Respiratory Motion Model Research Articles

Feasibility Study of Respiratory Motion Modeling Based Correction for MRI-Guided Intracardiac Interventional Procedures.

The purpose of this study is to improve the accuracy of interventional catheter guidance during intracardiac procedures. Specifically, the use of preprocedural magnetic resonance roadmap images for interventional guidance has limited anatomical accuracy due to intraprocedural respiratory motion of the heart. Therefore, we propose to build a novel respiratory motion model to compensate for this motion-induced error during magnetic resonance imaging (MRI)-guided procedures. We acquire 2-D real-time free-breathing images to characterize the respiratory motion, and build a smooth motion model via registration of 3-D prior roadmap images to the real-time images within a novel principal axes frame of reference. The model is subsequently used to correct the interventional catheter positions with respect to the anatomy of the heart. We demonstrate that the proposed modeling framework can lead to smoother motion models, and potentially lead to more accurate motion estimates. Specifically, MRI-guided intracardiac ablations were performed in six preclinical animal experiments. Then, from retrospective analysis, the proposed motion modeling technique showed the potential to achieve a 27% improvement in ablation targeting accuracy. The feasibility of a respiratory motion model-based correction framework has been successfully demonstrated. The improvement in ablation accuracy may lead to significant improvements in success rate and patient outcomes for MRI-guided intracardiac procedures.

5D respiratory motion model based image reconstruction algorithm for 4D cone-beam computed tomography

4D cone-beam computed tomography (4DCBCT) reconstructs a temporal sequence of CBCT images for the purpose of motion management or 4D treatment in radiotherapy. However the image reconstruction often involves the binning of projection data to each temporal phase, and therefore suffers from deteriorated image quality due to inaccurate or uneven binning in phase, e.g., under the non-periodic breathing. A 5D model has been developed as an accurate model of (periodic and non-periodic) respiratory motion. That is, given the measurements of breathing amplitude and its time derivative, the 5D model parametrizes the respiratory motion by three time-independent variables, i.e., one reference image and two vector fields. In this work we aim to develop a new 4DCBCT reconstruction method based on 5D model. Instead of reconstructing a temporal sequence of images after the projection binning, the new method reconstructs time-independent reference image and vector fields with no requirement of binning. The image reconstruction is formulated as a optimization problem with total-variation regularization on both reference image and vector fields, and the problem is solved by the proximal alternating minimization algorithm, during which the split Bregman method is used to reconstruct the reference image, and the Chambolle's duality-based algorithm is used to reconstruct the vector fields. The convergence analysis of the proposed algorithm is provided for this nonconvex problem. Validated by the simulation studies, the new method has significantly improved image reconstruction accuracy due to no binning and reduced number of unknowns via the use of the 5D model.

Respiratory motion correction of PET using MR-constrained PET-PET registration.

BackgroundRespiratory motion in positron emission tomography (PET) is an unavoidable source of error in the measurement of tracer uptake, lesion position and lesion size. The introduction of PET-MR dual modality scanners opens a new avenue for addressing this issue. Motion models offer a way to estimate motion using a reduced number of parameters. This can be beneficial for estimating motion from PET, which can otherwise be difficult due to the high level of noise of the data.MethodWe propose a novel technique that makes use of a respiratory motion model, formed from initial MR scan data. The motion model is used to constrain PET-PET registrations between a reference PET gate and the gates to be corrected. For evaluation, PET with added FDG-avid lesions was simulated from real, segmented, ultrashort echo time MR data obtained from four volunteers. Respiratory motion was included in the simulations using motion fields derived from real dynamic 3D MR volumes obtained from the same volunteers.ResultsPerformance was compared to an MR-derived motion model driven method (which requires constant use of the MR scanner) and to unconstrained PET-PET registration of the PET gates. Without motion correction, a median drop in uncorrected lesion {mathrm {SUV}}_{mathrm {peak}} intensity to 78.4 pm 18.6 ,,% and an increase in median head-foot lesion width, specified by a minimum bounding box, to 179 pm 63.7,, % was observed relative to the corresponding measures in motion-free simulations. The proposed method corrected these values to 86.9 pm 13.6,, % (p<0.001) and 100 pm 29.12,, % (p<0.001) respectively, with notably improved performance close to the diaphragm and in the liver. Median lesion displacement across all lesions was observed to be 6.6 pm 5.4,mathrm {mm} without motion correction, which was reduced to 3.5 pm 1.8,mathrm {mm} (p<0.001) with motion correction.DiscussionThis paper presents a novel technique for respiratory motion correction of PET data in PET-MR imaging. After an initial 30 second MR scan, the proposed technique does not require use of the MR scanner for motion correction purposes, making it suitable for MR-intensive studies or sequential PET-MR. The accuracy of the proposed technique was similar to both comparative methods, but robustness was improved compared to the PET-PET technique, particularly in regions with higher noise such as the liver.

Reference respiratory waveforms by minimum jerk model analysis.

CyberKnife(®) robotic surgery system has the ability to deliver radiation to a tumor subject to respiratory movements using Synchrony(®) mode with less than 2 mm tracking accuracy. However, rapid and rough motion tracking causes mechanical tracking errors and puts mechanical stress on the robotic joint, leading to unexpected radiation delivery errors. During clinical treatment, patient respiratory motions are much more complicated, suggesting the need for patient-specific modeling of respiratory motion. The purpose of this study was to propose a novel method that provides a reference respiratory wave to enable smooth tracking for each patient. The minimum jerk model, which mathematically derives smoothness by means of jerk, or the third derivative of position and the derivative of acceleration with respect to time that is proportional to the time rate of force changed was introduced to model a patient-specific respiratory motion wave to provide smooth motion tracking using CyberKnife(®). To verify that patient-specific minimum jerk respiratory waves were being tracked smoothly by Synchrony(®) mode, a tracking laser projection from CyberKnife(®) was optically analyzed every 0.1 s using a webcam and a calibrated grid on a motion phantom whose motion was in accordance with three pattern waves (cosine, typical free-breathing, and minimum jerk theoretical wave models) for the clinically relevant superior-inferior directions from six volunteers assessed on the same node of the same isocentric plan. Tracking discrepancy from the center of the grid to the beam projection was evaluated. The minimum jerk theoretical wave reduced the maximum-peak amplitude of radial tracking discrepancy compared with that of the waveforms modeled by cosine and typical free-breathing model by 22% and 35%, respectively, and provided smooth tracking for radial direction. Motion tracking constancy as indicated by radial tracking discrepancy affected by respiratory phase was improved in the minimum jerk theoretical model by 7.0% and 13% compared with that of the waveforms modeled by cosine and free-breathing model, respectively. The minimum jerk theoretical respiratory wave can achieve smooth tracking by CyberKnife(®) and may provide patient-specific respiratory modeling, which may be useful for respiratory training and coaching, as well as quality assurance of the mechanical CyberKnife(®) robotic trajectory.

Technical Note: Automatic real-time ultrasound tracking of respiratory signal using selective filtering and dynamic template matching

In respiratory motion modeling for liver interventions, the respiratory signal is usually obtained by using special tracking devices to monitor external skin. However, due to intrinsic limits and cost consideration of these tracking devices, a purely ultrasound image-based approach to tracking the signal is a more feasible option. In this study, a novel image-based method is proposed to obtain the respiratory signal directly from 2D ultrasound images by automatically identifying and tracking the liver boundary. The boundary identification is a multistage process, which is the key to utilize a Hessian matrix-based 2D filter to enhance the line-like liver boundary and weaken other liver tissues. For tracking the identified boundary, a new dynamic template matching technique is first applied to estimate 2D displacements, and a boundary-specific selection mechanism is then introduced to extract the respiratory signal from the 2D displacements. The experiments demonstrate that their method can obtain accurate breathing signals, which are in key phases comparable to the manually annotation and highly consistent to the electromagnetic-tracked ground-truth signals (average correlation coefficients 0.9209 and statistically significant p < 0.01). Meanwhile, the experiments also prove their method can achieve high real-time performance of about 80-160 Hz. This method provides a good alternative to traditional external-landmark-based tracking methods and may be widely applied for respiratory compensation in ultrasound-guided liver interventions.

WE-D-303-02: Applications of Volumetric Images Generated with a Respiratory Motion Model Based On An External Surrogate Signal

Purpose: Respiratory motion can vary significantly over the course of simulation and treatment. Our goal is to use volumetric images generated with a respiratory motion model to improve the definition of the internal target volume (ITV) and the estimate of delivered dose. Methods: Ten irregular patient breathing patterns spanning 35 seconds each were incorporated into a digital phantom. Ten images over the first five seconds of breathing were used to emulate a 4DCT scan, build the ITV, and generate a patient-specific respiratory motion model which correlated the measured trajectories of markers placed on the patients’ chests with the motion of the internal anatomy. This model was used to generate volumetric images over the subsequent thirty seconds of breathing. The increase in the ITV taking into account the full 35 seconds of breathing was assessed with ground-truth and model-generated images. For one patient, a treatment plan based on the initial ITV was created and the delivered dose was estimated using images from the first five seconds as well as ground-truth and model-generated images from the next 30 seconds. Results: The increase in the ITV ranged from 0.2 cc to 6.9 cc for the ten patients based on ground-truth information. The model predicted this increase in the ITV with an average error of 0.8 cc. The delivered dose to the tumor (D95) changed significantly from 57 Gy to 41 Gy when estimated using 5 seconds and 30 seconds, respectively. The model captured this effect, giving an estimated D95 of 44 Gy. Conclusion: A respiratory motion model generating volumetric images of the internal patient anatomy could be useful in estimating the increase in the ITV due to irregular breathing during simulation and in assessing delivered dose during treatment. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc. and Radiological Society of North America Research Scholar Grant #RSCH1206.

SU-E-J-54: A Framework of Physical-Law-Based Respiratory-Perturbation Modeling for Motion Prediction

Purpose A physics-law-based respiratory motion model should be more resilient to breathing irregularities. We assess the accuracy of a physical respiratory motion-perturbation (RMP) model, which is derived with physical relationships, patient-specific anatomy, and measureable breathing parameters. Methods The analytical RMP model was developed under the assumption that chest raise (Δthorax) contributes primarily to the anterior-posterior motion of the lung while belly motion (Δabdomen) caused by diaphragmatic motion contributes to superior-inferior motion. All model parameters were determined from patient-specific anatomy. This RMP model aims to use the base motion trajectory from simulation 4DCT and motion perturbations due to breathing irregularities at treatment from optical surface imaging (OSI) to calculate the tidal volume (TV=Δtorso) and breathing pattern (BPv=Δthorax/Δtorso). To test the prediction accuracy, two sets of 4DCT from eleven patients were used to predict lung motion from one set to the other. An in-house ITK-based C/C++ program was developed and used to automatically identify 20 bifurcation points per patient, calculate their motion trajectories, and sets their correspondence between the two 4DCT with visual verification. The base motion from one 4DCT and perturbations (ΔTV and ΔBP) from the other 4DCT (mimicking OSI) were used for prediction and the accuracy was assessed by comparing the predicted with the actual motion trajectory: A comparison was made using an established 5D model that was trained with one 4DCT to predict the other. The motion difference between the two 4DCT was used as a control. Results The RMP model prediction provides the prediction accuracy of −0.1±1.9mm, improved from the control of 0±3mm. The RMP produces similar results to the 5D model, but does not require model training. The motion variations range is 0–15mm. This work is in part supported by NIH (U54CA137788 and U54CA132378).

SU‐E‐J‐26: A Novel Technique for Markerless Self‐Sorted 4D‐CBCT Using Patient Motion Modeling: A Feasibility Study

Purpose:To develop an automatic markerless 4D‐CBCT projection sorting technique by using a patient respiratory motion model extracted from the planning 4D‐CT images.Methods:Each phase of onboard 4D‐CBCT is considered as a deformation of one phase of the prior planning 4D‐CT. The deformation field map (DFM) is represented as a linear combination of three major deformation patterns extracted from the planning 4D‐CT using principle component analysis (PCA). The coefficients of the PCA deformation patterns are solved by matching the digitally reconstructed radiograph (DRR) of the deformed volume to the onboard projection acquired. The PCA coefficients are solved for each single projection, and are used for phase sorting. Projections at the peaks of the Z direction coefficient are sorted as phase 1 and other projections are assigned into 10 phase bins by dividing phases equally between peaks. The 4D digital extended‐cardiac‐torso (XCAT) phantom was used to evaluate the proposed technique. Three scenarios were simulated, with different tumor motion amplitude (3cm to 2cm), tumor spatial shift (8mm SI), and tumor body motion phase shift (2 phases) from prior to on‐board images. Projections were simulated over 180 degree scan‐angle for the 4D‐XCAT. The percentage of accurately binned projections across entire dataset was calculated to represent the phase sorting accuracy.Results:With a changed tumor motion amplitude from 3cm to 2cm, markerless phase sorting accuracy was 100%. With a tumor phase shift of 2 phases w.r.t. body motion, the phase sorting accuracy was 100%. With a tumor spatial shift of 8mm in SI direction, phase sorting accuracy was 86.1%.Conclusion:The XCAT phantom simulation results demonstrated that it is feasible to use prior knowledge and motion modeling technique to achieve markerless 4D‐CBCT phase sorting.National Institutes of Health Grant No. R01‐CA184173 Varian Medical System

TH-CD-303-07: Evaluation of Four Data-Driven Respiratory Motion-Compensation Methods for Four-Dimensional Cone-Beam CT Registration and Reconstruction

Purpose: To develop and compare four 4D-CBCT reconstruction methods that correct for respiratory motion and enhance image quality. Methods: Four motion-compensation workflows were developed employing a combination of deformable image registration (DIR) and image reconstruction. Groupwise registration was used to simultaneously register all frames of a base Four-Dimensional Cone-Beam CT (4D-CBCT) FDK reconstruction to a reference frame, providing a 4D transformation. This 4D transformation expresses a respiratory motion model and was used to apply a deformation during the backprojection operation to create a motion-compensated reconstruction. Two variables were assessed: reference image of registration (fixed or mean frame) and inclusion or exclusion of the motion-compensated reconstruction step. Fixed frame registration refers to using an actual frame from the 4D image (end of inhalation) as the target image. Mean frame registration refers to using a per iteration-estimated average frame image instead. Image quality was assessed in one clinical case through tissue boundary sharpness, noise, and presence of view-aliasing artifact. Results: Diaphragm edge sharpness (DES) was defined as the slope of the intensity gradient along the lung-diaphragm boundary. DES relative to the base 4D-CBCT reconstruction improved by 26.8% using fixed-frame registration alone; 15.5% using fixed-frame with motion-compensated reconstruction; decreased by 2.9% using mean-frame registration alone; and improved by 12.2% using mean-frame with motion-compensated reconstruction. Noise was reduced in soft tissue by 8.7% and 75.8% for the fixed-frame registration and registration with motion-compensation methods, respectively, and by 8.8% and 77.7% for the corresponding mean-frame methods. Reductions in undersampling artifacts were visible for all four methods relative to the base reconstruction while retaining higher frequency spatial details (i.e. blood vessels). Conclusion: A data-driven approach combining groupwise registration and motion-compensation has the potential to improve 4D-CBCT image quality. Adding motion compensation after groupwise registration reduced sharpness somewhat but resulted in reduced noise and structural artifacts. This work was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA166119. The authors have no conflicts of interest.

TH-CD-303-11: Simultaneous Reference-Image and Deformation-Field Reconstruction for 4DCBCT with Periodic and Non-Periodic Breathing Based On 5D Respiratory Motion Model

Purpose: 4DCBCT reconstructs a temporal sequence of CBCT images, and often involves the binning of projection data to each temporal phase. Thus it suffers from binning errors during non-periodic breathing. The 5D model parameterizes respiratory motion by breathing amplitude and rate (e.g. tidal volume and airflow) independent of breathing irregularity. We propose to develop a new 4DCBCT reconstruction method based on the 5D model. Methods: The proposed CBCT technique requires that the breathing amplitude and rate are measured during CBCT. The proposed 5D-model-based method reconstructs a reference breathing phase, and time-independent deformation fields corresponding to the motion due to amplitude and rate. Therefore, the new method reconstructs 7 three-dimensional spatial maps, with reduced numbers of unknowns compared with the standard 4DCBCT method when 10 or more breathing phases are reconstructed. In addition, the 5D method explicitly manages non-periodic breathing.The 5D method is formulated as a simultaneous reference-image and deformation-field reconstruction problem, with total-variation (TV) regularization for both images and motions. Then the 4DCBCT reconstruction is carried out by alternately solving image reconstruction and image registration, in which the split Bregman method is used to reconstruct the reference image, and the Chambolle's duality-based algorithm is used to reconstruct the differential deformation fields. Moreover, the proximal terms are applied to ensure the convergence of the developed optimization algorithm for this nonconvex problem. Results: The proposed 5D method was validated in simulation based on measurements of a lung patient, in comparison with the state-of-art spatiotemporal-TV-based 4DCBCT iterative method. And the results show that the 5D method had significantly improved image quality and quantitative error, for both periodic and non-periodic breathing studies. Conclusion: We have developed a new 4DCBCT method based on 5D respiratory motion model, with improved image reconstruction during both periodic and non-periodic breathing studies. Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500).

SU‐E‐J‐234: Application of a Breathing Motion Model to ViewRay Cine MR Images

Purpose:A respiratory motion model previously used to generate breathing‐gated CT images was used with cine MR images. Accuracy and predictive ability of the in‐plane models were evaluated.Methods:Sagittalplane cine MR images of a patient undergoing treatment on a ViewRay MRI/radiotherapy system were acquired before and during treatment. Images were acquired at 4 frames/second with 3.5 × 3.5 mm resolution and a slice thickness of 5 mm. The first cine frame was deformably registered to following frames. Superior/inferior component of the tumor centroid position was used as a breathing surrogate. Deformation vectors and surrogate measurements were used to determine motion model parameters. Model error was evaluated and subsequent treatment cines were predicted from breathing surrogate data. A simulated CT cine was created by generating breathing‐gated volumetric images at 0.25 second intervals along the measured breathing trace, selecting a sagittal slice and downsampling to the resolution of the MR cines. A motion model was built using the first half of the simulated cine data. Model accuracy and error in predicting the remaining frames of the cine were evaluated.Results:Mean difference between model predicted and deformably registered lung tissue positions for the 28 second preview MR cine acquired before treatment was 0.81 +/− 0.30 mm. The model was used to predict two minutes of the subsequent treatment cine with a mean accuracy of 1.59 +/− 0.63 mm.Conclusion:Inplane motion models were built using MR cine images and evaluated for accuracy and ability to predict future respiratory motion from breathing surrogate measurements. Examination of long term predictive ability is ongoing. The technique was applied to simulated CT cines for further validation, and the authors are currently investigating use of in‐plane models to update pre‐existing volumetric motion models used for generation of breathing‐gated CT planning images.

EP-1492: A framework combining image registration, respiratory motion models, and motion compensated image reconstruction

EP-1492: A framework combining image registration, respiratory motion models, and motion compensated image reconstruction

Respiratory motion model based correction for improving the targeting accuracy of MRI-guided intracardiac electrophysiology procedures

Background Recently, there is an increased interest in using MRI to guide electrophysiology (EP) procedures as an alternative to X-ray fluoroscopy guidance, due to its excellent soft tissue contrast and lack of radiation. However, there exist tradeoffs between different MRI guidance schemes. Realtime 2D MR sequences are able to capture heart motion during an interventional setting, while sacrificing imaging quality, whereas high-resolution prior 3D roadmaps are static and do not reflect the respiratory motion of the heart. In this work, we explore the feasibility of deriving a motion model from these two complementary datasets, and evaluate its potential for improving the targeting accuracy of MRI-guided EP procedures.

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.

Generation of fluoroscopic 3D images with a respiratory motion model based on an external surrogate signal

Respiratory motion during radiotherapy can cause uncertainties in definition of the target volume and in estimation of the dose delivered to the target and healthy tissue. In this paper, we generate volumetric images of the internal patient anatomy during treatment using only the motion of a surrogate signal. Pre-treatment four-dimensional CT imaging is used to create a patient-specific model correlating internal respiratory motion with the trajectory of an external surrogate placed on the chest. The performance of this model is assessed with digital and physical phantoms reproducing measured irregular patient breathing patterns. Ten patient breathing patterns are incorporated in a digital phantom. For each patient breathing pattern, the model is used to generate images over the course of thirty seconds. The tumor position predicted by the model is compared to ground truth information from the digital phantom. Over the ten patient breathing patterns, the average absolute error in the tumor centroid position predicted by the motion model is 1.4 mm. The corresponding error for one patient breathing pattern implemented in an anthropomorphic physical phantom was 0.6 mm. The global voxel intensity error was used to compare the full image to the ground truth and demonstrates good agreement between predicted and true images. The model also generates accurate predictions for breathing patterns with irregular phases or amplitudes.

Effect of respiratory motion on internal radiation dosimetry.

Estimation of the radiation dose to internal organs is essential for the assessment of radiation risks and benefits to patients undergoing diagnostic and therapeutic nuclear medicine procedures including PET. Respiratory motion induces notable internal organ displacement, which influences the absorbed dose for external exposure to radiation. However, to their knowledge, the effect of respiratory motion on internal radiation dosimetry has never been reported before. Thirteen computational models representing the adult male at different respiratory phases corresponding to the normal respiratory cycle were generated from the 4D dynamic XCAT phantom. Monte Carlo calculations were performed using the mcnp transport code to estimate the specific absorbed fractions (SAFs) of monoenergetic photons/electrons, the S-values of common positron-emitting radionuclides (C-11, N-13, O-15, F-18, Cu-64, Ga-68, Rb-82, Y-86, and I-124), and the absorbed dose of (18)F-fluorodeoxyglucose ((18)F-FDG) in 28 target regions for both the static (average of dynamic frames) and dynamic phantoms. The self-absorbed dose for most organs/tissues is only slightly influenced by respiratory motion. However, for the lung, the self-absorbed SAF is about 11.5% higher at the peak exhale phase than the peak inhale phase for photon energies above 50 keV. The cross-absorbed dose is obviously affected by respiratory motion for many combinations of source-target pairs. The cross-absorbed S-values for the heart contents irradiating the lung are about 7.5% higher in the peak exhale phase than the peak inhale phase for different positron-emitting radionuclides. For (18)F-FDG, organ absorbed doses are less influenced by respiratory motion. Respiration-induced volume variations of the lungs and the repositioning of internal organs affect the self-absorbed dose of the lungs and cross-absorbed dose between organs in internal radiation dosimetry. The dynamic anatomical model provides more accurate internal radiation dosimetry estimates for the lungs and abdominal organs based on realistic modeling of respiratory motion. This work also contributes to a better understanding of model-induced uncertainties in internal radiation dosimetry.

Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion

Conventional in-room cone-beam computed tomography (CBCT) lacks explicit representation of patient respiratory motion and usually has poor image quality and inaccurate CT numbers for target delineation and/or adaptive treatment planning. In-room four-dimensional (4D) CBCT image acquisition is still time consuming and suffers the same issue of poor image quality. To overcome this limitation, we developed a computational framework to digitally synthesize high-quality daily 4D CBCT images using the prior knowledge of motion and appearance learned from the planning 4D CT dataset. A patient-specific respiratory motion model was first constructed from the planning 4D CT images using principal component analysis of displacement vector fields across different respiratory phases. Subsequently, the respiratory motion model as well as the image content of the planning CT was spatially mapped onto the daily CBCT using deformable image registration. The synthesized 4D images possess explicit patient motion while maintaining the geometric accuracy of patient's anatomy at the time of treatment. We validated our model by quantitatively comparing the synthesized 4D CBCT against the 4D CT dataset acquired in the same day from protocol patients undergoing daily in-room CBCT setup and weekly 4D CT for treatment evaluation. Our preliminary results have demonstrated good agreement of contours in different motion phases between the synthesized and acquired scans. Various imaging artifacts were also suppressed and soft-tissue visibility was enhanced.

Investigation of a Respiratory Motion Model Based on an External Surrogate Signal With a Digital Phantom and Realistic Patient Breathing

Investigation of a Respiratory Motion Model Based on an External Surrogate Signal With a Digital Phantom and Realistic Patient Breathing

Sci-Sat AM: Stereo - 09: Accuracy of Liver Cancer Treatment on Cyberknife® with Synchrony™ Optical Tracking Throughout the Respiratory Cycle

The Cyberknife® robotic stereotactic body radiation therapy system is well-suited for treating liver lesions over the respiratory cycle as it includes room-mounted orthogonal x-ray tracking of internal fiducial markers and optical tracking of external markers. The Synchrony™ software generates a model of internal target positions during patient respiration and correlates it to the external optical tracking system for real-time optical-based position corrections of the linear accelerator during beam delivery. Although clinical studies have provided preliminary outcomes for liver lesions treated with the Cyberknife system, to date, there is little data demonstrating the ability of the Synchrony software to track targets in the liver, which deforms throughout the respiratory cycle. In this study, we investigated the respiratory motion model performance for predicting tumour motion. We conducted a retrospective analysis of fifteen liver cancer patients treated on the Cyberknife using the Synchrony optical tracking system. We analyzed Cyberknife tracking information stored in the log files to extract the left-right (LR), anterior-posterior (AP) and superior-inferior (SI) correlation errors between the model-predicted position and the internal fiducial centroid position determined by x-ray imaging. Only translational tracking and corrections were applied during treatment. Overall, the correlation errors were greatest in the SI direction. We calculated radial correlation errors, and determined that the 95th, 98th and 99th percentile errors were 3.4 mm, 4.4 mm and 5.1 mm, respectively. Based on translational correlation tracking errors we expect the clinical target volume will be within 3.4 mm of the planning target volume for 95 % of beam delivery time.

Evaluation of respiratory and cardiac motion correction schemes in dual gated PET/CT cardiac imaging.

Cardiac imaging suffers from both respiratory and cardiac motion. One of the proposed solutions involves double gated acquisitions. Although such an approach may lead to both respiratory and cardiac motion compensation there are issues associated with (a) the combination of data from cardiac and respiratory motion bins, and (b) poor statistical quality images as a result of using only part of the acquired data. The main objective of this work was to evaluate different schemes of combining binned data in order to identify the best strategy to reconstruct motion free cardiac images from dual gated positron emission tomography (PET) acquisitions. A digital phantom study as well as seven human studies were used in this evaluation. PET data were acquired in list mode (LM). A real-time position management system and an electrocardiogram device were used to provide the respiratory and cardiac motion triggers registered within the LM file. Acquired data were subsequently binned considering four and six cardiac gates, or the diastole only in combination with eight respiratory amplitude gates. PET images were corrected for attenuation, but no randoms nor scatter corrections were included. Reconstructed images from each of the bins considered above were subsequently used in combination with an affine or an elastic registration algorithm to derive transformation parameters allowing the combination of all acquired data in a particular position in the cardiac and respiratory cycles. Images were assessed in terms of signal-to-noise ratio (SNR), contrast, image profile, coefficient-of-variation (COV), and relative difference of the recovered activity concentration. Regardless of the considered motion compensation strategy, the nonrigid motion model performed better than the affine model, leading to higher SNR and contrast combined with a lower COV. Nevertheless, when compensating for respiration only, no statistically significant differences were observed in the performance of the two motion models considered. Superior image SNR and contrast were seen using the affine respiratory motion model in combination with the diastole cardiac bin in comparison to the use of the whole cardiac cycle. In contrast, when simultaneously correcting for cardiac beating and respiration, the elastic respiratory motion model outperformed the affine model. In this context, four cardiac bins associated with eight respiratory amplitude bins seemed to be adequate. Considering the compensation of respiratory motion effects only, both affine and elastic based approaches led to an accurate resizing and positioning of the myocardium. The use of the diastolic phase combined with an affine model based respiratory motion correction may therefore be a simple approach leading to significant quality improvements in cardiac PET imaging. However, the best performance was obtained with the combined correction for both cardiac and respiratory movements considering all the dual-gated bins independently through the use of an elastic model based motion compensation.