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Title: SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging

Abstract

Purpose: kV fluoroscopic imaging combined with MV treatment beam imaging has been investigated for intrafractional motion monitoring and correction. It is, however, subject to additional kV imaging dose to normal tissue. To balance tracking accuracy and imaging dose, we previously proposed an adaptive imaging strategy to dynamically decide future imaging type and moments based on motion tracking uncertainty. kV imaging may be used continuously for maximal accuracy or only when the position uncertainty (probability of out of threshold) is high if a preset imaging dose limit is considered. In this work, we propose more accurate methods to estimate tracking uncertainty through analyzing acquired data in real-time. Methods: We simulated motion tracking process based on a previously developed imaging framework (MV + initial seconds of kV imaging) using real-time breathing data from 42 patients. Motion tracking errors for each time point were collected together with the time point’s corresponding features, such as tumor motion speed and 2D tracking error of previous time points, etc. We tested three methods for error uncertainty estimation based on the features: conditional probability distribution, logistic regression modeling, and support vector machine (SVM) classification to detect errors exceeding a threshold. Results: For conditional probability distribution, polynomial regressionsmore » on three features (previous tracking error, prediction quality, and cosine of the angle between the trajectory and the treatment beam) showed strong correlation with the variation (uncertainty) of the mean 3D tracking error and its standard deviation: R-square = 0.94 and 0.90, respectively. The logistic regression and SVM classification successfully identified about 95% of tracking errors exceeding 2.5mm threshold. Conclusion: The proposed methods can reliably estimate the motion tracking uncertainty in real-time, which can be used to guide adaptive additional imaging to confirm the tumor is within the margin or initialize motion compensation if it is out of the margin.« less

Authors:
 [1];  [2]; ;  [3]
  1. Capital Medical University, Beijing, Beijing (China)
  2. Yale New Haven Hospital, New Haven, CT (United States)
  3. Yale University School of Medicine, New Haven, CT (United States)
Publication Date:
OSTI Identifier:
22649297
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
61 RADIATION PROTECTION AND DOSIMETRY; 60 APPLIED LIFE SCIENCES; BIOMEDICAL RADIOGRAPHY; DOSE LIMITS; ERRORS; IMAGE PROCESSING

Citation Formats

Yan, H, Chen, Z, Nath, R, and Liu, W. SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging. United States: N. p., 2016. Web. doi:10.1118/1.4956933.
Yan, H, Chen, Z, Nath, R, & Liu, W. SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging. United States. doi:10.1118/1.4956933.
Yan, H, Chen, Z, Nath, R, and Liu, W. Wed . "SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging". United States. doi:10.1118/1.4956933.
@article{osti_22649297,
title = {SU-G-BRA-09: Estimation of Motion Tracking Uncertainty for Real-Time Adaptive Imaging},
author = {Yan, H and Chen, Z and Nath, R and Liu, W},
abstractNote = {Purpose: kV fluoroscopic imaging combined with MV treatment beam imaging has been investigated for intrafractional motion monitoring and correction. It is, however, subject to additional kV imaging dose to normal tissue. To balance tracking accuracy and imaging dose, we previously proposed an adaptive imaging strategy to dynamically decide future imaging type and moments based on motion tracking uncertainty. kV imaging may be used continuously for maximal accuracy or only when the position uncertainty (probability of out of threshold) is high if a preset imaging dose limit is considered. In this work, we propose more accurate methods to estimate tracking uncertainty through analyzing acquired data in real-time. Methods: We simulated motion tracking process based on a previously developed imaging framework (MV + initial seconds of kV imaging) using real-time breathing data from 42 patients. Motion tracking errors for each time point were collected together with the time point’s corresponding features, such as tumor motion speed and 2D tracking error of previous time points, etc. We tested three methods for error uncertainty estimation based on the features: conditional probability distribution, logistic regression modeling, and support vector machine (SVM) classification to detect errors exceeding a threshold. Results: For conditional probability distribution, polynomial regressions on three features (previous tracking error, prediction quality, and cosine of the angle between the trajectory and the treatment beam) showed strong correlation with the variation (uncertainty) of the mean 3D tracking error and its standard deviation: R-square = 0.94 and 0.90, respectively. The logistic regression and SVM classification successfully identified about 95% of tracking errors exceeding 2.5mm threshold. Conclusion: The proposed methods can reliably estimate the motion tracking uncertainty in real-time, which can be used to guide adaptive additional imaging to confirm the tumor is within the margin or initialize motion compensation if it is out of the margin.},
doi = {10.1118/1.4956933},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: Multiple targets with large intrafraction independent motion are often involved in advanced prostate, lung, abdominal, and head and neck cancer radiotherapy. Current standard of care treats these with the originally planned fields, jeopardizing the treatment outcomes. A real-time multi-leaf collimator (MLC) tracking method has been developed to address this problem for the first time. This study evaluates the geometric uncertainty of the multi-target tracking method. Methods: Four treatment scenarios are simulated based on a prostate IMAT plan to treat a moving prostate target and static pelvic node target: 1) real-time multi-target MLC tracking; 2) real-time prostate-only MLC tracking; 3)more » correcting for prostate interfraction motion at setup only; and 4) no motion correction. The geometric uncertainty of the treatment is assessed by the sum of the erroneously underexposed target area and overexposed healthy tissue areas for each individual target. Two patient-measured prostate trajectories of average 2 and 5 mm motion magnitude are used for simulations. Results: Real-time multi-target tracking accumulates the least uncertainty overall. As expected, it covers the static nodes similarly well as no motion correction treatment and covers the moving prostate similarly well as the real-time prostate-only tracking. Multi-target tracking reduces >90% of uncertainty for the static nodal target compared to the real-time prostate-only tracking or interfraction motion correction. For prostate target, depending on the motion trajectory which affects the uncertainty due to leaf-fitting, multi-target tracking may or may not perform better than correcting for interfraction prostate motion by shifting patient at setup, but it reduces ∼50% of uncertainty compared to no motion correction. Conclusion: The developed real-time multi-target MLC tracking can adapt for the independently moving targets better than other available treatment adaptations. This will enable PTV margin reduction to minimize health tissue toxicity while remain tumor coverage when treating advanced disease with independently moving targets involved. The authors acknowledge funding support from the Australian NHMRC Australia Fellowship and NHMRC Project Grant No. APP1042375.« less
  • Purpose: A clinical trial on stereotactic body radiation therapy (SBRT) for high-risk prostate cancer is undergoing at our institution. In addition to escalating dose to the prostate, we have increased dose to intra-prostatic lesions. Intra-fractional prostate motion deteriorates well planned radiation dose, especially for the small intra-prostatic lesions. To solve this problem, we have developed a motion tracking and 4D dose-reconstruction system to facilitate adaptive re-planning. Methods: Patients in the clinical trial were treated with VMAT using four arcs and 10 FFF beam. KV triggered x-ray projections were taken every 3 sec during delivery to acquire 2D projections of 3Dmore » anatomy at the direction orthogonal to the therapeutic beam. Each patient had three implanted prostate markers. Our developed system first determined 2D projection locations of these markers and then 3D prostate translation and rotation via 2D/3D registration of the markers. Using delivery log files, our GPU-based Monte Carlo tool (goMC) reconstructed dose corresponding to each triggered image. The calculated 4D dose distributions were further aggregated to yield the delivered dose. Results: We first tested each module in our system. MC dose engine were commissioned to our treatment planning system with dose difference of <0.5%. For motion tracking, 1789 kV projections from 7 patients were acquired. The 2D marker location error was <1 mm. For 3D motion tracking, root mean square (RMS) errors along LR, AP, and CC directions were 0.26mm, 0.36mm, and 0.01mm respectively in simulation studies and 1.99mm, 1.37mm, and 0.22mm in phantom studies. We also tested the entire system workflow. Our system was able to reconstruct delivered dose. Conclusion: We have developed a functional intra-fractional motion tracking and 4D dose re-construction system to support our clinical trial on adaptive high-risk prostate cancer SBRT. Comprehensive evaluations have shown the capability and accuracy of our system.« less
  • Purpose: To detect target position on kV X-ray fluoroscopic images using a feature-based tracking algorithm, Accelerated-KAZE (AKAZE), for markerless real-time tumor tracking (RTTT). Methods: Twelve lung cancer patients treated with RTTT on the Vero4DRT (Mitsubishi Heavy Industries, Japan, and Brainlab AG, Feldkirchen, Germany) were enrolled in this study. Respiratory tumor movement was greater than 10 mm. Three to five fiducial markers were implanted around the lung tumor transbronchially for each patient. Before beam delivery, external infrared (IR) markers and the fiducial markers were monitored for 20 to 40 s with the IR camera every 16.7 ms and with an orthogonalmore » kV x-ray imaging subsystem every 80 or 160 ms, respectively. Target positions derived from the fiducial markers were determined on the orthogonal kV x-ray images, which were used as the ground truth in this study. Meanwhile, tracking positions were identified by AKAZE. Among a lot of feature points, AKAZE found high-quality feature points through sequential cross-check and distance-check between two consecutive images. Then, these 2D positional data were converted to the 3D positional data by a transformation matrix with a predefined calibration parameter. Root mean square error (RMSE) was calculated to evaluate the difference between 3D tracking and target positions. A total of 393 frames was analyzed. The experiment was conducted on a personal computer with 16 GB RAM, Intel Core i7-2600, 3.4 GHz processor. Results: Reproducibility of the target position during the same respiratory phase was 0.6 +/− 0.6 mm (range, 0.1–3.3 mm). Mean +/− SD of the RMSEs was 0.3 +/− 0.2 mm (range, 0.0–1.0 mm). Median computation time per frame was 179 msec (range, 154–247 msec). Conclusion: AKAZE successfully and quickly detected the target position on kV X-ray fluoroscopic images. Initial results indicate that the differences between 3D tracking and target position would be clinically acceptable.« less
  • Purpose: To evaluate the effect of inter- and intra-fractional tumor motion on the error in four-dimensional computed tomography (4DCT) maximal intensity projection (MIP)–based lung tumor internal target volumes (ITV), using deformable image registration of real-time 2D-sagital cine-mode MRI acquired during lung SBRT treatments. Methods: Five lung tumor patients underwent free breathing SBRT treatment on the ViewRay, with dose prescribed to PTV (4DCT MIP-based ITV+3–6mm margin). Sagittal slice cine-MR images (3.5×3.5mm pixels) were acquired through the center of the tumor at 4 frames per second throughout the treatments (3–4 fractions of 21–32 minutes duration). Tumor GTVs were contoured on the firstmore » frame of the cine and tracked throughout the treatment using off-line optical-flow based deformable registration implemented on a GPU cluster. Pseudo-4DCT MIP-based ITVs were generated from MIPs of the deformed GTV contours limited to short segments of image data. All possible pseudo-4DCT MIP-based ITV volumes were generated with 1s resolution and compared to the ITV volume of the entire treatment course. Varying pseudo-4DCT durations from 10-50s were analyzed. Results: Tumors were covered in their entirety by PTV in the patients analysed here. However, pseudo-4DCT based ITV volumes were observed that were as small as 29% of the entire treatment-ITV, depending on breathing irregularity and the duration of pseudo-4DCT. With an increase in duration of pseudo-4DCT from 10–50s the minimum volume acquired from 95% of all pseudo-4DCTs increased from 62%–81% of the treatment ITV. Conclusion: A 4DCT MIP-based ITV offers a ‘snap-shot’ of breathing motion for the brief period of time the tumor is imaged on a specific day. Real time MRI over prolonged periods of time and over multiple treatment fractions shows that the accuracy of this snap-shot varies according to inter- and intra-fractional tumor motion. Further work is required to investigate the dosimetric effect of these results.« less
  • Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associatedmore » algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately improving tumor motion management for radiation therapy of cancer patients.« less