Patient-specific Respiratory Motion Research Articles

Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study.

In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs.

Interplay Effect of Splenic Motion for Total Lymphoid Irradiation in Pediatric Proton Therapy.

(1) Background: The most significant cause of an unacceptable deviation from the planned dose during respiratory motion is the interplay effect. We examined the correlation between the magnitude of splenic motion and its impact on plan quality for total lymphoid irradiation (TLI); (2) Methods: Static and 4D CT images from ten patients were used for interplay effect simulations. Patients' original plans were optimized based on the average CT extracted from the 4D CT and planned with two posterior beams using scenario-based optimization (±3 mm of setup and ±3% of range uncertainty) and gradient matching at the level of mid-spleen. Dynamically accumulated 4D doses (interplay effect dose) were calculated based on the time-dependent delivery sequence of radiation fluence across all phases of the 4D CT. Dose volume parameters for each simulated treatment delivery were evaluated for plan quality; (3) Results: Peak-to-peak splenic motion (≤12 mm) was measured from the 4D CT of ten patients. Interplay effect simulations revealed that the ITV coverage of the spleen remained within the protocol tolerance for splenic motion, ≤8 mm. The D100% coverage for ITV spleen decreased from 95.0% (nominal plan) to 89.3% with 10 mm and 87.2% with 12 mm of splenic motion; (4) Conclusions: 4D plan evaluation and robust optimization may overcome problems associated with respiratory motion in proton TLI treatments. Patient-specific respiratory motion evaluations are essential to confirming adequate dosimetric coverage when proton therapy is utilized.

Evaluation of Patient-Specific Quality Control (QC) for Markerless Dynamic Tumor Tracking (MDTT) Deliveries

To report on the patient-specific QC for MDTT for liver and lung tumors treated using the dome-of-liver/diaphragm as a surrogate for the target position and compare to the QC of conventional fiducial-based tracking. Vero4DRT is a linear accelerator that has a gimbal-mounted waveguide and collimation system allowing the radiation beam to perform dynamic tumor tracking based on a patient's respiratory motion. A correlation model is built between an external IR marker placed on the chest and an internal structure, which may be implanted fiducial surrogates or in the case of MDTT, an anatomic landmark. During treatment an orthogonal kV image pair is taken every second. The auto detected internal structure position versus the predicted position is recorded in treatment log files. Five IMRT tracking plans were delivered to two commercial motion phantoms with in-house additions for patient-specific tracking QC. For point dose measurements, a 0.6cc farmer chamber was placed inside the dynamic tracking phantom which contains fiducials for fiducial-based tracking delivery and an anatomic landmark for MDTT. For 2D dose distribution measurements, a radiographic film insert for the Quasar Respiratory Motion Phantom was constructed. The phantom contains both fiducials and has a liver-dome shape at one end to facilitate MDTT. Both motion platforms were interfaced with software that enabled patient-specific respiratory motion traces acquired during a 4DCT scan. Each plan was delivered three times on each phantom: 1) in static mode, 2) during fiducial-based tracking, and 3) during MDTT. Chamber measurements taken during static and tracked delivery were compared to the average chamber dose from the treatment planning software (TPS). Film distributions measured during tracked deliveries were compared to the static film distribution using gamma analysis with FILMQA PRO software. Finally, dynamic tracking treatment statistics, extracted from log files, were compared between fiducial and markerless tracking deliveries. All chamber measurements resulted in dose differences <2.5% compared with the TPS. Dose differences between fiducial-based and MDTT were 0.26% on average (range 0.03-0.62%). For film dose map analysis, gamma pass rates were >95% for all plans for all tracking methods. The average gamma pass rate for 3%/3mm was 99.4% (fiducial-based) vs 98.4% (markerless). For 2%/2mm, the average gamma pass rate was 96.4% (fiducial-based tracking) vs 95.6% (MDTT). Dynamic treatment statistics from logs files reported an average 3D absolute deviation from predicted position of 0.52mm (±0.24) for fiducial tracking and 1.19mm (±0.50) for MDTT. Both fiducial-based and MDTT plans meet passing criteria for patient-specific QC. 3D absolute deviation of detected versus predicted position are larger for MDTT compared to fiducial tracking, however no significant impact on dose delivery was measured.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

Patient-specific respiratory motion management using lung tumors vs fiducial markers for real-time tumor-tracking stereotactic body radiotherapy.

In real-time lung tumor-tracking stereotactic body radiotherapy (SBRT), tracking accuracy is related to radiotherapy efficacy. This study aimed to evaluate the respiratory movement relationship between a lung tumor and a fiducial marker position in each direction using four-dimensional (4D) computed tomography (CT) images. A series of 31 patients with a fiducial marker for lung SBRT was retrospectively analyzed using 4DCT. In the upper (UG) and middle and lower lobe groups (MLG), the cross-correlation coefficients of respiratory movement between the lung tumor and fiducial marker position in four directions (anterior-posterior, left-right, superior-inferior [SI], and three-dimensional [3D]) were calculated for each gating window (≤1, ≤2, and≤3mm). Subsequently, the proportions of phase numbers in unplanned irradiation (with lung tumors outside the gating window and fiducial markers inside the gating window) were calculated for each gating window. In the SI and 3D directions, the cross-correlation coefficients were significantly different between UG (mean r=0.59, 0.63, respectively) and MLG (mean r=0.95, 0.97, respectively). In both the groups, the proportions of phase numbers in unplanned irradiation were 11%, 28%, and 63% for the≤1-, ≤2-, and≤3-mm gating windows, respectively. Compared with MLG, fiducial markers for UG have low cross-correlation coefficients between the lung tumor and the fiducial marker position. Using 4DCT to assess the risk of unplanned irradiation in a gating window setting and selecting a high cross-correlation coefficient fiducial marker in advance are important for accurate treatment using lung SBRT.

A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model.

Due to respiratory motion, precise tracking of lung nodule movement is a persistent challenge for guiding percutaneous lung biopsy during image-guided intervention. We developed an automated image-guided system incorporating effective and robust tracking algorithms to address this challenge. Accurate lung motion prediction and personalized image-guided intervention are the key technological contributions of this work. A patient-specific respiratory motion model is developed to predict pulmonary movements of individual patients. It is based on the relation between the artificial 4D CT and corresponding positions tracked by position sensors attached on the chest using an electromagnetic (EM) tracking system. The 4D CT image of the thorax during breathing is calculated through deformable registration of two 3D CT scans acquired at inspiratory and expiratory breath-hold. The robustness and accuracy of the image-guided intervention system were assessed on a static thorax phantom under differentclinical parametric combinations. Real 4D CT images of ten patients were used to evaluate the accuracy of the respiratory motion model. The mean error of the model in different breathing phases was 1.59 ± 0.66mm. Using a static thorax phantom, we achieved an average targeting accuracy of 3.18 ± 1.2mm across 50 independent tests with different intervention parameters. The positive results demonstrate the robustness and accuracy of our system for personalized lung cancer intervention. The proposed system integrates a patient-specific respiratory motion compensation model to reduce the effect of respiratory motion during percutaneous lung biopsy and help interventional radiologists target the lesion efficiently. Our preclinical studies indicate that the image-guided system has the ability to accurately predict and track lung nodules of individual patients and has the potential for use in the diagnosis and treatment of early stage lung cancer.

A novel tool for motion-related dose inaccuracies reduction in 99mTc-MAA SPECT/CT images for SIRT planning

IntroductionIn Selective Internal Radiation Therapy (SIRT), 90Y is administered to primary/secondary hepatic lesions. An accurate pre-treatment planning using 99mTc-MAA SPECT/CT allows the assessment of its feasibility and of the activity to be injected. Unfortunately, SPECT/CT suffers from patient-specific respiratory motion which causes artifacts and absorbed dose inaccuracies. In this study, a data-driven solution was developed to correct the respiratory motion. MethodsThe tool realigns the barycenter of SPECT projection images and shifts them to obtain a fine registration with the attenuation map. The tool was validated using a modified dynamic phantom with several breathing patterns. We compared the absorbed dose distributions derived from uncorrected(Dm)/corrected(Dc) images with static ones(Ds) in terms of γ-passing rates, 210 Gy isodose volumes, dose-volume histograms and percentage differences of mean doses (i.e., ΔD¯m and ΔD¯c, respectively). The tool was applied to twelve SIRT patients and the Bland-Altman analysis was performed on mean doses. ResultsIn the phantom study, the agreement between Dc and Ds was higher (γ-passing rates generally > 90%) than Dm and Ds. The isodose volumes in Dc were closer than Dm to Ds, with differences up to 10% and 30% respectively. A reduction from a median ΔD¯m = -19.3% to ΔD¯c = -0.9%, from ΔD¯m = -42.8% to ΔD¯c = -7.0% and from ΔD¯m = 1586% to ΔD¯c = 47.2% was observed in liver-, tumor- and lungs-like structures. The Bland-Altman analysis on patients showed variations (±50 Gy) and (±4 Gy) between D¯c and D¯m of tumor and lungs, respectively. ConclusionThe proposed tool allowed the correction of 99mTc-MAA SPECT/CT images, improving the accuracy of the absorbed dose distribution.

Liver-ultrasound based motion modelling to estimate 4D dose distributions for lung tumours in scanned proton therapy

Motion mitigation strategies are crucial for scanned particle therapy of mobile tumours in order to prevent geometrical target miss and interplay effects. We developed a patient-specific respiratory motion model based on simultaneously acquired time-resolved volumetric MRI and 2D abdominal ultrasound images. We present its effects on 4D pencil beam scanned treatment planning and simulated dose distributions. Given an ultrasound image of the liver and the diaphragm, principal component analysis and Gaussian process regression were applied to infer dense motion information of the lungs. 4D dose calculations for scanned proton therapy were performed using the estimated and the corresponding ground truth respiratory motion; the differences were compared by dose difference volume metrics. We performed this simulation study on 10 combined CT and 4DMRI data sets where the motion characteristics were extracted from 5 healthy volunteers and fused with the anatomical CT data of two lung cancer patients. Median geometrical estimation errors below 2 mm for all data sets and maximum dose differences of = 43.2% and = 16.3% were found. Moreover, it was shown that abdominal ultrasound imaging allows to monitor organ drift. This study demonstrated the feasibility of the proposed ultrasound-based motion modelling approach for its application in scanned proton therapy of lung tumours.

4D CT Deformable Registration Using Unsupervised Deep Learning for Lung Stereotactic Body Radiation Therapy

In current lung stereotactic body radiation therapy (SBRT), physicians assess the tumor motion on the movie loops of the 4D CT images to select treatment phases for gated lung SBRT. However, physician’s manual selection of the treatment phase is time-consuming and subjective. To address this challenge, we propose to develop a 4D CT deformable registration method, namely LungRegNet, to accurately assess the respiratory motion of lung/tumor using 4D CT images which could help choose the optimal treatment phases for lung SBRT. The LungRegNet uses deep learning and consists of two sub-networks which are CoarseNet and FineNet. As the name suggests, CoarseNet predicts large lung motion on a coarse scale image while FineNet predicts local lung motion on a fine scale image. Both the CoarseNet and FineNet include a generator and a discriminator. The generator is trained to directly predict the displacement vector field (DVF) to deform the moving image. The discriminator is used to introduce additional DVF regularization to encourage realistic motion prediction by distinguishing the deformed images from the original images. CoarseNet is first trained to deform the moving images. The deformed images are then used by the FineNet for FineNet training. To increase the registration accuracy of the LungRegNet, we generate vessel-enhanced images by generating pulmonary vasculature probability maps prior to the network prediction. We performed five-fold cross validation on 10 lung 4D CT datasets, in which 10 CT images were acquired during a whole respiratory period for each patient. In addition, we tested our method using 10 datasets from public database DIRLAB that provided 300 manual landmark pairs per case for target registration error (TRE) calculation. The mean and standard deviation of TRE was 1.00±0.53mm for our patients and 1.59±1.58mm for the external DIRLAB datasets. The results indicated that our LungRegNet achieved better registration accuracy in terms of TRE than other available state-of-the-art deep learning-based methods on DIRLAB datasets. We have developed a novel unsupervised deep learning-based method to rapidly register 4D CT lung images and demonstrated its clinical feasibility and accuracy. This LungRegNet could accurately estimate patient-specific respiratory motion for lung SBRT to help physicians objectively find the optimal treatment phases, and enable more accurate accumulative dose calculation and thus more accurate plan evaluation. This tool will be further tested in prospective trials to determine if it could improve lung tumor delineation, reduce dose delivered to the organs-at-risk, and potentially improve survival of many lung cancer patients.

Mixed reality based respiratory liver tumor puncture navigation

This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation (RFA). Our system contains an optical see-through head-mounted display device (OST-HMD), Microsoft HoloLens for perfectly overlaying the virtual information on the patient, and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene. Compared with traditional navigation method with CT, our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system. In addition, to alleviate the difficulty during needle placement induced by respiratory motion, we reconstruct the patient-specific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver tumors. The proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE, which has the potential to be applied in clinical RFA guidance.

A Kalman-Based Approach With EM Optimization for Respiratory Motion Modeling in Medical Imaging

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.

Patient specific prospective respiratory motion correction for efficient, free-breathing cardiovascular MRI.

To develop a patient-specific respiratory motion correction technique with true 100% acquisition efficiency. A short training scan consisting of a series of single heartbeat images, each acquired with a preceding diaphragmatic navigator, was performed to fit a model relating the patient-specific 3D respiratory motion of the heart-to-diaphragm position. The resulting motion model was then used to update the imaging plane in real-time to correct for translational motion based on respiratory position provided by the navigator. The method was tested in a group of 11 volunteers with 5 separate free-breathing acquisitions: FB, no motion correction; FB-TF, free breathing with a linear tracking factor; Nav Gate, navigator gating; Nav Gate-TF, navigator gating with a tracking factor; and PROCO, prospective motion correction (proposed). Each acquisition lasted for 50 accepted heartbeats, where non-gated scans had a 100% acceptance rate, and gated scans accepted data only within a ±4 mm navigator window. Retrospective image registration was used to measure residual motion and determine the effectiveness of each method. PROCO reduced the range/RMSE of residual motion to 4.08 ± 1.4/0.90 ± 0.3 mm, compared to 10.78 ± 6.9/2.97 ± 2.2 mm for FB, 5.32 ± 2.92/1.24 ± 0.8 mm for FB-TF, 4.08 ± 1.6/0.93 ± 0.4 mm for Nav Gate, and 2.90 ± 1.0/0.63 ± 0.2 mm for Nav Gate-TF. Nav Gate and Nav Gate-TF reduced scan efficiency to 48.84 ± 9.31% and 54.54 ± 10.12%, respectively. PROCO successfully limited the residual motion in single-shot imaging to the level of traditional navigator gating while maintaining 100% acquisition efficiency.

Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis.

Accurate tracking and modeling of internal and external respiratory motion in the thoracic and abdominal regions of a human body is a highly discussed topic in external beam radiotherapy treatment. Errors in target/normal tissue delineation and dose calculation and the increment of the healthy tissues being exposed to high radiation doses are some of the unsolicited problems caused due to inaccurate tracking of the respiratory motion. Many related works have been introduced for respiratory motion modeling, but a majority of them highly depend on radiography/fluoroscopy imaging, wearable markers or surgical node implanting techniques. We, in this article, propose a new respiratory motion tracking approach by exploiting the advantages of an RGB-D camera. First, we create a patient-specific respiratory motion model using principal component analysis (PCA) removing the spatial and temporal noise of the input depth data. Then, this model is utilized for real-time external respiratory motion measurement with high accuracy. Additionally, we introduce a marker-based depth frame registration technique to limit the measuring area into an anatomically consistent region that helps to handle the patient movements during the treatment. We achieved a 0.97 correlation comparing to a spirometer and 0.53 mm average error considering a laser line scanning result as the ground truth. As future work, we will use this accurate measurement of external respiratory motion to generate a correlated motion model that describes the movements of internal tumors.

Patient-specific respiratory motion estimation using Sparse Motion Field Presentation.

Respiratory motion estimation plays a significant role in radiation therapy. Previous motion estimation approaches usually depended on 4DCT, which introduced extra radio dose for patients, and the local motion details were ignored in the statistical model. In this paper, we propose a novel estimation framework, which employs the Sparse Motion Field Presentation (SMFP) method to obtain a coarse motion estimation which preserves patient-specific respiratory motion details and an Adaptive Variable Coefficient (AVC) motion prior registration approach is applied for the accurate estimation. The experimental results show that the proposed framework effectively preserved the local motion details and achieved more accurate motion estimations compared to the Mean Motion Model (MMM) and the Principal Component Analysis (PCA) model. We achieved motion estimations for diaphragmatic breathing type, thoracic breathing type and mixed type, respectively. The accuracy measured in the average symmetric surface distance (standard deviation) were 1.9(0.9) mm, 2.4(1.1) mm and 2.2(1.0) mm, when the sum of squared intensity difference (SSD) were 5.0, 6.1 and 5.6, respectively.

Autoadaptive motion modelling for MR-based respiratory motion estimation.

Respiratory motion poses significant challenges in image-guided interventions. In emerging treatments such as MR-guided HIFU or MR-guided radiotherapy, it may cause significant misalignments between interventional road maps obtained pre-procedure and the anatomy during the treatment, and may affect intra-procedural imaging such as MR-thermometry. Patient specific respiratory motion models provide a solution to this problem. They establish a correspondence between the patient motion and simpler surrogate data which can be acquired easily during the treatment. Patient motion can then be estimated during the treatment by acquiring only the simpler surrogate data. In the majority of classical motion modelling approaches once the correspondence between the surrogate data and the patient motion is established it cannot be changed unless the model is recalibrated. However, breathing patterns are known to significantly change in the time frame of MR-guided interventions. Thus, the classical motion modelling approach may yield inaccurate motion estimations when the relation between the motion and the surrogate data changes over the duration of the treatment and frequent recalibration may not be feasible. We propose a novel methodology for motion modelling which has the ability to automatically adapt to new breathing patterns. This is achieved by choosing the surrogate data in such a way that it can be used to estimate the current motion in 3D as well as to update the motion model. In particular, in this work, we use 2D MR slices from different slice positions to build as well as to apply the motion model. We implemented such an autoadaptive motion model by extending our previous work on manifold alignment. We demonstrate a proof-of-principle of the proposed technique on cardiac gated data of the thorax and evaluate its adaptive behaviour on realistic synthetic data containing two breathing types generated from 6 volunteers, and real data from 4 volunteers. On synthetic data the autoadaptive motion model yielded 21.45% more accurate motion estimations compared to a non-adaptive motion model 10min after a change in breathing pattern. On real data we demonstrated the method's ability to maintain motion estimation accuracy despite a drift in the respiratory baseline. Due to the cardiac gating of the imaging data, the method is currently limited to one update per heart beat and the calibration requires approximately 12min of scanning. Furthermore, the method has a prediction latency of 800ms. These limitations may be overcome in future work by altering the acquisition protocol.

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.

WE-AB-204-03: A Novel 3D Printed Phantom for 4D PET/CT Imaging and SIB Radiotherapy Verification

Purpose: To construct and test a 3D printed phantom designed to mimic variable PET tracer uptake seen in lung tumor volumes. To assess segmentation accuracy of sub-volumes of the phantom following 4D PET/CT scanning with ideal and patient-specific respiratory motion. To plan, deliver and verify delivery of PET-driven, gated, simultaneous integrated boost (SIB) radiotherapy plans. Methods: A set of phantoms and inserts were designed and manufactured for a realistic representation of lung cancer gated radiotherapy steps from 4D PET/CT scanning to dose delivery. A cylindrical phantom (40x 120 mm) holds inserts for PET/CT scanning. The novel 3D printed insert dedicated to 4D PET/CT mimics high PET tracer uptake in the core and lower uptake in the periphery. This insert is a variable density porous cylinder (22.12×70 mm), ABS-P430 thermoplastic, 3D printed by uPrint SE Plus with inner void volume (5.5×42 mm). The square pores (1.8×1.8 mm2 each) fill 50% of outer volume, resulting in a 2:1 SUV ratio of PET-tracer in the void volume with respect to porous volume. A matching in size cylindrical phantom is dedicated to validate gated radiotherapy. It contains eight peripheral holes matching the location of the porous part of the 3D printed insert, and one central hole. These holes accommodate adaptors for Farmer-type ion chamber and cells vials. Results: End-to-end test were performed from 4D PET/CT scanning to transferring data to the planning system and target volume delineation. 4D PET/CT scans were acquired of the phantom with different respiratory motion patterns and gating windows. A measured 2:1 18F-FDG SUV ratio between inner void and outer volume matched the 3D printed design. Conclusion: The novel 3D printed phantom mimics variable PET tracer uptake typical of tumors. Obtained 4D PET/CT scans are suitable for segmentation, treatment planning and delivery in SIB gated treatments of NSCLC.

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.

Respiratory motion estimation in x-ray angiography for improved guidance during coronary interventions

During percutaneous coronary interventions (PCI) catheters and arteries are visualized by x-ray angiography (XA) sequences, using brief contrast injections to show the coronary arteries. If we could continue visualizing the coronary arteries after the contrast agent passed (thus in non-contrast XA frames), we could potentially lower contrast use, which is advantageous due to the toxicity of the contrast agent. This paper explores the possibility of such visualization in mono-plane XA acquisitions with a special focus on respiratory based coronary artery motion estimation.We use the patient specific coronary artery centerlines from pre-interventional 3D CTA images to project on the XA sequence for artery visualization. To achieve this, a framework for registering the 3D centerlines with the mono-plane 2D + time XA sequences is presented. During the registration the patient specific cardiac and respiratory motion is learned. We investigate several respiratory motion estimation strategies with respect to accuracy, plausibility and ease of use for motion prediction in XA frames with and without contrast. The investigated strategies include diaphragm motion based prediction, and respiratory motion extraction from the guiding catheter tip motion. We furthermore compare translational and rigid respiratory based heart motion.We validated the accuracy of the 2D/3D registration and the respiratory and cardiac motion estimations on XA sequences of 12 interventions. The diaphragm based motion model and the catheter tip derived motion achieved 1.58 mm and 1.83 mm median 2D accuracy, respectively. On a subset of four interventions we evaluated the artery visualization accuracy for non-contrast cases. Both diaphragm, and catheter tip based prediction performed similarly, with about half of the cases providing satisfactory accuracy (median error < 2 mm).

PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions.

PurposeIntraoperative coronary motion modeling with motion surrogates enables prospective motion prediction in X-ray angiograms (XA) for percutaneous coronary interventions. The motion of coronary arteries is mainly affected by patients breathing and heartbeat. Purpose of our work is therefore to extract coronary motion surrogates that are related to respiratory and cardiac motion. In particular, we focus on respiratory motion surrogates extraction in this paper.MethodsWe propose a fast automatic method for extracting patient-specific respiratory motion surrogate from cardiac XA. The method starts with an image preprocessing step to remove all tubular and curvilinear structures from XA images, such as vessels and guiding catheters, followed by principal component analysis on pixel intensities. The respiratory motion surrogate of an XA image is then obtained by projecting its vessel-removed image onto the first principal component.ResultsThis breathing motion surrogate was demonstrated to get high correlation with ground truth diaphragm motion (correlation coefficient over 0.9 on average). In comparison with other related methods, the method we developed did not show significant difference (p>0.05), but did improve robustness and run faster on monoplane and biplane data in retrospective and prospective scenarios.Conclusionswe developed and evaluated a method in extraction of respiratory motion surrogate from interventional X-ray images that is easy to implement and runs in real time and thus allows extracting respiratory motion surrogates during interventions.