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

Estimating follow-up CTs from geometric deformations of catheter implants in interstitial breast brachytherapy: A feasibility study using electromagnetic tracking

BACKGROUND

Electromagnetic tracking (EMT) systems have been shown to provide valuable information on the geometry of catheter implants in breast cancer patients undergoing interstitial brachytherapy (iBT). In the context of an extended patient-specific, pre-treatment verification, EMT can play a key role in determining the potential need and, if applicable, the appropriate time for treatment adaptation. To detect dosimetric shortcomings the relative position between catheters, and target volume and critical structures must be known. Since EMT cannot provide the anatomical context and standard imaging techniques such as cone-beam CT are not yet available in most brachytherapy suites, it is not possible to detect anatomic changes on a daily or fraction basis, so the need for adaptive planning cannot be identified.

PURPOSE

The aim of this feasibility study is to develop and evaluate a technique capable of estimating follow-up CTs at any time based on the initial treatment planning CT (PCT) and surrogate information about changes of the implant geometry from an EMT system.

METHODS

A deformation vector field is calculated from two different implant reconstructions acquired in treatment position through EMT, the first immediately after the PCT and the second at another time point during the course of treatment. The calculation is based on discrete displacement vectors of pairs of control and target points. These are extrapolated by means of different radial basis functions in order to cover the entire CT volume. The adequate parameters for the calculation of the deformation field were identified. By warping the PCT according to the deformation field, one obtains an estimated CT (ECT) that reflects the geometric changes. For the proof of concept, ECTs were computed for the time point of the clinical follow-up CT (FCT) that is embedded in the treatment workflow after the fourth fraction.

RESULTS

ECT and clinical FCTs of 20 patients were compared to each other quantitatively in terms of absolute Hounsfield unit differences in the planning target volume (PTV) and in a convex hull (CH) enclosing the catheters. The median differences were 31.2  and 29.5 HU for the CH and the PTV, respectively.

CONCLUSION

The proposed ECT approach was able to approximate the "anatomy of the day" and therefore, in principle, allows a dosimetric appraisal of the treatment plan quality before each fraction. In this way, it can contribute to a more detailed patient-specific quality assurance in iBT of the breast and help to identify the timing for a potential treatment adaptation.

Inter- and intrafractional 4D dose accumulation for evaluating ΔNTCP robustness in lung cancer

BACKGROUND AND PURPOSE

Model-based selection of proton therapy patients relies on a predefined reduction in normal tissue complication probability (NTCP) with respect to photon therapy. The decision is necessarily made based on the treatment plan, but NTCP can be affected when the delivered treatment deviates from the plan due to delivery inaccuracies. Especially for proton therapy of lung cancer, this can be important because of tissue density changes and, with pencil beam scanning, the interplay effect between the proton beam and breathing motion.

MATERIALS AND METHODS

In this work, we verified whether the expected benefit of proton therapy is retained despite delivery inaccuracies by reconstructing the delivered treatment using log-file based dose reconstruction and inter- and intrafractional accumulation. Additionally, the importance of two uncertain parameters for treatment reconstruction, namely deformable image registration (DIR) algorithm and α/β ratio, was assessed.

RESULTS

The expected benefit or proton therapy was confirmed in 97% of all studied cases, despite regular differences up to 2 percent point (p.p.) NTCP between the delivered and planned treatments. The choice of DIR algorithm affected NTCP up to 1.6 p.p., an order of magnitude higher than the effect of α/β ratio.

CONCLUSION

For the patient population and treatment technique employed, the predicted clinical benefit for patients selected for proton therapy was confirmed for 97.0% percent of all cases, although the NTCP based proton selection was subject to 2 p.p. variations due to delivery inaccuracies.

Limitations of phase-sorting based pencil beam scanned 4D proton dose calculations under irregular motion

Objective.4D dose calculation (4DDC) for pencil beam scanned (PBS) proton therapy is typically based on phase-sorting of individual pencil beams onto phases of a single breathing cycle 4DCT. Understanding the dosimetric limitations and uncertainties of this approach is essential, especially for the realistic treatment scenario with irregular free breathing motion.Approach.For three liver and three lung cancer patient CTs, the deformable multi-cycle motion from 4DMRIs was used to generate six synthetic 4DCT(MRI)s, providing irregular motion (11/15 cycles for liver/lung; tumor amplitudes ∼4-18 mm). 4DDCs for two-field plans were performed, with the temporal resolution of the pencil beam delivery (4-200 ms) or with 8 phases per breathing cycle (500-1000 ms). For the phase-sorting approach, the tumor center motion was used to determine the phase assignment of each spot. The dose was calculated either using the full free breathing motion or individually repeating each single cycle. Additionally, the use of an irregular surrogate signal prior to 4DDC on a repeated cycle was simulated. The CTV volume with absolute dose differences >5% (V_dosediff>5%) and differences in CTVV_95%andD_5%-D_95%compared to the free breathing scenario were evaluated.Main results.Compared to 4DDC considering the full free breathing motion with finer spot-wise temporal resolution, 4DDC based on a repeated single 4DCT resulted inV_dosediff>5%of on average 34%, which resulted in an overestimation ofV_95%up to 24%. However, surrogate based phase-sorting prior to 4DDC on a single cycle 4DCT, reduced the averageV_dosediff>5%to 16% (overestimationV_95%up to 19%). The 4DDC results were greatly influenced by the choice of reference cycle (V_dosediff>5%up to 55%) and differences due to temporal resolution were much smaller (V_dosediff>5%up to 10%).Significance.It is important to properly consider motion irregularity in 4D dosimetric evaluations of PBS proton treatments, as 4DDC based on a single 4DCT can lead to an underestimation of motion effects.

NuTracker: a coordinate-based neural network representation of lung motion for intrafraction tumor tracking with various surrogates in radiotherapy

Objective. Tracking tumors and surrounding tissues in real-time is critical for reducing errors and uncertainties during radiotherapy. Existing methods are either limited by the linear representation or scale poorly with the volume resolution. To address both issues, we propose a novel coordinate-based neural network representation of lung motion to predict the instantaneous 3D volume at arbitrary spatial resolution from various surrogates: patient surface, fiducial marker, and single kV projection.Approach. The proposed model, namely NuTracker, decomposes the 4DCT into a template volume and dense displacement fields (DDFs), and uses two coordinate neural networks to predict them from spatial coordinates and surrogate states. The predicted template is spatially warped with the predicted DDF to produce the deformed volume for a given surrogate state. The nonlinear coordinate networks enable representing complex motion at infinite resolution. The decomposition allows imposing different regularizations on the spatial and temporal domains. The meta-learning and multi-task learning are used to train NuTracker across patients and tasks, so that commonalities and differences can be exploited. NuTracker was evaluated on seven patients implanted with markers using a leave-one-phase-out procedure.Main results. The 3D marker localization error is 0.66 mm on average and <1 mm at 95th-percentile, which is about 26% and 32% improvement over the predominant linear methods. The tumor coverage and image quality are improved by 5.7% and 11% in terms of dice and PSNR. The difference in the localization error for different surrogates is small and is not statistically significant. Cross-population learning and multi-task learning contribute to performance. The model tolerates surrogate drift to a certain extent.Significance. NuTracker can provide accurate estimation for entire tumor volume based on various surrogates at infinite resolution. It is of great potential to apply the coordinate network to other imaging modalities, e.g. 4DCBCT and other tasks, e.g. 4D dose calculation.

Report of AAPM Task Group 290: Respiratory motion management for particle therapy

Dose uncertainty induced by respiratory motion remains a major concern for treating thoracic and abdominal lesions using particle beams. This Task Group report reviews the impact of tumor motion and dosimetric considerations in particle radiotherapy, current motion‐management techniques, and limitations for different particle‐beam delivery modes (i.e., passive scattering, uniform scanning, and pencil‐beam scanning). Furthermore, the report provides guidance and risk analysis for quality assurance of the motion‐management procedures to ensure consistency and accuracy, and discusses future development and emerging motion‐management strategies. This report supplements previously published AAPM report TG76, and considers aspects of motion management that are crucial to the accurate and safe delivery of particle‐beam therapy. To that end, this report produces general recommendations for commissioning and facility‐specific dosimetric characterization, motion assessment, treatment planning, active and passive motion‐management techniques, image guidance and related decision‐making, monitoring throughout therapy, and recommendations for vendors. Key among these recommendations are that: (1) facilities should perform thorough planning studies (using retrospective data) and develop standard operating procedures that address all aspects of therapy for any treatment site involving respiratory motion; (2) a risk‐based methodology should be adopted for quality management and ongoing process improvement.

Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy

Shape and location organ variability induced by respiration constitutes one of the main challenges during dose delivery in radiotherapy. Providing up-to-date volumetric information during treatment can improve tumor tracking, thereby increasing treatment efficiency and reducing damage to healthy tissue. We propose a novel probabilistic model to address the problem of volumetric estimation with scalable predictive horizon from image-based surrogates during radiotherapy treatments, thus enabling out-of-plane tracking of targets. This problem is formulated as a conditional learning task, where the predictive variables are the 2D surrogate images and a pre-operative static 3D volume. The model learns a distribution of realistic motion fields over a population dataset. Simultaneously, a seq-2-seq inspired temporal mechanism acts over the surrogate images yielding extrapolated-in-time representations. The phase-specific motion distributions are associated with the predicted temporal representations, allowing the recovery of dense organ deformation in multiple times. Due to its generative nature, this model enables uncertainty estimations by sampling the latent space multiple times. Furthermore, it can be readily personalized to a new subject via fine-tuning, and does not require inter-subject correspondences. The proposed model was evaluated on free-breathing 4D MRI and ultrasound datasets from 25 healthy volunteers, as well as on 11 cancer patients. A navigator-based data augmentation strategy was used during the slice reordering process to increase model robustness against inter-cycle variability. The patient data was used as a hold-out test set. Our approach yields volumetric prediction from image surrogates with a mean error of 1.67 ± 1.68 mm and 2.17 ± 0.82 mm in unseen cases of the patient MRI and US datasets, respectively. Moreover, model personalization yields a mean landmark error of 1.4 ± 1.1 mm compared to ground truth annotations in the volunteer MRI dataset, with statistically significant improvements over state-of-the-art.

An MRI framework for respiratory motion modelling validation

Introduction

Respiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine‐MRI and (ii) additional 4DMRI data for testing intra‐/inter‐fraction breathing motion variability.

Methods

Repeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter‐fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine‐MRI and 4DMRI respiratory phases, respectively.

Results

The median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm³), with higher values in the presence of large inter‐fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks’ position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra‐ and inter‐fraction motion, respectively. The range of motion described in the cine‐MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter‐fraction baseline variations occurred.

Conclusions

Results suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI‐guided radiotherapy.

Divergence-Free Fitting-Based Incompressible Deformation Quantification of Liver

Liver is an incompressible organ that maintains its volume during the respiration-induced deformation. Quantifying this deformation with the incompressible constraint is significant for liver tracking. The constraint can be accomplished with retaining the divergence-free field obtained by the deformation decomposition. However, the decomposition process is time-consuming, and the removal of non-divergence-free field weakens the deformation. In this study, a divergence-free fitting-based registration method is proposed to quantify the incompressible deformation rapidly and accurately. First, the deformation to be estimated is mapped to the velocity in a diffeomorphic space. Then, this velocity is decomposed by a fast Fourier-based Hodge-Helmholtz decomposition to obtain the divergence-free, curl-free, and harmonic fields. The curl-free field is replaced and fitted by the obtained harmonic field with a translation field to generate a new divergence-free velocity. By optimizing this velocity, the final incompressible deformation is obtained. Moreover, a deep learning framework (DLF) is constructed to accelerate the incompressible deformation quantification. An incompressible respiratory motion model is built for the DLF by using the proposed registration method and is then used to augment the training data. An encoder-decoder network is introduced to learn appearance-velocity correlation at patch scale. In the experiment, we compare the proposed registration with three state-of-the-art methods. The results show that the proposed method can accurately achieve the incompressible registration of liver with a mean liver overlap ratio of 95.33%. Moreover, the time consumed by DLF is nearly 15 times shorter than that by other methods.

Method for a motion model based automated 4D dose calculation

The Vero system can treat intra-fractionally moving tumors with gimbaled dynamic tumor tracking (DTT) by rotating the treatment beam so that it follows the motion of the tumor. However, the changes in the beam geometry and the constant breathing motion of the patient influence the dose applied to the patient. This study aims to perform a full 4D dose reconstruction for thirteen patients treated with DTT at the Vero system at the Universitätsklinikum Erlangen and investigates the temporal resolution required to perform an accurate 4D dose reconstruction. For all patients, a 4DCT was used to train a 4D motion model, which is able to calculate pseudo-CT images for arbitrary breathing phases. A new CT image was calculated for every 100 ms of treatment and a dose calculation was performed according to the current beam geometry (i.e. the rotation of the treatment beam at this moment in time) by rotating according to the momentary beam rotation, which is extracted from log-files. The resulting dose distributions were accumulated on the planning CT and characteristic parameters were extracted and compared. [Formula: see text]-evaluations of dose accumulations with different spatial-temporal resolutions were performed to determine the minimal required resolution. In total 173 700 dose calculations were performed. The accumulated 4D dose distributions show a reduced mean GTV dose of 0.77% compared to the static treatment plan. For some patients larger deviations were observed, especially in the presence of a poor 4DCT quality. The [Formula: see text]-evaluation showed that a temporal resolution of 500 ms is sufficient for an accurate dose reconstruction. If the tumor motion is regarded as well, a spatial-temporal sampling of 1400 ms and 2 mm yields accurate results, which reduces the workload by 84%.

Real-time intrafraction motion monitoring in external beam radiotherapy

Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

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 ROI-based global motion model established on 4DCT and 2D cine-MRI data for MRI-guidance in radiation therapy

In-room magnetic resonance imaging (MRI) allows the acquisition of fast 2D cine-MRI centered in the tumor for advanced motion management in radiotherapy. To achieve 3D information during treatment, patient-specific motion models can be considered the most viable solution. However, conventional global motion models are built using a single motion surrogate, independently from the anatomical location. In this work, we present a novel motion model based on regions of interest (ROIs) established on 4D computed tomography (4DCT) and 2D cine-MRI, aiming at accurately compensating for changes during treatment. In the planning phase, a motion model is built on a 4DCT dataset, through 3D deformable image registration (DIR). ROIs are then defined and correlated with motion fields derived by 2D DIR between CT slices centered in the tumor. In the treatment phase, the model is applied to in-room cine-MRI data to compensate for organ motion in a multi-modal framework, aiming at estimating a time-resolved 3DCT. The method is validated on a digital phantom and tested on two lung patients. Analysis is performed by considering different anatomical planes (coronal, sagittal and a combination of the two) and evaluating the performance of the method on tumor and diaphragm. For the phantom study, the ROI-based model results in a uniform median error on both diaphragm and tumor below 1.5 mm. For what concerns patients, median errors on both diaphragm and tumor are around 2 mm (maximum patient resolution), confirming the capability of the method to regionally compensate for motion. A novel ROI-based motion model is proposed as an integral part of an envisioned clinical MRI-guided workflow aiming at enhanced image guidance compared to conventional strategies.

Management of organ motion in scanned ion beam therapy

Scanned ion beam therapy has special demands for treatment of intra-fractionally moving tumors such as lesions in lung or liver. Interplay effects between beam and organ motion can in those settings lead to under-dosage of the target volume. Dedicated treatment techniques such as gating or abdominal compression are required. In addition 4D treatment planning should be used to determine strategies for patient specific treatment planning such as an increased beam focus or the use of internal target volumes incorporating range changes.Several work packages of the Clinical Research Units 214 and 214/2 funded by the German Research Council investigated the management of organ motion in scanned ion beam therapy. A focus was laid on 4D treatment planning using TRiP4D and the development of motion mitigation strategies including their quality assurance. This review focuses on the activity in the second funding period covering adaptive treatment planning strategies, 4D treatment plan optimization, and the application of motion management in pre-clinical research on radiation therapy of cardiac arrhythmias.

Multiple-CT optimization of intensity-modulated proton therapy - Is it possible to eliminate adaptive planning?

BACKGROUND AND PURPOSE

We hypothesized that a plan's robustness to anatomical changes can be improved by optimizing with multiple CT scans of a patient. The purpose of this study was to determine whether an intensity modulated proton therapy (IMPT) plan could be developed to meet dose criteria on both planning and adaptive CT plans.

MATERIAL AND METHODS

Eight lung cancer patients who underwent adaptive IMPT were retrospectively selected. Each patient had two CTs: a primary planning CT (PCT) and an adaptive planning CT (ACT), and IMPT plans associated with the scans. PCT and ACT were then used in combination to optimize one plan (MCT plan). The doses to the target and organs at risk from the PCT plan, ACT plan, P-ACT plan (PCT plan calculated on ACT data), and MCT plans calculated on both CTs were compared.

RESULTS

The MCT plan maintained the D_95% on both CTs (mean, 65.99 Gy on PCT and 66.02 Gy on ACT). Target dose coverage on ACT was significantly better with the MCT plan than with the P-ACT plan (p = 0.01). MCT plans had slightly higher lung V₂₀ (0.6%, p = 0.02) than did PCT plans. The various plans showed no statistically significant difference in heart and spinal cord dose.

CONCLUSIONS

The results of this study indicate that an IMPT plan can meet the dose criteria on both PCT and ACT, and that MCT optimization can improve the plan's robustness to anatomical change.