The comparative performance of four respiratory motion predictors for real-time tumour tracking

Krauß, A.; Nill, Simeon; Oelfke, Uwe

doi:10.1088/0031-9155/56/16/015

Cited by 81 publications

(142 citation statements)

References 30 publications

(37 reference statements)

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“…(2) numerically stable for the cases of a poorly conditioned X (cf. Krauss et al 8 for more detail). We implement for each motion axis one predictor model, i.e., we do not use principal component analysis (PCA) to build one model for all three motion axis as we do not see the need with this rather simple predictor.…”

Section: Methodsmentioning

confidence: 98%

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

Rottmann

Berbeco

2014

Medical Physics

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

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Section: Methodsmentioning

confidence: 98%

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

Rottmann

Berbeco

2014

Medical Physics

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“…In recent years, several novel approaches have been investigated including neural networks, wavelets and support vector regression (SVR) algorithms [3,4]. In [5], Ernst et al presented one of the most comprehensive studies so far.…”

Section: Introductionmentioning

confidence: 99%

Respiratory Motion Compensation with Relevance Vector Machines

Dürichen

Wissel

Ernst

et al. 2013

Advanced Information Systems Engineering

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Abstract. In modern robotic radiation therapy, tumor movements due to respiration can be compensated. The accuracy of these methods can be increased by time series prediction of external optical surrogates. An algorithm based on relevance vector machines (RVM) is introduced. We evaluate RVM with linear and nonlinear basis functions on a real patient data set containing 304 motion traces and compare it with a wavelet based least mean square algorithm (wLMS), the best algorithm for this data set so far. Linear RVM outperforms wLMS significantly and increases the prediction accuracy for 80.3 % of the data. We show that real time prediction is possible in case of linear RVM and discuss how the predicted variance can be used to construct promising hybrid algorithms, which further reduce the prediction error.

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“…However, the tracking delivery software supports various motion acquisition methods, like implanted electromagnetic transponders 33 and ultrasound transducers 34 . DynaTrack effectively compensates for the additional latency by prediction 35 . The tumor trajectories in this study were generated based on 4DCT data by fitting an ellipse.…”

Section: Discussionmentioning

confidence: 99%

Real‐time 4D dose reconstruction for tracked dynamic MLC deliveries for lung SBRT

et al. 2016

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PurposeThis study provides a proof of concept for real‐time 4D dose reconstruction for lung stereotactic body radiation therapy (SBRT) with multileaf collimator (MLC) tracking and assesses the impact of tumor tracking on the size of target margins.MethodsThe authors have implemented real‐time 4D dose reconstruction by connecting their tracking and delivery software to an Agility MLC at an Elekta Synergy linac and to their in‐house treatment planning software (TPS). Actual MLC apertures and (simulated) target positions are reported to the TPS every 40 ms. The dose is calculated in real‐time from 4DCT data directly after each reported aperture by utilization of precalculated dose‐influence data based on a Monte Carlo algorithm. The dose is accumulated onto the peak‐exhale (reference) phase using energy‐mass transfer mapping. To investigate the impact of a potentially reducible safety margin, the authors have created and delivered treatment plans designed for a conventional internal target volume (ITV) + 5 mm, a midventilation approach, and three tracking scenarios for four lung SBRT patients. For the tracking plans, a moving target volume (MTV) was established by delineating the gross target volume (GTV) on every 4DCT phase. These were rigidly aligned to the reference phase, resulting in a unified maximum GTV to which a 1, 3, or 5 mm isotropic margin was added. All scenarios were planned for 9‐beam step‐and‐shoot IMRT to meet the criteria of RTOG 1021 (3 × 18 Gy). The GTV 3D center‐of‐volume shift varied from 6 to 14 mm.ResultsReal‐time dose reconstruction at 25 Hz could be realized on a single workstation due to the highly efficient implementation of dose calculation and dose accumulation. Decreased PTV margins resulted in inadequate target coverage during untracked deliveries for patients with substantial tumor motion. MLC tracking could ensure the GTV target dose for these patients. Organ‐at‐risk (OAR) doses were consistently reduced by decreased PTV margins. The tracked MTV + 1 mm deliveries resulted in the following OAR dose reductions: lung V 20 up to 3.5%, spinal cord D 2 up to 0.9 Gy/Fx, and proximal airways D 2 up to 1.4 Gy/Fx.ConclusionsThe authors could show that for patient data at clinical resolution and realistic motion conditions, the delivered dose could be reconstructed in 4D for the whole lung volume in real‐time. The dose distributions show that reduced margins yield lower doses to healthy tissue, whilst target dose can be maintained using dynamic MLC tracking.

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The comparative performance of four respiratory motion predictors for real-time tumour tracking

Cited by 81 publications

References 30 publications

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

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

Respiratory Motion Compensation with Relevance Vector Machines

Real‐time 4D dose reconstruction for tracked dynamic MLC deliveries for lung SBRT

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