Validation of a model for physical dose variations in irregularly moving targets treated with carbon ion beams.

PURPOSE: In particle therapy, conventional treatment planning systems rely on an imaging representation of the irradiated region to compute the dose. For irregular breathing, when an imaging dataset describing the actual motion is not available, a different approach for dose estimation is needed. To this aim, we validate a method for the estimation of physical dose variations in gated carbon ion treatments, providing also a demonstration of the feasibility of physical dose metrics to assess the method performance. Finally, we describe a sample use case, in which this method is used to assess plan robustness with respect to undetected irregular tumor motion. METHODS: The method entails the definition of a patient- and beam-specific water equivalent depth (WED) space, the simulation of motion as a translation equal to tumor displacement, and the reconstruction of the altered dose. We validated the approach using four-dimensional computed tomographies (4DCTs) and clinical plans in 12 patients, treated with respiratory gated carbon ion beams at the National Centre for Oncological Hadrontherapy (Pavia, Italy). Using the end-exhale CT and dose distribution as a reference, the physical dose delivered at the end-inhale tumor position was estimated and compared to the ground-truth dose recalculation on the end-inhale CT. Biologically effective and physical dose variations between the plan and the recalculation were compared as well. As a use case, we evaluated dose changes caused by simulated irregular tumor motion, that is, linear and nonlinear baseline shifts and/or amplitude variations with hysteresis. RESULTS: The ratio between biologically effective and physical equivalent uniform dose (EUD) variations due to end-exhale to end-inhale motion was less than one for 96% of investigated structures. In the validation study, we found a median error corresponding to a 14% EUD overestimation for the tumor and 4% EUD underestimation for a subgroup of organs at risk, together with a high EUD variation due to motion [median 352% EUD variation between end-exhale and end-inhale doses in the planning tumor volume (PTV)]. Considering relevant dose-volume histogram (DVH) metrics, the median difference between estimated and ground truth doses was ≤ 4%. Gamma analysis between estimated and recalculated dose distributions resulted in a pass rate > 80% for 83% of the target volumes. For the two patients selected for the sample use case, a patient-specific assessment of the method performance was performed on the 4DCT and it was possible to relate EUD variations of both tumor and organs at risk to the simulated target motion. CONCLUSIONS: The physical dose distribution was found to be more sensitive to motion with respect to the biologically effective one, suggesting the suitability of the physical dose metrics for the WED-space method validation. We showed that the method can compensate for intra-fractional tumor motion with proper accuracy in the selected patient group, although its use is recommended when limited deformations are expected. In conclusion, the WED-space method can provide simulations of dose alteration due to irregular breathing when imaging data are lacking, and, once integrated with relative biological effectiveness (RBE) modeling, it would be useful in evaluating the robustness of carbon ion treatment plans.

A Hybrid Image Registration and Matching Framework for Real-Time Motion Tracking in MRI-Guided Radiotherapy.

OBJECTIVE: MRI-guided radiotherapy (MRIgRT) is an emerging treatment technique where anatomical and pathological structures are imaged through integrated MR-radiotherapy units. This work aims 1) at assessing the accuracy of optical-flow-based motion tracking in liver cine-MRI sequences; and 2) at testing a MRIgRT workflow combining similarity-based image matching with image registration. METHODS: After an initialization stage, a set of template images is collected and registered to the first frame of the cine-MRI sequence. Subsequent incoming frames are either matched to the most similar template image or registered to the first frame when the similarity index is lower than a given threshold. The tracking accuracy was evaluated by considering ground-truth liver landmarks trajectories, as obtained through the scale-invariant features transform (SIFT). RESULTS: Results on a population of 30 liver subjects show that the median difference between SIFT- and optical flow-based landmarks trajectories is 1.0 mm, i.e., lower than the cine-MRI pixel size (1.28 mm). The computational time of the motion tracking workflow (<50 ms) is suitable for real-time motion compensation in MRIgRT. Such time could be further reduced to ≍30 ms with limited loss of accuracy by the combined image matching/registration approach. CONCLUSION: The reported workflow allows us to track liver motion with accuracy comparable to robust feature matching. Its computational time is suitable for online motion monitoring. SIGNIFICANCE: Real-time feedback on the patient anatomy is a crucial requirement for the treatment of mobile tumors using advanced motion mitigation strategies.

Evaluation of target coverage and margins adequacy during CyberKnife Lung Optimized Treatment.

PURPOSE: Evaluation of target coverage and verification of safety margins, in motion management strategies implemented by Lung Optimized Treatment (LOT) module in CyberKnife system. METHODS: Three fiducial-less motion management strategies provided by LOT can be selected according to tumor visibility in the X ray images acquired during treatment. In 2-view modality the tumor is visible in both X ray images and full motion tracking is performed. In 1-view modality the tumor is visible in a single X ray image, therefore, motion tracking is combined with an internal target volume (ITV)-based margin expansion. In 0-view modality the lesion is not visible, consequently the treatment relies entirely on an ITV-based approach. Data from 30 patients treated in 2-view modality were selected providing information on the three-dimensional tumor motion in correspondence to each X ray image. Treatments in 1-view and 0-view modalities were simulated by processing log files and planning volumes. Planning target volume (PTV) margins were defined according to the tracking modality: end-exhale clinical target volume (CTV) + 3 mm in 2-view and ITV + 5 mm in 0-view. In the 1-view scenario, the ITV encompasses only tumor motion along the non-visible direction. Then, non-uniform ITV to PTV margins were applied: 3 mm and 5 mm in the visible and non-visible direction, respectively. We defined the coverage of each voxel of the CTV as the percentage of X ray images where such voxel was included in the PTV. In 2-view modality coverage was calculated as the intersection between the CTV centred on the imaged target position and the PTV centred on the predicted target position, as recorded in log files. In 1-view modality, coverage was calculated as the intersection between the CTV centred on the imaged target position and the PTV centred on the projected predictor data. In 0-view modality coverage was calculated as the intersection between the CTV centred on the imaged target position and the non-moving PTV. Similar to dose-volume histogram, CTV coverage-volume histograms (defined as CVH) were derived for each patient and treatment modality. The geometric coverages of the 90% and 95% of CTV volume (C90, C95, respectively) were evaluated. Patient-specific optimal margins (ensuring C95 ≥ 95%) were computed retrospectively. RESULTS: The median ± interquartile-rage of C90 and C95 for upper lobe lesions was 99.1 ± 0.6% and 99.0 ± 3.1%, whereas they were 98.9 ± 4.2% and 97.8 ± 7.5% for lower and middle lobe tumors. In 2-view, 1-view and 0-view modality, adopted margins ensured C95 ≥ 95% in 70%, 85% and 63% of cases and C95 ≥ 90% in 90%, 88% and 83% of cases, respectively. In 2-view, 1-view and 0-view a reduction in margins still ensured C95 ≥ 95% in 33%, 78% and 59% of cases, respectively. CONCLUSIONS: CTV coverage analysis provided an a-posteriori evaluation of the treatment geometric accuracy and allowed a quantitative verification of the adequacy of the PTV margins applied in CyberKnife LOT treatments offering guidance in the selection of CTV margins.

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

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

Examination of a deformable motion model for respiratory movements and 4D dose calculations using different driving surrogates.

PURPOSE: The aim of this study was to evaluate a surrogate-driven motion model based on four-dimensional computed tomography that is able to predict CT volumes corresponding to arbitrary respiratory phases. Furthermore, the comparison of three different driving surrogates is examined and the feasibility of using the model for 4D dose re-calculation will be discussed. METHODS: The study is based on repeated 4DCTs of twenty patients treated for bronchial carcinoma and metastasis. The motion model was estimated from the planning 4DCT through deformable image registration. To predict a certain phase of a follow-up 4DCT, the model considers inter-fractional variations (baseline correction) and intra-fractional respiratory parameters (amplitude and phase) derived from surrogates. The estimated volumes resulting from the model were compared to ground-truth clinical 4DCTs using absolute HU differences in the lung region and landmarks localized using the Scale Invariant Feature Transform. Finally, the γ-index was used to evaluate the dosimetric effects of the intensity differences measured between the estimated and the ground-truth CT volumes. RESULTS: The results show absolute HU differences between estimated and ground-truth images with median value (± standard deviation) of (61.3 ± 16.7) HU. Median 3D distances, measured on about 400 matching landmarks in each volume, were (2.9 ± 3.0) mm. 3D errors up to 28.2 mm were found for CT images with artifacts or reduced quality. Pass rates for all surrogate approaches were above 98.9% with a γ-criterion of 2%/2 mm. CONCLUSION: The results depend mainly on the image quality of the initial 4DCT and the deformable image registration. All investigated surrogates can be used to estimate follow-up 4DCT phases, however, uncertainties decrease for volumetric approaches. Application of the model for 4D dose calculations is feasible.

Magnetic resonance imaging-guided versus surrogate-based motion tracking in liver radiation therapy: a prospective comparative study.

PURPOSE: This study applied automatic feature detection on cine-magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. METHODS AND MATERIALS: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each feature was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. RESULTS: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57 mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. CONCLUSIONS: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.

Surrogate-driven deformable motion model for organ motion tracking in particle radiation therapy.

The aim of this study is the development and experimental testing of a tumor tracking method for particle radiation therapy, providing the daily respiratory dynamics of the patient's thoraco-abdominal anatomy as a function of an external surface surrogate combined with an a priori motion model. The proposed tracking approach is based on a patient-specific breathing motion model, estimated from the four-dimensional (4D) planning computed tomography (CT) through deformable image registration. The model is adapted to the interfraction baseline variations in the patient's anatomical configuration. The driving amplitude and phase parameters are obtained intrafractionally from a respiratory surrogate signal derived from the external surface displacement. The developed technique was assessed on a dataset of seven lung cancer patients, who underwent two repeated 4D CT scans. The first 4D CT was used to build the respiratory motion model, which was tested on the second scan. The geometric accuracy in localizing lung lesions, mediated over all breathing phases, ranged between 0.6 and 1.7 mm across all patients. Errors in tracking the surrounding organs at risk, such as lungs, trachea and esophagus, were lower than 1.3 mm on average. The median absolute variation in water equivalent path length (WEL) within the target volume did not exceed 1.9 mm-WEL for simulated particle beams. A significant improvement was achieved compared with error compensation based on standard rigid alignment. The present work can be regarded as a feasibility study for the potential extension of tumor tracking techniques in particle treatments. Differently from current tracking methods applied in conventional radiotherapy, the proposed approach allows for the dynamic localization of all anatomical structures scanned in the planning CT, thus providing complete information on density and WEL variations required for particle beam range adaptation.

Geometric and dosimetric accuracy and imaging dose of the real-time tumour tracking system of a gimbal mounted linac.

PURPOSE: To suggest a comprehensive testing scheme to evaluate the geometric and dosimetric accuracy and the imaging dose of the VERO dynamic tumour tracking (DTT) for its clinical implementation. METHODS: Geometric accuracy was evaluated for gantry 0° and 90° in terms of prediction (EP), mechanical (EM) and tracking (ET) errors for sinusoidal patterns with 10 and 20 mm amplitudes, 2-6 s periods and phase shift up to 1 s and for 3 patient patterns. The automatic 4D model update was investigated simulating changes in the breathing pattern during treatment. Dosimetric accuracy was evaluated with gafchromic films irradiated in static and moving phantom with and without DTT. The entrance skin dose (ESD) was assessed using a solid state detector and gafchromic films. RESULTS: The RMS of EP, EM, and ET were up to 0.8, 0.5 and 0.9 mm for all non phased-shifted motion patterns while for the phased-shifted ones, EP and ET increased to 2.2 and 2.6 mm. Up to 4 updates are necessary to restore a good correlation model, according to type of change. For 100 kVp and 1 mA s X-ray beam, the ESD per portal due to 20 s fluoroscopy was 16.6 mGy, while treatment verification at a frequency of 1 Hz contributed with 4.2 mGy/min. CONCLUSIONS: The proposed testing scheme highlighted that the VERO DTT system tracks a moving target with high accuracy. The automatic update of the 4D model is a powerful tool to guarantee the accuracy of tracking without increasing the imaging dose.

Real-time tumor tracking with an artificial neural networks-based method: a feasibility study.

The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy. This motion compensation strategy is currently applied clinically only in conventional photon radiotherapy but not in particle therapy, as greater accuracy in dose delivery is required. We proposed a tracking method that exploits artificial neural networks to estimate the internal tumor trajectory as a function of external surrogate signals. The developed algorithm was tested by means of a retrospective clinical data analysis in 20 patients, who were treated with state of the art infra-red motion tracking for photon radiotherapy, which is used as a benchmark. Integration into a hardware platform for motion tracking in particle therapy was performed and then tested on a moving phantom, specifically developed for this purpose. Clinical data show that a median tracking error reduction up to 0.7 mm can be achieved with respect to state of the art technologies. The phantom study demonstrates that a real-time tumor position estimation is feasible when the external signals are acquired at 60 Hz. The results of this work show that neural networks can be considered a valuable tool for the implementation of high accuracy real-time tumor tracking methodologies.