External Surrogates Research Articles

1460: Evaluation of respiratory motion in lung cancer SBRT using 4D CBCT with no external surrogates.

1460: Evaluation of respiratory motion in lung cancer SBRT using 4D CBCT with no external surrogates.

High-speed bioimpedance-based gating system for radiotherapy: Prototype and proof of principle.

To investigate a novel bioimpedance-based respiratory gating system (BRGS) designed for external beam radiotherapy and to evaluate its technical characteristics in comparison with existing similar systems. The BRGS was tested on three healthy volunteers in free breathing and breath-hold patterns under laboratory conditions. Its parameters, including the time delay (TD) between the actual impedance change and the gating signal, temperature drift, root mean square (RMS) noise, and signal-to-noise ratio (SNR), were measured and analyzed. The gate-on TD and the gate-off TD were found to be 9.0±2.0ms [mean±standard deviation (M±SD)] and 7.2±1.3ms, respectively. The temperature drift of the BRGS output signal was 0.02 Ω after 30 min of operation. RMS noise averaged 0.14±0.05 Ω (M±SD) for all subjects and varied from 0.08 to 0.20 Ω with repeated measurements. A significant difference in SNR (p<0.001) was observed between subjects, ranging from 4 to 15. The evaluated bioimpedance-based gating system showed a high performance in real-time respiratory monitoring and may potentially be used as an external surrogate guidance for respiratory-gated external beam radiotherapy. Direct comparison with commercially available systems, 4D correlation studies, and expansion of the patient sample are goals for future preclinical studies.

Prospective Randomized Trial Comparing 2 Devices for Deep Inspiration Breath Hold Management in Breast Radiation Therapy: Results of the BRAVEHeart Trial

Aims: The BRAVEHeart trial prospectively randomized left-sided breast cancer patients to one of two deep inspiration breath hold (DIBH) biofeedback devices: a novel chest surface tracking system and an abdominal block tracking system. The primary hypothesis was that the accuracy of chest tracking would be higher than that of abdominal tracking as the chest is a more direct surrogate of the breast target.Methods: Left-sided breast cancer patients were treated in DIBH with IMRT delivery. Patients were randomized to either the novel chest surface system or abdominal block system for active management of breath hold with visual feedback. On both trial arms the unallocated system monitored passively.239,296 cine EPID images were analyzed retrospectively to extract the chest wall position. Treatment accuracy was quantified as the deviation of the internal chest wall during treatment relative to the planned position from the digitally reconstructed radiograph. The correlation between motion of the external surrogate and internal chest wall was calculated per breath hold. Ease of use was assessed with questionnaires for both radiation therapists and patients and appointment length recorded.Results: Data from 26 participants were available for analysis. No difference was found in delivered treatment accuracy between arms. Across all patients and fractions, the median correlation between internal chest wall movement and external surrogate was 0.69 for the chest surface and 0.17 for the abdominal block. Patients found it easy to follow visual feedback from both systems. No difference was found in appointment length between arms.Conclusions: No statistical evidence was found for superior treatment accuracy, satisfaction or appointment length for the novel chest surface tracking device compared to the abdominal block system. During DIBH, the median per-breath hold correlation of internal chest wall movement to the motion of the chest surface was higher than the median correlation of the abdominal block to the chest surface.

The first clinical implementation of real-time 6 degree-of-freedom image-guided radiotherapy for liver SABR patients

PurposeMultiple survey results have identified a demand for improved motion management for liver cancer IGRT. Until now, real-time IGRT for liver has been the domain of dedicated and expensive cancer radiotherapy systems. The purpose of this study was to clinically implement and characterise the performance of a novel real-time 6 degree-of-freedom (DoF) IGRT system, Kilovoltage Intrafraction Monitoring (KIM) for liver SABR patients. Methods/MaterialsThe KIM technology segmented gold fiducial markers in intra-fraction x-ray images as a surrogate for the liver tumour and converted the 2D segmented marker positions into a real-time 6DoF tumour position. Fifteen liver SABR patients were recruited and treated with KIM combined with external surrogate guidance at three radiotherapy centres in the TROG 17.03 LARK multi-institutional prospective clinical trial. Patients were either treated in breath-hold or in free breathing using the gating method. The KIM localisation accuracy and dosimetric accuracy achieved with KIM + external surrogate were measured and the results were compared to those with the estimated external surrogate alone. ResultsThe KIM localisation accuracy was 0.2±0.9 mm (left–right), 0.3±0.6 mm (superior-inferior) and 1.2±0.8 mm (anterior-posterior) for translations and −0.1◦±0.8◦ (left–right), 0.6◦±1.2◦ (superior-inferior) and 0.1◦±0.9◦ (anterior-posterior) for rotations. The cumulative dose to the GTV with KIM + external surrogate was always within 5% of the plan. In 2 out of 15 patients, >5% dose error would have occurred to the GTV and an organ-at-risk with external surrogate alone. ConclusionsThis work demonstrates that real-time 6DoF IGRT for liver can be implemented on standard radiotherapy systems to improve treatment accuracy and safety. The observations made during the treatments highlight the potential false assurance of using traditional external surrogates to assess tumour motion in patients and the need for ongoing improvement of IGRT technologies.

Development of a prediction model for target positioning by using diaphragm waveforms extracted from CBCT projection images.

To develop a prediction model (PM) for target positioning using diaphragm waveforms extracted from CBCT projection images. Nineteen patients with lung cancer underwent orthogonal rotational kV x-ray imaging lasting 70 s. IR markers placed on their abdominal surfaces and an implanted gold marker located nearest to the tumor were considered as external surrogates and the target, respectively. Four different types of regression-based PM were trained using surrogate motions and target positions for the first 60 s, as follows: Scenario A: Based on the clinical scenario, 3D target positions extracted from projection images were used as they were (PMCL ). Scenario B: The short-arc 4D-CBCT waveform exhibiting eight target positions was obtained by averaging the target positions in Scenario A. The waveform was repeated for 60 s (W4D-CBCT ) by adapting to the respiratory phase of the external surrogate. W4D-CBCT was used as the target positions (PM4D-CBCT ). Scenario C: The Amsterdam Shroud (AS) signal, which depicted the diaphragm motion in the superior-inferior direction was extracted from the orthogonal projection images. The amplitude and phase of W4D-CBCT were corrected based on the AS signal. The AS-corrected W4D-CBCT was used as the target positions (PMAS-4D-CBCT ). Scenario D: The AS signal was extracted from single projection images. Other processes were the same as in Scenario C. The prediction errors were calculated for the remaining 10 s. The 3D prediction error within 3mm was 77.3% for PM4D-CBCT , which was 12.8% lower than that for PMCL . Using the diaphragm waveforms, the percentage of errors within 3mm improved by approximately 7% to 84.0%-85.3% for PMAS-4D-CBCT in Scenarios C and D, respectively. Statistically significant differences were observed between the prediction errors of PM4D-CBCT and PMAS-4D-CBCT . PMAS-4D-CBCT outperformed PM4D-CBCT , proving the efficacy of the AS signal-based correction. PMAS-4D-CBCT would make it possible to predict target positions from 4D-CBCT images without gold markers.

Evaluation of tumour motion and internal/External Correlation in lung SABR.

This study aims to analyse lung tumour motion and to investigate the correlation between the internal tumour motion acquired from four-dimensional computed tomography (4DCT) and the motion of an external surrogate. A dataset of 363 4DCT images was analysed. Tumours were classified based on their anatomical lobes. The recorded gross tumour volume (GTV) information included the centroid GTV motion in the superior-inferior (SI), anteroposterior (AP) and left-right (LR) directions, and in three-dimensions (3D). For the internal/external correlation, the RPM surrogate breathing signals of 260 patients were analysed via an in-house script. The external motion was correlated with the 3D centroid motion, and the maximum tumour motion via Spearman's correlation. The effect of tumour volume on the amount of motion was evaluated. The greatest 3D tumour amplitude was found for tumours located in the lower part of the lung, with a maximum of 26.7 mm. The Spearman's correlation of the internal 3D motion was weak in the upper (r = 0.21) and moderate in the middle (r = 0.51) and the lower (r = 0.52) lobes. There was no obvious difference in the correlation coefficients between the maximum tumour displacement and the centroid motion. No correlation was found between the tumour volume and the magnitude of motion. Our results suggest that tumour location can be a good predictor of its motion. However, tumour size is a poor predictor of the motion. This knowledge of the distribution of tumour motion throughout the thorax will be valuable for research areas where motion management is still in its infancy.

A motion model-guided 4D dose reconstruction for pencil beam scanned proton therapy

Objective. 4D dose reconstruction in proton therapy with pencil beam scanning (PBS) typically relies on a single pre-treatment 4DCT (p4DCT). However, breathing motion during the fractionated treatment can vary considerably in both amplitude and frequency. We present a novel 4D dose reconstruction method combining delivery log files with patient-specific motion models, to account for the dosimetric effect of intra- and inter-fractional breathing variability. Approach. Correlation between an external breathing surrogate and anatomical deformations of the p4DCT is established using principal component analysis. Using motion trajectories of a surface marker acquired during the dose delivery by an optical tracking system, deformable motion fields are retrospectively reconstructed and used to generate time-resolved synthetic 4DCTs (‘5DCTs’) by warping a reference CT. For three abdominal/thoracic patients, treated with respiratory gating and rescanning, example fraction doses were reconstructed using the resulting 5DCTs and delivery log files. The motion model was validated beforehand using leave-one-out cross-validation (LOOCV) with subsequent 4D dose evaluations. Moreover, besides fractional motion, fractional anatomical changes were incorporated as proof of concept. Main results. For motion model validation, the comparison of 4D dose distributions for the original 4DCT and predicted LOOCV resulted in 3%/3 mm gamma pass rates above 96.2%. Prospective gating simulations on the p4DCT can overestimate the target dose coverage V95% by up to 2.1% compared to 4D dose reconstruction based on observed surrogate trajectories. Nevertheless, for the studied clinical cases treated with respiratory-gating and rescanning, an acceptable target coverage was maintained with V95% remaining above 98.8% for all studied fractions. For these gated treatments, larger dosimetric differences occurred due to CT changes than due to breathing variations. Significance. To gain a better estimate of the delivered dose, a retrospective 4D dose reconstruction workflow based on motion data acquired during PBS proton treatments was implemented and validated, thus considering both intra- and inter-fractional motion and anatomy changes.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features.

Internal tumor motion is commonly predicted using external respiratory signals. However, the internal/external correlation is complex and patient-specific. The purpose of this study was to develop various models based on the radiomic features of computed tomography (CT) images to predict the accuracy of tumor motion tracking using external surrogates and to find accurate and reliable tracking algorithms. Images obtained from a total of 108 and 71 patients pathologically diagnosed with lung and liver cancers, respectively, were examined. Real-time position monitoring motion was fitted to tumor motion, and samples with fitting errors greater than 2 mm were considered positive. Radiomic features were extracted from internal target volumes of average intensity projections, and cross-validation least absolute shrinkage and selection operator (LassoCV) was used to conduct feature selection. Based on the radiomic features, a total of 26 separate models (13 for the lung and 13 for the liver) were trained and tested. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess performance. Relative standard deviation was used to assess stability. Thirty-three and 22 radiomic features were selected for the lung and liver, respectively. For the lung, the AUC varied from 0.848 (decision tree) to 0.941 [support vector classifier (SVC), logistic regression]; sensitivity varied from 0.723 (extreme gradient boosting) to 0.848 [linear support vector classifier (linearSVC)]; specificity varied from 0.834 (gaussian naive bayes) to 0.936 [multilayer perceptron (MLP), wide and deep (W&D)]; and MLP and W&D had better performance and stability than the median. For the liver, the AUC varied from 0.677 [light gradient boosting machine (Light)] to 0.892 (logistic regression); sensitivity varied from 0.717 (W&D) to 0.862 (MLP); specificity varied from 0.566 (Light) to 0.829 (linearSVC); and logistic regression, MLP, and SVC had better performance and stability than the median. Respiratory-sensitive radiomic features extracted from CT images of lung and liver tumors were proved to contain sufficient information to establish an external/internal motion relationship. We developed a rapid and accurate method based on radiomics to classify the accuracy of monitoring a patient's external surface for lung and liver tumor tracking. Several machine learning algorithms-in particular, MLP-demonstrated excellent classification performance and stability.

Uncertainty-driven determination of target measurement times for indirect tracking validation in adaptive radiotherapy

Objective. Hybrid indirect tumor tracking strategies combine continuous monitoring of surrogate signals with episodic radiographic imaging of the target to check and update their models during the treatment. This validation process is traditionally performed at predetermined and fixed-rate time intervals. This study investigates a new validation procedure based on the real-time uncertainty associated with the predicted target positions. Approach. An adaptive version of a Bayesian method for indirect tracking is developed to simulate different validation processes within a single framework: no validation, regular validation and uncertainty-based validation. While regular validation involves measuring targets at fixed intervals, uncertainty-based validation takes advantage of a key Bayesian feature, which is the real-time confidence information associated with predictions. The validation processes are applied to ground truth breathing signals consisting of a lung target and two different surrogates (one internal, one external). Their impact on prediction accuracy is evaluated with root-mean-square error (RMSE) and incidence of large errors. The number of validation measurements triggered is also examined. Main results. When using the internal surrogate and compared to regular validation, uncertainty-based validation results in significantly better prediction accuracy while using fewer validation measurements: RMSE and fraction of large errors are reduced on average by 12% and 26% respectively, with 36% fewer validation measurements. With the external surrogate, whose correlation with the target is less stable over time, more validation measurements are automatically triggered, which leads to a substantial reduction of prediction errors: RMSE and fraction of large errors are reduced on average by 17% and 28% respectively compared to regular validation. It is also observed that depending on the initial instant, regular validation can result in worse prediction accuracy compared to no validation. Significance. Uncertainty-based validation has the potential to be more efficient and effective than a validation process performed at prescheduled and fixed-rate time intervals.

A study of quantitative indicators for slice sorting in cine-mode 4DCT.

The uncertainties of four-dimensional computed tomography (4DCT), also called as residual motion artefacts (RMA), induced from irregular respiratory patterns can degrade the quality of overall radiotherapy. This study aims to quantify and reduce those uncertainties. A comparative study on quantitative indicators for RMA was performed, and based on this, we proposed a new 4DCT sorting method that is applicable without disrupting the current clinical workflow. In addition to the default phase sorting strategy, both additional amplitude information from external surrogates and the quantitative metric for RMA, investigated in this study, were introduced. The comparison of quantitative indicators and the performance of the proposed sorting method were evaluated via 10 cases of breath-hold (BH) CT and 30 cases of 4DCT. It was confirmed that N-RMSD (normalised root-mean-square-deviation) was best matched to the visual standards of our institute’s regime, manual sorting method, and could accurately represent RMA. The performance of the proposed method to reduce 4DCT uncertainties was improved by about 18.8% in the averaged value of N-RMSD compared to the default phase sorting method. To the best of our knowledge, this is the first study that evaluates RMA indicators using both BHCT and 4DCT with visual-criteria-based manual sorting and proposes an improved 4DCT sorting strategy based on them.

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

Purposes/ObjectivesHistorically, 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 MaterialsThis 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.Results303,123 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).ConclusionDiaphragmatic 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.

Adaptive confidence regions for indirect tracking of moving tumors in radiotherapy

Target motion in the course of radiotherapy is one of the largest factors affecting the treatment quality of highly dynamic sites such as lung. A critical component of real-time motion management is not only the prediction of tumor location at a future point in time but assessment of positional uncertainty for the purposes of margin adjustment and optimization of validationschemes. In this study, we propose to investigate the ability of a confidence estimator to accurately reflect the reliability of individual target position predictions and prospectively detect large prediction errors by relying exclusively on a surrogatesignal. This work uses a Bayesian framework for indirect tracking. While constant covariance estimates are commonly used to express the uncertainty of the models involved, in this study new adaptive estimates are derived from the surrogate behavior to reflect increasing uncertainty when the breathing conditions differ from the reference conditions observed during the training step. The accuracy of the resulting 95% predicted confidence regions (CRs) is evaluated on nine breathing sequences involving changes of respiratory types (free, thoracic, abdominal, deep). The breathing motions are collected simultaneously from a lung target and two different surrogate signals (an external marker and an anatomical location within the liver). Receiver operating characteristic (ROC) analysis is performed to evaluate the ability of the predicted uncertainty to prospectively detect large predictionerrors. Higher CR accuracy is obtained when using the proposed adaptive estimates over using constant estimations: on average over the cohort, the proportion of actual target positions lying within the 95% CR is increased by 40 and 35 p.p. with the internal and external surrogates. The time-dependent inflation of the CR width tends to match the magnitude variation of the prediction errors: the adaptive CR effectively enlarges when the target position cannot be predicted reliably, which corresponds to potentially high prediction errors. More precisely, the ROC analysis indicates that the proposed uncertainty estimate can detect if prediction errors are greater than 5 mm with on average high sensitivity (90%) and modest specificity (54% and 47% from internal and external surrogates, respectively). While relying exclusively on the surrogate motion characteristics being continuously monitored, the Bayesian framework coupled to adaptive uncertainty estimations can provide reliable CR able to detect large prediction errors. The findings of this study could be further used to automatically trigger risk management mechanisms prospectively.

PO-1589 Gating Device L: an external surrogate for respiratory motion tracking on TrueBeam

PO-1589 Gating Device L: an external surrogate for respiratory motion tracking on TrueBeam

Dosimetric impact of phase shifts on Radixact Synchrony tracking system with patient-specific breathing patterns.

PurposeThe Synchrony tracking system of Radixact is capable of real‐time tumor tracking by building a correlation model between external light‐emitting diodes on the patient's chest and an internal marker. A phase shift between the chest wall and a lung tumor has been reported. Hence, this study focused on evaluating the accuracy of the tracking system, especially under a patient‐specific breathing pattern with respiratory phase shifts.MethodsA phantom containing fiducial markers was placed on a moving platform. The intrinsic delivery accuracy was verified with a patient‐specific breathing pattern. Three patient‐specific breathing patterns were then implemented, for which phase shifts, φ, were introduced. Phase shifts with +0.3 s and +1 s were tested for dosimetric aspects, whereas ±0.3, ±0.6, and ±0.8 s shifts were used for tracking accuracy. The resultant dose distributions were analyzed by γ comparison. Dose profiles in the superior‐inferior and lateral directions were compared. Logfiles of the tracking information were extracted from the system and compared with the input breathing pattern. The root mean square (RMS) difference was used to quantify the consistency.ResultsWhen the φ value was as large as 1 s, a severe inconsistency was observed. The target was significantly underdosed, down to 89% of the originally planned dose. γ analysis revealed that the failed portion was concentrated in the target region. The RMS of the tracking difference was close to 1 mm when φ was ±0.3 s and approximately 4 mm when φ was ±0.8 s. Tracking errors increased with an increase in the degree of phase shifts.ConclusionPhase shifts between the patient chest wall and the internal target may hamper treatment delivery and jeopardize treatment using Synchrony Tracking. Hence, a larger planning target volume (PTV) may be necessary if a large phase shift is observed in a patient, especially when an external surrogate shows a lag in motion when compared with the tumor.

Four-Dimensional Computed Tomography-Based Correlation of Respiratory Motion of Lung Tumors With Implanted Fiducials and an External Surrogate

PurposeOur purpose was to assess the suitability of airway-implanted internal fiducial markers and an external surrogate of respiratory motion for motion management during radiation therapy of lung tumors. Methods and MaterialsWe analyzed 4-dimensional computed tomography scans acquired during radiation therapy simulation for 28 patients with lung tumors who had anchored fiducial markers bronchoscopically implanted inside small airways in or near the tumor in a prospective trial. We used a linear mixed model to build population-based correlative models of tumor and surrogate motion. The first 24 of the 28 patients were used to build correlative models, and 4 of the 28 consecutive patients were excluded and used as an internal validation cohort. Of the 24 patients from the model building cohort, all were used for the models based on the internal fiducial. The external surrogate was completely visualized in 11 patients from the model building cohort, so only those were used for the models based on the external surrogate. Furthermore, we determined the predicted residual error sum of squares for our correlative models, which may serve as benchmarks for future research. ResultsThe motion of the internal fiducials was significantly associated with the tumor motion in the anterior-posterior (P < .0001) and superior-inferior (SI) directions (P < .0001). We also observed a strong correlation of the external surrogate anterior-posterior motion to the tumor dominant SI motion (P < .0001). In the validation cohort, the internal fiducial SI motion was the only reliable predictor of lung tumor motion. ConclusionsThe internal fiducials appear to be more reliable predictors of lung tumor motion than the external surrogate. The suitability of such airway-implanted internal fiducial markers for advanced motion management techniques should be further investigated. Although the external surrogate seems to be less reliable, its wide availability and noninvasive application support its clinical utility, albeit the greater uncertainty will need to be compensated for.

Intrafraction Accuracy and Efficiency of a Surface Imaging System for Deep Inspiration Breath Hold in Ablative Pancreatic Cancer Treatment

Beam gating with deep inspiration breath hold (DIBH) has been widely used for motion management in radiotherapy. It commonly relies on some external surrogate for internal target motion, while the exact internal motion is unknown. In this study, we tracked internal targets and characterized the residual motion during DIBH treatment for pancreatic cancer patients guided by a surface imaging system. We also report the treatment time statistics.Eight consecutive patients with pancreatic cancer treated with surface-guided DIBH volumetrically-modulated arc therapy in 2019-2020 were selected for this study, each with some radiopaque markers (fiducial markers or surgical clips) implanted near or inside the target. Five patients were treated in 25 fractions, two were treated in 15 fractions, and one was treated in 10 fractions. The surface imaging system monitored a three-dimensional surface with gating thresholds of 3 mm translation and 3° rotation. During dose delivery, kV images were automatically taken every 20˚ or 40˚ gantry rotation, from which internal markers were identified. The marker displacement from their initial positions and the residual motion amplitudes were calculated. For the analysis of treatment efficiency, the treatment time of every session was obtained from imaging and treatment timestamps recorded at the treatment console.Even though the external surface was limited to 3 mm / 3°, significant residual internal target motion was observed for some patients. The residual motion ranged 3-21 mm. The average target displacement relative to the initial position was 0-3 mm for the cohort. The magnitude of instantaneous displacement was > 5 mm in 13% of the images analyzed. The average treatment time was 16 min with standard deviation 3 min.Internal target motion may differ significantly from external surrogate motion in DIBH treatment, even with surface image guidance. Radiographic verification of target position at the beginning and during each fraction is essential for accurate delivery of radiotherapy.

The UAE’s ‘dogs of war’: boosting a small state’s regional power projection

ABSTRACT This article suggests based on the case study of the United Arab Emirates (UAE) that mercenaries as commercial surrogates can become an integral part of an overall effort of military transformation helping regimes in the Middle East to increase military capacity and capability on the battlefield. As the most assertive Arab state post-Arab Spring, the UAE arguably shows the greatest discrepancy between ambitiousness of its strategic objectives and available in-house capacity and capability among states in the region. Consequently, despite its ongoing military transformation, the Emirates more than any other Arab state had to inevitably draw on external surrogates to maintain their military presence in Somalia, Yemen, and Libya. Thereby, the case study of the UAE is quite exceptional in the region, as it has set a new trend for the commercialization of military services at the higher end of the military spectrum when translating capital into military capability and capacity. This in turn confronts Abu Dhabi’s western partners with difficult choices as they rely increasingly on the UAE to bear the burden of conflict in the region.

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.

Validation of a CT-based motion model with in-situ fluoroscopy for lung surface deformation estimation

Many surrogate-based motion models (SMMs), proposed to guide motion management in radiotherapy, are constructed by correlating motion of an external surrogate and internal anatomy during CT-simulation. Changes in this correlation define model break down. We validate a methodology that incorporates fluoroscopic (FL) images acquired during treatment for SMM construction and update. Under a prospective IRB, 4DCT scans, VisionRT (VRT) surfaces, and orthogonal FLs were collected from five lung cancer patients. VRT surfaces and two FL time-series were acquired pre- and post-treatment. A simulated annealing optimization scheme was used to estimate optimal lung deformations by maximizing the mutual information (MI) between digitally reconstructed radiographs (DRRs) of the SMM-estimated 3D images and FLs. Our SMM used partial-least-regression and was trained using the optimal deformations and VRT surfaces from the first breathing-cycle. SMM performance was evaluated using the MI score between reference FLs and the corresponding SMM or phase-assigned 4DCT DRRs. The Hausdorff distance for contoured landmarks was used to evaluate target position estimation error. For four out of five patients, two principal components approximated lung surface deformations with submillimeter accuracy. Analysis of the MI score between more than 4000 pairs of FL and DRR demonstrated that our model led to more similarity between the FL and DRR images compared to 4DCT and DRR images from a model based on an a priori correlation model. Our SMM consistently displayed lower mean and 95th percentile Hausdorff distances. For one patient, 95th percentile Hausdorff distance was reduced by 11 mm. Patient-averaged reductions in mean and 95th percentile Hausdorff distances were 3.6 mm and 7 mm for right-lung, and 3.1 mm and 4 mm for left-lung targets. FL data were used to evaluate model performance and investigate the feasibility of model update. Despite variability in breathing, use of post-treatment FL preserved model fidelity and consistently outperformed 4DCT for position estimation.