Technical Note: Four-dimensional deformable digital phantom for MRI sequence development.

Respiratory motion modeling of both tumor and surrounding tissues is a key element in minimizing errors and uncertainties in radiation therapy. Different continuous motion models have been previously developed. However, most of these models are based on the use of parameters such as amplitude and phase extracted from 1D external respiratory signal. A potentially reduced correlation between the internal structures (tumor and healthy organs) and the corresponding external surrogates obtained from such 1D respiratory signal is a limitation of these models. The objective of this work is to describe a continuous patient specific respiratory motion model, accounting for the irregular nature of respiratory signals, using patient external surface information as surrogate measures rather than a 1D respiratory signal. Ten patients were used in this study having each one 4D CT series, a synchronized RPM signal and patient surfaces extracted from the 4D CT volumes using a threshold based segmentation algorithm. A patient specific model based on the use of principal component analysis was subsequently constructed. This model relates the internal motion described by deformation matrices and the external motion characterized by the amplitude and the phase of the respiratory signal in the case of the RPM or using specific regions of interest (ROI) in the case of the patients' external surface utilization. The capability of the different models considered to handle the irregular nature of respiration was assessed using two repeated 4D CT acquisitions (in two patients) and static CT images acquired at extreme respiration conditions (end of inspiration and expiration) for one patient. Both quantitative and qualitative parameters covering local and global measures, including an expert observer study, were used to assess and compare the performance of the different motion estimation models considered. Results indicate that using surface information [correlation coefficient (CC): 0.998 ± 0.0006 and model error (ME): 1.35 ± 0.21 mm] is superior to the use of both motion phase and amplitude extracted from a 1D respiratory signal (CC and ME of 0.971 ± 0.02 and 1.64 ± 0.28 mm). The difference in performance was more substantial compared to the use of only one parameter (phase or amplitude) used in the motion model construction. Similarly, the patient surface based model was better in estimating the motion in the repeated 4D CT acquisitions and those CT images acquired at the full inspiration (FI) and the full expiration (FE). Once more, within this context the use of both amplitude and phase in the model building was substantially more robust than the use of phase or amplitude only. The present study demonstrates the potential of using external patient surfaces for the construction of patient specific respiratory motion models. Such information can be obtained using different devices currently available. The use of external surface information led to the best performance in estimating internal structure motion. On the other hand, the use of both amplitude and phase parameters derived from an 1D respiration signal led to largely superior model performance relative to the use of only one of these two parameters in the model building process.

Cardiac applications in radiation therapy are rapidly expanding including magnetic resonance guided radiation therapy (MRgRT) for real-time gating for targeting and avoidance near the heart or treating ventricular tachycardia (VT). This work describes the development and implementation of a novel multi-modality and magnetic resonance (MR)-compatible cardiac phantom. The patient-informed 3D model was derived from manual contouring of a contrast-enhanced Coronary Computed Tomography Angiography scan, exported as a Stereolithography model, then post-processed to simulate female heart with an average volume. The model was 3D-printed using Elastic50A to provide MR contrast to water background. Two rigid acrylic modules containing cardiac structures were designed and assembled, retrofitting to an MR-safe programmable motor to supply cardiac and respiratory motion in superior-inferior directions. One module contained a cavity for an ion chamber (IC), and the other was equipped with multiple interchangeable cavities for plastic scintillation detectors (PSDs). Images were acquired on a 0.35 T MR-linac for validation of phantom geometry, motion, and simulated online treatment planning and delivery. Three motion profiles were prescribed: patient-derived cardiac (sine waveform, 4.3mm peak-to-peak, 60 beats/min), respiratory (cos4 waveform, 30mm peak-to-peak, 12 breaths/min), and a superposition of cardiac (sine waveform, 4mm peak-to-peak, 70 beats/min) and respiratory (cos4 waveform, 24mm peak-to-peak, 12 breaths/min). The amplitude of the motion profiles was evaluated from sagittal cine images at eight frames/s with a resolution of 2.4mm × 2.4mm. Gated dosimetry experiments were performed using the two module configurations for calculating dose relative to stationary. A CT-based VT treatment plan was delivered twice under cone-beam CT guidance and cumulative stationary doses to multi-point PSDs were evaluated. No artifacts were observed on any images acquired during phantom operation. Phantom excursions measured 49.3±25.8%/66.9±14.0%, 97.0±2.2%/96.4±1.7%, and 90.4±4.8%/89.3±3.5% of prescription for cardiac, respiratory, and cardio-respiratory motion profiles for the 2-chamber (PSD) and 12-substructure (IC) phantom modules respectively. In the gated experiments, the cumulative dose was<2% from expected using the IC module. Real-time dose measured for the PSDs at 10Hz acquisition rate demonstrated the ability to detect the dosimetric consequences of cardiac, respiratory, and cardio-respiratory motion when sampling of different locations during a single delivery, and the stability of our phantom dosimetric results over repeated cycles for the high dose and high gradient regions. For the VT delivery, high dose PSD was<1% from expected (5-6 cGy deviation of 5.9Gy/fraction) and high gradient/low dose regions had deviations<3.6% (6.3 cGy less than expected 1.73Gy/fraction). A novel multi-modality modular heart phantom was designed, constructed, and used for gated radiotherapy experiments on a 0.35 T MR-linac. Our phantom was capable of mimicking cardiac, cardio-respiratory, and respiratory motion while performing dosimetric evaluations of gated procedures using IC and PSD configurations. Time-resolved PSDs with small sensitive volumes appear promising for low-amplitude/high-frequency motion and multi-point data acquisition for advanced dosimetric capabilities. Illustrating VT planning and delivery further expands our phantom to address the unmet needs of cardiac applications in radiotherapy.

Respiratory motion during PET imaging causes the resulting images to become corrupted by artifacts. Respiratory motion models have found application in estimating and correcting for the effects of motion in a wide range of applications. In this work we evaluate the performance of an MR-derived motion model for motion-correction of PET data that is suitable for use in a simultaneous PET-MR imaging system. The technique is based on the formation of a subject-specific respiratory motion model from near real-time dynamic MR images and is capable of making real-time motion estimates based on an MR navigator signal. This approach has an advantage over gating based approaches in that individual motion corrections can be applied at each time point, avoiding the need to combine large amounts of PET data for correction by a single transformation for each gate. In our investigation, we evaluate the effects of the MR-derived motion model and PET motion compensation using dynamic PET simulations based on dynamic MR data (105 real-time frames, 0.7s each) of the thorax acquired from a healthy volunteer. This large amount of data, acquired during different breathing patterns, allows us to more accurately simulate the complex respiratory motion variation that is observed in real PET acquisitions. PET images are corrected for motion using two approaches: reconstruct-transform-average (RTA) and motion compensated image reconstruction (MCIR). We compare the methods in terms of quantification and visualization of three tumors that were artificially added to the emission and attenuation maps of the volunteer close to the diaphragm. It is demonstrated that our motion model can be used to correct successfully for motion in real-time dynamic PET-MR acquisitions. The correction is feasible using either RTA or MCIR, although the latter provides

One of the most important parameters reducing the sensitivity and specificity in the thoracic and abdominal areas is respiratory motion and associated deformations which represent today an important challenge in medical imaging. The negative impact of respiratory motion is not only evident in the field of cancer diagnostic imaging but also in therapy applications, particularly in the field of radiation therapy. Modelling of the respiratory motion is therefore very important for the efficacy of both diagnostic and therapeutic imaging applications. All respiratory motion models presented to date are patient specific and require a 4D CT acquisition for every patient implying an associated increased dose which cannot be easily justified for all patients. The objective of this study is the description of a framework for the creation of a global generic model from 4D CT images of different patients. This global model is based on principal component analysis (PCA) and can be adapted to a given patient anatomy needing only one static CT image in combination with respiratory synchronised images of the patient surface. In this work 4D CT images of three patients were used to create the model which was tested by subsequently adapting it on the static CT images of two different patients. Our comparative results between the model derived and the corresponding acquired 4D CT images demonstrate the efficiency of the developed generic motion model.

Purpose: Cone-beam CT is a common approach for 3D guidance in interventional radiology (IR), but its long image acquisition time poses a limit to image quality due to complex deformable motion of soft-tissue structures. Multi-region autofocus optimization showed successful compensation of deformable motion with gains in image quality. However, the use of sub-optimal conventional autofocus metrics, accompanied by the high-dimensionality and non-convexity of the optimization problem result in challenged convergence. This work presents a learning-based approach to the design of autofocus metrics tailored to the specific problem of motion compensation in soft-tissue CBCT. Methods: A deep convolutional neural network (CNN) was used to automatically extract image features quantifying the local motion amplitude and principal direction in volumetric regions of interest (128 x 128x 128 voxels) of a motion contaminated volume. The estimated motion amplitude is then used as the core component of the cost function of the deformable autofocus method, complemented by a regularization term favoring similar motion for regions close in space. The network consists of a siamese arrangement of three branches acting on the three central orthogonal planes of the volumetric ROI. The network was trained with simulated data generated from publicly available CT datasets, including deformable motion fields from which volumetric ROIs with locally rigid motion were extracted. The performance of motion amplitude estimation and of the final CNN-based deformable autofocus were assessed on synthetic CBCT data generated similarly to the training dataset and featuring deformable motion fields with 1 to 5 components with random direction and random amplitude ranging from 0 mm to 50 mm. Results: Predicted local motion amplitude showed good agreement with the true values, showing a liner relationship (R2 = 0.96, slope 0.95), and slight underestimation of the motion amplitude. Absolute errors in total motion amplitude and individual components remained < 2 mm throughout the explored range. Relative errors were higher for low amplitude motion, pointing to need of a larger training cohort, more focused on low motion amplitude scenarios. Motion compensation with the learning-based metric showed successful compensation of motion artifacts in single ROI environments, with 40% reduction in median RMSE using the motion static image as reference. Deformable motion compensation resulted in better visualization of soft-tissue structures, and overall sharper image details, with slight residual motion artifacts for regions combining moderate motion amplitude with complex anatomical structures. Conclusion: Deformable motion compensation with automatically learned autofocus metrics was proved feasible, opening the way to the design of metrics with potential for more reliable performance and easier optimization than conventional autofocus metrics.

We propose a bilinear model of respiratory organ motion. The advantages of classical statistical shape modelling are combined with a preconditioned trajectory basis for separately modelling the shape and motion components of the data. The separation of a linear basis into bilinear form leads to a more compact representation of the underlying physical process and the resulting model respects the temporal regularity within the training data, which is an important property for modelling quasi-periodic data. Bilinear modelling is combined with a Bayesian reconstruction algorithm for sparse data under observation noise. By applying the model to liver motion data, we show that our bilinear formulation of respiratory motion is significantly more parsimonious and can even outperform linear PCA-based models.

Respiratory motion degrades quantitative and qualitative analysis of medical images. Estimation and, hence, correction of motion commonly uses static correspondence models between an external surrogate signal and internal motion. This paper presents a patient specific respiratory motion model with the ability to adapt in the presence of irregular motion via a Kalman filter with expectation maximization for parameter estimation. The adaptive approach introduces generalizability allowing the model to account for a broader variety of motion. This may be required in the presence of irregular breathing and with different sensors monitoring the external surrogate signal. The motion model framework utilizing an adaptive Kalman filter approach is tested on dynamic magnetic resonance imaging data of nine volunteers and compared to a state-of-the-art static total least squares approach. Results demonstrate the framework is capable of reducing motion to the order of ${p} ) more effective in the presence of irregular motion, assessed using the ${F}$ -test for model comparison. Utilizing the total sum of squares of estimated vector field error from the calculated ground truth, we observe approximately a fifty percent reduction in root mean square error and thirty percent reduction in standard deviation utilizing the Kalman model (EKF) in comparison to a static counterpart.

Data-driven respiratory gating techniques in cardiac PET extract respiratory motion signal from the PET data to guide respiratory gating and motion estimation. However, the influence of the quality of the list-mode data including the uptake ratio between the myocardium and background and the detected count level on its performance has never been studied thoroughly. In this project, list-mode PET data derived from realistic Monte Carlo simulation of different uptake ratios and count levels was employed to quantitatively evaluate the respiratory motion signal extraction and estimation techniques. Simulated projection data were generated from the 4D XCAT phantom with known respiratory motion. The list-mode data were rebinned into projection data at 200 msec intervals and the centers-of-mass of a region-of-interest over the heart were calculated. Contributions from the background were subtracted in the centroid calculation. The peak in the Fourier transform of the time sequence of the centroid locations, or the frequency spectrum, was identified as the respiratory motion signal and its SNR was measured. The high frequency components of the frequency spectrum were removed for noise smoothing before extracting the respiratory motion signal for use in respiratory gating estimation. The results show that with background correction, the amplitude of the estimated respiratory motion signal is increased and is closer to the truth. Both lower myocardium to background uptake ratio and lower count level reduce the SNR of the respiratory motion signal. When they become too low, the extraction of the respiratory motion signal becomes difficult or fails completely.

We describe an investigation of the use of the Insight Toolkit (ITK), a widely used open-source image segmentation and registration software, to obtain the motion transform from the MR images for both rigid and deformable PET motion corrections for simultaneous PET/MR scans. A human brain PET/MR FDG study and two human cardiac PET/MR FDG studies were acquired on Siemens' Biograph-mMR. ITK's 3D-Versor registration was used to obtain the rigid motion transform of the brain from the moved and the reference T2 weighted MR images. Registrations based on B-Spline transform and Demons transform were used to obtain the deformable motion fields from the TWIST (Time-resolved angiography With Interleaved Stochastic Trajectories) and CINE MR images. The motion transforms derived from the registrations were then applied to the PET reconstructions. It has been observed that 3D-Versor registration produces accurate rigid motion transform. With regard to deformable motions, Demons transform performs slightly better than B-Spline transform in terms of %RMSE in the registered TWIST MR images. However, the motion field obtained from the TWIST MR images using Demons transform was observed to introduce irregular distortion in the PET image. On the other hand, Demons motion transforms obtained from the CINE images do not introduce any artifact in the PET image, whereas the B-Spline method does not register the CINE MR images very well. The motion corrected PET image has better signal-to-noise ratio than the gated PET image as expected.

Accurate fusion of late gadolinium enhancement magnetic resonance imaging (MRI) and electro-anatomical voltage mapping (EAM) is required to evaluate the potential of MRI to identify the substrate of ventricular tachycardia. However, both datasets are not acquired at the same cardiac phase and EAM data is corrupted with respiratory motion limiting the accuracy of current rigid fusion techniques. Knowledge of cardiac and respiratory motion during EAM is thus required to enhance the fusion process. In this study, we propose a novel approach to characterize both cardiac and respiratory motion from EAM data using the temporal evolution of the 3D catheter location recorded from clinical EAM systems. Cardiac and respiratory motion components are extracted from the recorded catheter location using multi-band filters. Filters are calibrated for each EAM point using estimates of heart rate and respiratory rate. The method was first evaluated in numerical simulations using 3D models of cardiac and respiratory motions of the heart generated from real time MRI data acquired in 5 healthy subjects. An accuracy of 0.6–0.7 mm was found for both cardiac and respiratory motion estimates in numerical simulations. Cardiac and respiratory motions were then characterized in 27 patients who underwent LV mapping for treatment of ventricular tachycardia. Mean maximum amplitude of cardiac and respiratory motion was 10.2±2.7 mm (min = 5.5, max = 16.9) and 8.8±2.3 mm (min = 4.3, max = 14.8), respectively. 3D Cardiac and respiratory motions could be estimated from the recorded catheter location and the method does not rely on additional imaging modality such as X-ray fluoroscopy and can be used in conventional electrophysiology laboratory setting.

The use of magnetic resonance imaging (MRI) in radiation oncology is expanding rapidly, and more clinics are integrating MRI into their radiation therapy workflows. However, radiation therapy presents a new set of challenges and places additional constraints on MRI compared to diagnostic radiology that, if not properly addressed, can undermine the advantages MRI offers for radiation treatment planning (RTP). The authors introduce here strategies to manage several challenges of using MRI for virtual simulation in external beam RTP. A total of 810 clinical MRI simulation exams were performed using a dedicated MRI scanner for external beam RTP of brain, breast, cervix, head and neck, liver, pancreas, prostate, and sarcoma cancers. Patients were imaged in treatment position using MRI-optimal immobilization devices. Radiofrequency (RF) coil configurations and scan protocols were optimized based on RTP constraints. Off-resonance and gradient nonlinearity-induced geometric distortions were minimized or corrected prior to using images for RTP. A multidisciplinary MRI simulation guide, along with window width and level presets, was created to standardize use of MR images during RTP. A quality assurance program was implemented to maintain accuracy and repeatability of MRI simulation exams. The combination of a large bore scanner, high field strength, and circumferentially wrapped, flexible phased array RF receive coils permitted acquisition of thin slice images with high contrast-to-noise ratio (CNR) and image intensity uniformity, while simultaneously accommodating patient setup and immobilization devices. Postprocessing corrections and alternative acquisition methods were required to reduce or correct off-resonance and gradient nonlinearity induced geometric distortions. The methodology described herein contains practical strategies the authors have implemented through lessons learned performing clinical MRI simulation exams. In their experience, these strategies provide robust, high fidelity, high contrast MR images suitable for external beam RTP.

Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12mm, compared to 2.6% for the CoM method and 8.5% for no correction. We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

We propose a new type of computational phantom, the “4D voxel phantom,” for realistic modeling of continuous respiratory motion in Monte Carlo dose calculation. In this phantom, continuous respiratory motion is realized by linear interpolation of the deformation vector fields (DVFs) between the neighboring original phases in the 4D CT data of a patient and by subsequent application of the DVFs to the phase images or to the reference image to produce multiple inter-phase images between the neighboring original phase images. A 4D voxel phantom is a combination of high-temporal-resolution voxel phantoms and on-the-fly dose registration to the reference phase image. In the course of particle transport simulation, the dose or deposited energy is directly registered to the reference phase image on-the-fly (i.e., after each event) using a DVF for dose registration. In the present study, we investigated two methods - DRP (DIR [deformable image registration] with respect to Reference Phase image) and DNP (DIR with respect to Neighboring original Phase image) - for production of multiple inter-phase images or high-temporal-resolution voxel phantoms. Utilizing these two methods, two 4D voxel phantoms each with 100 phases were produced from the original 10-phase images of the 4D CT data of a real patient in order to compare the two methods and to test the feasibility of the 4D voxel phantom methodology in general. We found that it is possible to produce a 4D voxel phantom very rapidly (i.e., <;40 min on a 4-core personal computer for a 100-phase phantom) in a fully automated process. The dose calculation results showed that the constructed 100-phase 4D voxel phantoms provide cumulative-dose distributions very similar to those of the conventional 10-phase approach for stationary proton-beam irradiation. The passing rates of the dose distributions of the 4D voxel phantoms were higher than 99.9% according to the 3% and 3 mm gamma criteria, which results validate the 4D voxel phantom methodology. The point-and dose-tracking analysis data showed that the DRP method, which uses the minimal number of DIR operations but uses inverse DVFs, provides significantly better results than those of the DNP method, which uses only DIR to generate the DVFs for inter-phase image generation and dose registration. The present study also showed that the computation time does not significantly increase when the number of phases in the 4D voxel phantom is increased for more realistic representation of continuous respiratory motion; the only significant increase is in the memory occupancy, which grows almost linearly with the number of phases.

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.

To assess the dosimetric impact of interplay between spot-scanning proton beam and respiratory motion in intensity-modulated proton therapy (IMPT) for stage III lung cancer. Eleven patients were sampled from 112 patients with stage III nonsmall cell lung cancer to well represent the distribution of 112 patients in terms of target size and motion. Clinical target volumes (CTVs) and planning target volumes (PTVs) were defined according to the authors' clinical protocol. Uniform and realistic breathing patterns were considered along with regular- and hypofractionation scenarios. The dose contributed by a spot was fully calculated on the computed tomography (CT) images corresponding to the respiratory phase that the spot is delivered, and then accumulated to the reference phase of the 4DCT to generate the dynamic dose that provides an estimation of what might be delivered under the influence of interplay effect. The dynamic dose distributions at different numbers of fractions were compared with the corresponding 4D composite dose which is the equally weighted average of the doses, respectively, computed on respiratory phases of a 4DCT image set. Under regular fractionation, the average and maximum differences in CTV coverage between the 4D composite and dynamic doses after delivery of all 35 fractions were no more than 0.2% and 0.9%, respectively. The maximum differences between the two dose distributions for the maximum dose to the spinal cord, heart V40, esophagus V55, and lung V20 were 1.2 Gy, 0.1%, 0.8%, and 0.4%, respectively. Although relatively large differences in single fraction, correlated with small CTVs relative to motions, were observed, the authors' biological response calculations suggested that this interfractional dose variation may have limited biological impact. Assuming a hypofractionation scenario, the differences between the 4D composite and dynamic doses were well confined even for single fraction. Despite the presence of interplay effect, the delivered dose may be reliably estimated using the 4D composite dose. In general the interplay effect may not be a primary concern with IMPT for lung cancers for the authors' institution. The described interplay analysis tool may be used to provide additional confidence in treatment delivery.