showed synchronization with the location of the marker within the latency of the system.

CONCLUSIONS

PeTrack can track the motion of internal fiducial positron emission markers with submillimeter precision. The system can be used to gate the delivery of a Linac beam based on the position of a moving fiducial marker. This highlights the potential of the system for use in respiratory-gated radiotherapy.

Evaluation of respiratory pattern during respiratory-gated radiotherapy

The respiratory cycle is not strictly regular, and generally varies in amplitude and period from one cycle to the next. We evaluated the characteristics of respiratory patterns acquired during respiratory gating treatment in more than 300 patients. A total 331 patients treated with respiratory-gated carbon-ion beam therapy were selected from a group of patients with thoracic and abdominal conditions. Respiratory data were acquired for a total of 3,171 fractions using an external respiratory sensing monitor and evaluated for respiratory cycle, duty cycle, magnitude of baseline drift, and intrafractional/interfractional peak inhalation/exhalation positional variation. Results for the treated anatomical sites and patient positioning were compared. Mean ± SD respiratory cycle averaged over all patients was 4.1 ± 1.3 s. Mean ± SD duty cycle averaged over all patients was 36.5 ± 7.3 %. Two types of baseline drift were seen, the first decremental and the second incremental. For respiratory peak variation, the mean intrafractional variation in peak-inhalation position relative to the amplitude in the first respiratory cycle (15.5 ± 9.3 %) was significantly larger than that in exhalation (7.5 ± 4.6 %). Interfractional variations in inhalation (17.2 ± 18.5 %) were also significantly greater than those in exhalation (9.4 ± 10.0 %). Statistically significant differences were observed between patients in the supine position and those in the prone position in mean respiratory cycle, duty cycle, and intra-/interfractional variations. We quantified the characteristics of the respiratory curve based on a large number of respiratory data obtained during treatment. These results might be useful in improving the accuracy of respiratory-gated treatment.

A new respiratory monitoring and processing system based on Wii remote: proof of principle

PURPOSE

To create a patient respiratory management system and patient self-practice tool using the Wii remote, a widely available consumer hardware product.

METHODS

The Wii remote (Wiimote) (Nintendo, Redmond, WA) contains an infrared (IR) camera that can track up to four spots whose coordinates are reported to a host computer via Bluetooth. The Wiimote is capable of tracking a fiducial box currently used by a commercial monitoring system [Real-time Position Management(TM) (RPM) system, Varian Associates, Palo Alto, CA], if the correct IR source is used. The authors validated the Wiimote tracking by comparing the amplitude and frequency of signals among those reported by Wiimote with known movements from an inhouse servo-driven respiratory simulator, as well as with those measured using the RPM. The simulator comparison was done using standard sinusoid signals with amplitude of 2.0 cm as well as recorded patient respiratory traces. The RPM comparisons were done by simultaneously recording the RPM reflective box position with the Wiimote and the RPM. Timing was compared between these two systems by using the digital beam-on signal from the CT scanner, for the 4DCT to synchronize these acquisitions.

RESULTS

The data acquisition rate from the Wiimote was 100.0 ± 0.4 Hz with a version 2.1 Bluetooth adaptor. The standard deviation of the height of the motion extrema was 0.06 and 1.1 mm when comparing those measured by the Wiimote and the servomotor encoder for standard sinusoid signal and prerecorded patient respiratory signal, respectively. The standard deviation of the amplitude of motion extrema between the Wiimote and RPM was 0.9 mm and the timing difference was 253 ms.

CONCLUSION

The performance of Wiimote shows promise for respiratory monitoring for its faster sampling rate as well as the potential optical and GPU abilities. If used with care it can deliver reasonable spatial and temporal accuracy.

Tracking lung tumors in orthogonal X-rays

This paper presents a computationally very efficient, robust, automatic tracking method that does not require any implanted fiducials for low-contrast tumors. First, it generates a set of motion hypotheses and computes corresponding feature vectors in local windows within orthogonal-axis X-ray images. Then, it fits a regression model that maps features to 3D tumor motions by minimizing geodesic distances on motion manifold. These hypotheses can be jointly generated in 3D to learn a single 3D regression model or in 2D through back projection to learn two 2D models separately. Tumor is tracked by applying regression to the consecutive image pairs while selecting optimal window size at every time. Evaluations are performed on orthogonal X-ray videos of 10 patients. Comparative experimental results demonstrate superior accuracy (~1 pixel average error) and robustness to varying imaging artifacts and noise at the same time.

Optimized order estimation for autoregressive models to predict respiratory motion

PURPOSE

To successfully ablate moving tumors in robotic radio-surgery, it is necessary to compensate for motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in the CyberKnife[Formula: see text] Synchrony system. Tracking errors, originating from system immanent time delays, are typically reduced by time series prediction. Many prediction algorithms exploit autoregressive (AR) properties of the signal. Estimating the optimal model order [Formula: see text] for these algorithms constitutes a challenge often solved via grid search or prior knowledge about the signal.

METHODS

Aiming at a more efficient approach instead, this study evaluates the Akaike information criterion (AIC), the corrected AIC, and the Bayesian information criterion (BIC) on the first minute of the respiratory signal. Exemplarily, we evaluated the approach for a least mean square (LMS) and a wavelet-based LMS (wLMS) predictor.

RESULTS

Analyzing 12 motion traces, orders estimated by AIC had the highest prediction accuracy for both prediction algorithms. Extending the investigations to 304 real motion traces, the prediction error of wLMS using AIC was found to decrease significantly by 85.1 % of the data compared to the original implementation

CONCLUSIONS

The overall results suggest that using AIC to estimate the model order [Formula: see text] for prediction algorithms based on AR properties is a valid method which avoids intensive grid search and leads to high prediction accuracy.

Extracting respiratory signals from thoracic cone beam CT projections

The patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such a signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principal component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely the Amsterdam Shroud method, the intensity analysis method and the Fourier-transform-based phase analysis method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting a respiratory signal. We also identified the applicability of each existing method.

Evaluation of the potential of multi-modal sensors for respiratory motion prediction and correlation

In modern robotic radiotherapy, precise radiation of moving tumors is possible by tracking external optical surrogates. The surrogates are used to compensate for time delays and to predict internal landmarks using a correlation model. The correlation depends significantly on the surrogate position and breathing characteristics of the patient. In this context, we aim to increase the accuracy and robustness of prediction and correlation models by using a multi-modal sensor setup. Here, we evaluate the correlation coefficient of a strain belt, an acceleration and temperature sensor (air flow) with respect to external optical sensors and one internal landmark in the liver, measured by 3D ultrasound. The focus of this study is the influence of breathing artefacts, like coughing and harrumphing. Evaluating seven subjects, we found a strong decrease of the correlation for all modalities in case of artefacts. The results indicate that no precise motion compensation during these times is possible. Overall, we found that apart from the optical markers, the strain belt and temperature sensor data show the best correlation to external and internal motion.

Reproducibility of four-dimensional computed tomography-based lung ventilation imaging

RATIONALE AND OBJECTIVES

A novel ventilation imaging method based on four-dimensional (4D) computed tomography (CT) has been applied to the field of radiation oncology. Understanding its reproducibility is a prerequisite for clinical applications. The purpose of this study was to quantify the reproducibility of 4D CT ventilation imaging over different days and the same session.

MATERIALS AND METHODS

Two ventilation images were created from repeat 4D CT scans acquired over the average time frames of 15 days for 6 lung cancer patients and 5 minutes for another 6 patients. The reproducibility was quantified using the voxel-based Spearman rank correlation coefficients for all lung voxels and Dice similarity coefficients (DSC) for the spatial overlap of segmented high-, moderate-, and low-functional lung volumes. Furthermore, the relationship between the variation in abdominal motion range as a measure of the depth of breathing and variation in ventilation was evaluated using linear regression.

RESULTS

The voxel-based correlation between the two ventilation images was moderate on average (0.50 ± 0.15). The DSCs were also moderate for the high- (0.60 ± 0.08), moderate- (0.46 ± 0.06), and low-functional lung (0.58 ± 0.09). No patients demonstrated strong correlations. The relationship between the motion range variation and ventilation variation was found to be moderate and significant.

CONCLUSIONS

We investigated the reproducibility of 4D CT ventilation imaging over the time frames of 15 days and 5 minutes and found that it was only moderately reproducible. Respiratory variation during 4D CT scans was found to deteriorate the reproducibility. Improvement of 4D CT imaging is necessary to increase the reproducibility of 4D CT ventilation imaging.

Potential underestimation of the internal target volume (ITV) from free-breathing CBCT

PURPOSE

Localization prior to delivery of SBRT to free-breathing patients is performed by aligning the planning internal target volume (ITV) from 4DCT with an on-board free-breathing cone-beam CT (FB-CBCT) image. The FB-CBCT image is assumed to also generate an ITV that captures the full range of motion, due to the acquisition spanning multiple respiratory cycles. However, the ITV could potentially be underestimated when the ratio of time spent in inspiration versus time spent in expiration (I/E ratio) deviates from unity. Therefore, the aim of this study was to investigate the effect of variable I/E ratios on the FB ITV generated from a FB-CBCT scan.

METHODS

This study employed both phantom and patient imaging data. For the phantom study, five periodic respiratory cycles were simulated with different I/E ratios. Six patient respiratory cycles with variable I/E ratios were also selected. All profiles were then programmed into a motion phantom for imaging and modified to exhibit three peak-to-peak motion amplitudes (0.5, 1.0, and 2.0 cm). Each profile was imaged using two spherical targets with 1.0 and 3.0 cm diameters. 2D projections were acquired with full gantry rotation of a kiloVoltage (kV) imager mounted onto the gantry of a modem linear accelerator. CBCT images were reconstructed from 2D projections using a standard filtered back-projection reconstruction algorithm. Quantitative analyses for the phantom study included computing the change in contrast along the direction of target motion as well as determining the area (which is proportional to the target volume) inside of the contour extracted using a Canny edge detector. For the patient study, projection data that were previously acquired under an investigational 4D CBCT slow-gantry imaging protocol were used to generate both FB-CBCT and 4D CBCT images. Volumes were then manually contoured from both datasets (using the same window and level) for quantitative comparison.

RESULTS

The phantom study indicated a reduction in contrast at the inferior edge of the ITV (corresponding to inspiration) as the ratio decreased, for both simulated and patient respiratory cycles. For the simulated phantom respiratory cycles, the contrast reduction of the smallest I/E ratio was 27.6% for the largest target with the smallest amplitude and 89.7% for the smallest target with the largest amplitude. For patient respiratory cycles, these numbers were 22.3% and 94.0%, respectively. The extracted area from inside of the target contours showed a decreasing trend as the I/E ratio decreased. In the patient study, the FB-CBCT ITVs of both lung tumors studied were underestimated when compared with their corresponding 4D CBCT ITV. The underestimations found were 40.1% for the smaller tumor and 24.2% for the larger tumor.

CONCLUSIONS

The ITV may be underestimated in a FB-CBCT image when a patient's respiratory pattern is characterized by a disparate length of time spent in inspiration versus expiration. Missing the full target motion information during on-board verification imaging may result in localization errors.

A simple respiratory indicator for irradiation during voluntary breath holding: a one-touch device without electronic materials

PURPOSE

To evaluate the use, structural principles, operation, and acquired reproducibility of a respiratory monitoring device to be used for voluntary patient breath holding.

MATERIALS AND METHODS

Evaluation was performed of a respiratory monitoring device that enables determination of the respiratory level in a patient by measuring the movement of two contacts on the abdomen and chest wall. Neither metallic nor electronic materials are used in the mechanics for this device. The initial study group comprised 21 consecutive patients (15 men, six women; mean age, 75 years; range, 56-92 years) with lung or abdominal tumors who underwent examination with the device and computed tomography (CT) for three-dimensional reproducibility of lung base position during voluntary breath holding with or without use of the device.

RESULTS

One patient with mild dementia was excluded; in most of the remaining 20 patients, high reproducibility of the breath-holding position was achieved in a short time with the device. In these 20 patients who were able to adapt to use of the device, three-dimensional mean maximum differences in lung base position during three random voluntary breath holds were 2.0 mm along the cranial-caudal axis, 1.5 mm along the anterior-posterior axis, and 1.2 mm along the right-left axis. The differences in all axes were significantly smaller with use of the respiratory monitoring device than without the device.

CONCLUSION

The device demonstrates satisfactory reproducibility of voluntary patient breath holding easily and inexpensively and may offer a convenient device for easy use during irradiation with voluntary breath-holding conditions that require a small internal margin.

Forecasting respiratory motion with accurate online support vector regression (SVRpred)

OBJECT

To accurately deliver radiation in image-guided robotic radiosurgery, highly precise prediction algorithms are required. A new prediction method is presented and evaluated.

MATERIALS AND METHODS

SVRpred, a new prediction method based on support vector regression (SVR), has been developed and tested. Computer-generated data mimicking human respiratory motion with a prediction horizon of 150 ms was used for lab tests. The algorithm was subsequently evaluated on a respiratory motion signal recorded during actual radiosurgical treatment using the CyberKnife. The algorithm's performance was compared to the MULIN prediction methods and Wavelet-based multi scale autoregression (wLMS).

RESULTS

The SVRpred algorithm clearly outperformed both the MULIN and the wLMS algorithms on both real (by 15 and 16 percentage points, respectively) and noise-corrupted simulated data (by 13 and 48 percentage points, respectively). Only on noise-free artificial data, the SVRpred algorithm did perform as well as the MULIN algorithms but not as well as the wLMS algorithm.

CONCLUSION

This new algorithm is a feasible tool for the prediction of human respiratory motion signals significantly outperforming previous algorithms. The only drawback is the high computational complexity and the resulting slow prediction speed. High performance computers will be needed to use the algorithm in live prediction of signals sampled at a high resolution.

Lung tumor tracking in fluoroscopic video based on optical flow

Respiratory gating and tumor tracking for dynamic multileaf collimator delivery require accurate and real-time localization of the lung tumor position during treatment. Deriving tumor position from external surrogates such as abdominal surface motion may have large uncertainties due to the intra- and interfraction variations of the correlation between the external surrogates and internal tumor motion. Implanted fiducial markers can be used to track tumors fluoroscopically in real time with sufficient accuracy. However, it may not be a practical procedure when implanting fiducials bronchoscopically. In this work, a method is presented to track the lung tumor mass or relevant anatomic features projected in fluoroscopic images without implanted fiducial markers based on an optical flow algorithm. The algorithm generates the centroid position of the tracked target and ignores shape changes of the tumor mass shadow. The tracking starts with a segmented tumor projection in an initial image frame. Then, the optical flow between this and all incoming frames acquired during treatment delivery is computed as initial estimations of tumor centroid displacements. The tumor contour in the initial frame is transferred to the incoming frames based on the average of the motion vectors, and its positions in the incoming frames are determined by fine-tuning the contour positions using a template matching algorithm with a small search range. The tracking results were validated by comparing with clinician determined contours on each frame. The position difference in 95% of the frames was found to be less than 1.4 pixels (approximately 0.7 mm) in the best case and 2.8 pixels (approximately 1.4 mm) in the worst case for the five patients studied.

The use of CT density changes at internal tissue interfaces to correlate internal organ motion with an external surrogate

The purpose of this paper is to describe a non-invasive method to monitor the motion of internal organs affected by respiration without using external markers or spirometry, to test the correlation with external markers, and to calculate any time shift between the datasets. Ten lung cancer patients were CT scanned with a GE LightSpeed Plus 4-Slice CT scanner operating in a ciné mode. We retrospectively reconstructed the raw CT data to obtain consecutive 0.5 s reconstructions at 0.1 s intervals to increase image sampling. We defined regions of interest containing tissue interfaces, including tumour/lung interfaces that move due to breathing on multiple axial slices and measured the mean CT number versus respiratory phase. Tumour motion was directly correlated with external marker motion, acquired simultaneously, using the sample coefficient of determination, r(2). Only three of the ten patients showed correlation higher than r(2) = 0.80 between tumour motion and external marker position. However, after taking into account time shifts (ranging between 0 s and 0.4 s) between the two data sets, all ten patients showed correlation better than r(2) = 0.8. This non-invasive method for monitoring the motion of internal organs is an effective tool that can assess the use of external markers for 4D-CT imaging and respiratory-gated radiotherapy on a patient-specific basis.

Real-time tumor tracking with preprogrammed dynamic multileaf-collimator motion and adaptive dose-rate regulation

The authors have developed a new method for real-time tumor tracking with dynamic multileaf-collimator (MLC) motion under condition of free breathing. Unlike other previously proposed tumor-tracking methods, their new method uses a preprogrammed dynamic MLC sequence in combination with real-time dose-rate control. This new scheme circumvents the technical challenge in MLC-based tumor tracking of having to control the MLC motion in real time, based on real-time detected tumor motion. With their new method, the movement of the tumor, as a function of breathing phase, amplitude, or tidal volume, is reflected in the preprogrammed MLC sequence. The irregularity of breathing during treatment is handled by real-time regulation of the machine dose rate, which effectively speeds up or slows down the delivery of radiation as needed. This method is based on the fact that all of the parameters in dynamic radiation delivery, including MLC motion, are enslaved to the cumulative dose, which, in turn, can be accelerated or decelerated by varying the dose rate. Because commercially available MLC systems do not allow the MLC delivery sequence to be modified in real time based on the patient's breathing signal, previously proposed tumor-tracking techniques using a MLC cannot be readily implemented in the clinic today. By using a preprogrammed MLC sequence to handle the required motion, the task for real-time control is greatly simplified. With their new scheme, which they call dose-rate-regulated tracking (DRRT), it is possible to use existing linear accelerators that have dynamic MLC capability to achieve real-time tumor tracking, provided that the beam dose rate can be controlled externally. Tracking-error evaluation for 13 patients out of 14 resulted in a tracking error of less than 1 mm (1 sigma), if the effect of the response time of the treatment machine on the dose-rate modulation can be neglected. Film measurements on a moving phantom with variable breathing patterns and DRRT delivery showed that 97% of the measurement points have gamma values less than 1 (for 3% and 2-mm criteria), while non-DRRT delivery showed only 87%. This study shows that real-time tracking is feasible with DRRT even when the patient breathing frequency is irregular. Effects of the variation of breathing amplitude and of base line drift on the tracking error with DRRT are discussed; pending further study, a criterion is suggested for patient selection in the application of this new technique in the clinic.

Inference of hysteretic respiratory tumor motion from external surrogates: a state augmentation approach

It is important to monitor tumor movement during radiotherapy. Respiration-induced motion affects tumors in the thorax and abdomen (in particular, those located in the lung region). For image-guided radiotherapy (IGRT) systems, it is desirable to minimize imaging dose, so external surrogates are used to infer the internal tumor motion between image acquisitions. This process relies on consistent correspondence between the external surrogate signal and the internal tumor motion. Respiratory hysteresis complicates the external/internal correspondence because two distinct tumor positions during different breathing phases can yield the same external observation. Previous attempts to resolve this ambiguity often subdivided the data into inhale/exhale stages and restricted the estimation to only one of these directions. In this study, we propose a new approach to infer the internal tumor motion from external surrogate signal using state augmentation. This method resolves the hysteresis ambiguity by incorporating higher-order system dynamics. It circumvents the segmentation of the internal/external trajectory into different phases, and estimates the inference map based on all the available external/internal correspondence pairs. Optimization of the state augmentation is investigated. This method generalizes naturally to adaptive on-line algorithms.

Real-time prediction of respiratory motion based on local regression methods

Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths.

A deformable lung tumor tracking method in fluoroscopic video using active shape models: a feasibility study

A dynamic multi-leaf collimator (DMLC) can be used to track a moving target during radiotherapy. One of the major benefits for DMLC tumor tracking is that, in addition to the compensation for tumor translational motion, DMLC can also change the aperture shape to conform to a deforming tumor projection in the beam's eye view. This paper presents a method that can track a deforming lung tumor in fluoroscopic video using active shape models (ASM) (Cootes et al 1995 Comput. Vis. Image Underst. 61 38-59). The method was evaluated by comparing tracking results against tumor projection contours manually edited by an expert observer. The evaluation shows the feasibility of using this method for precise tracking of lung tumors with deformation, which is important for DMLC-based real-time tumor tracking.

Investigation of the dosimetric effect of respiratory motion using four-dimensional weighted radiotherapy

We have developed a four-dimensional weighted radiotherapy (4DW-RT) technique. This method involves designing the motion of the linear accelerator beam to coincide with the tumour motion determined from 4D-CT imaging while including a weighting factor to account for irregular motion and limitations of the delivery system. Experiments were conducted with a moving phantom to assess limitations of the delivery system when applying this method. Although the multi-leaf collimator motion remains within the tolerance of the linear accelerator, the extent of motion was less than 1 mm larger than the designed one, and there was a net system latency of approximately 0.2 s. The dose distributions were measured and simulated using different weighting factors and motion scenarios. The breathing characteristics (period, extent of motion, drift and standard deviations) of 32 patients were evaluated using the Varian RPM system. Breathing variability was assessed by plotting the average breathing motion as a function of the breathing phase. Simulations were carried out to determine the optimal weighting factor based on typical patient breathing characteristics. These results establish that the 4DW-RT method demonstrates potential for dose escalation without increasing exposure to healthy tissue.

Dual-component model of respiratory motion based on the periodic autoregressive moving average (periodic ARMA) method

A new approach to the problem of modelling and predicting respiration motion has been implemented. This is a dual-component model, which describes the respiration motion as a non-periodic time series superimposed onto a periodic waveform. A periodic autoregressive moving average algorithm has been used to define a mathematical model of the periodic and non-periodic components of the respiration motion. The periodic components of the motion were found by projecting multiple inhale-exhale cycles onto a common subspace. The component of the respiration signal that is left after removing this periodicity is a partially autocorrelated time series and was modelled as an autoregressive moving average (ARMA) process. The accuracy of the periodic ARMA model with respect to fluctuation in amplitude and variation in length of cycles has been assessed. A respiration phantom was developed to simulate the inter-cycle variations seen in free-breathing and coached respiration patterns. At +/-14% variability in cycle length and maximum amplitude of motion, the prediction errors were 4.8% of the total motion extent for a 0.5 s ahead prediction, and 9.4% at 1.0 s lag. The prediction errors increased to 11.6% at 0.5 s and 21.6% at 1.0 s when the respiration pattern had +/-34% variations in both these parameters. Our results have shown that the accuracy of the periodic ARMA model is more strongly dependent on the variations in cycle length than the amplitude of the respiration cycles.

Geometric uncertainty of 2D projection imaging in monitoring 3D tumor motion

The purpose of this study was to investigate the accuracy of two-dimensional (2D) projection imaging methods in three-dimensional (3D) tumor motion monitoring. Many commercial linear accelerator types have projection imaging capabilities, and tumor motion monitoring is useful for motion inclusive, respiratory gated or tumor tracking strategies. Since 2D projection imaging is limited in its ability to resolve the motion along the imaging beam axis, there is unresolved motion when monitoring 3D tumor motion. From the 3D tumor motion data of 160 treatment fractions for 46 thoracic and abdominal cancer patients, the unresolved motion due to the geometric limitation of 2D projection imaging was calculated as displacement in the imaging beam axis for different beam angles and time intervals. The geometric uncertainty to monitor 3D motion caused by the unresolved motion of 2D imaging was quantified using the root-mean-square (rms) metric. Geometric uncertainty showed interfractional and intrafractional variation. Patient-to-patient variation was much more significant than variation for different time intervals. For the patient cohort studied, as the time intervals increase, the rms, minimum and maximum values of the rms uncertainty show decreasing tendencies for the lung patients but increasing for the liver and retroperitoneal patients, which could be attributed to patient relaxation. Geometric uncertainty was smaller for coplanar treatments than non-coplanar treatments, as superior-inferior (SI) tumor motion, the predominant motion from patient respiration, could be always resolved for coplanar treatments. Overall rms of the rms uncertainty was 0.13 cm for all treatment fractions and 0.18 cm for the treatment fractions whose average breathing peak-trough ranges were more than 0.5 cm. The geometric uncertainty for 2D imaging varies depending on the tumor site, tumor motion range, time interval and beam angle as well as between patients, between fractions and within a fraction.

Tumor and normal tissue motion in the thorax during respiration: Analysis of volumetric and positional variations using 4D CT

PURPOSE

To investigate temporospatial variations of tumor and normal tissue during respiration in lung cancer patients.

METHODS AND MATERIALS

In 14 patients, gross tumor volume (GTV) and normal tissue structures were manually contoured on four-dimensional computed tomography (4D-CT) scans. Structures were evaluated for volume changes, centroid (center of mass) motion, and phase dependence of variations relative to inspiration. Only volumetrically complete structures were used for analysis (lung in 2, heart in 8, all other structures in >10 patients).

RESULTS

During respiration, the magnitude of contoured volumes varied up to 62.5% for GTVs, 25.5% for lungs, and 12.6% for hearts. The range of maximum three-dimensional centroid movement for individual patients was 1.3-24.0 mm for GTV, 2.4-7.9 mm for heart, 5.2-12.0 mm for lungs, 0.3-5.5 mm for skin markers, 2.9-10.0 mm for trachea, and 6.6-21.7 mm for diaphragm. During respiration, the centroid positions of normal structures varied relative to the centroid position of the respective GTV by 1.5-8.1 mm for heart, 2.9-9.3 mm for lungs, 1.2-9.2 mm for skin markers, 0.9-7.1 mm for trachea, and 2.7-16.4 mm for diaphragm.

CONCLUSION

Using 4D-CT, volumetric changes, positional alterations as well as changes in the position of contoured structures relative to the GTV were observed with large variations between individual patients. Although the interpretation of 4D-CT data has considerable uncertainty because of 4D-CT artifacts, observer variations, and the limited acquisition time, the findings might have a significant impact on treatment planning.