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Title: SU-G-BRA-06: Quantification of Tracking Performance of a Multi-Layer Electronic Portal Imaging Device

Abstract

Purpose: The purpose of this study was to quantify the improvement in tumor tracking, with and without fiducial markers, afforded by employing a multi-layer (MLI) electronic portal imaging device (EPID) over the current state-of-the-art, single-layer, digital megavolt imager (DMI) architecture. Methods: An ideal observer signal-to-noise ratio (d’) approach was used to quantify the ability of an MLI EPID and a current, state-of-the-art DMI EPID to track lung tumors from the treatment beam’s-eye-view. Using each detector modulation transfer function (MTF) and noise power spectrum (NPS) as inputs, a detection task was employed with object functions describing simple three-dimensional Cartesian shapes (spheres and cylinders). Marker-less tumor tracking algorithms often use texture discrimination to differentiate benign and malignant tissue. The performance of such algorithms is simulated by employing a discrimination task for the ideal observer, which measures the ability of a system to differentiate two image quantities. These were defined as the measured textures for benign and malignant lung tissue. Results: The NNPS of the MLI ∼25% of that of the DMI at the expense of decreased MTF at intermediate frequencies (0.25≤

Authors:
; ; ;  [1]
  1. Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA (United States)
Publication Date:
OSTI Identifier:
22649294
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:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ANIMAL TISSUES; BIOMEDICAL RADIOGRAPHY; EQUIPMENT; FIDUCIAL MARKERS; IMAGES; LUNGS; NEOPLASMS; PARTICLE TRACKS; PERFORMANCE; TRANSFER FUNCTIONS

Citation Formats

Hu, Y, Rottmann, J, Myronakis, M, and Berbeco, R. SU-G-BRA-06: Quantification of Tracking Performance of a Multi-Layer Electronic Portal Imaging Device. United States: N. p., 2016. Web. doi:10.1118/1.4956930.
Hu, Y, Rottmann, J, Myronakis, M, & Berbeco, R. SU-G-BRA-06: Quantification of Tracking Performance of a Multi-Layer Electronic Portal Imaging Device. United States. doi:10.1118/1.4956930.
Hu, Y, Rottmann, J, Myronakis, M, and Berbeco, R. Wed . "SU-G-BRA-06: Quantification of Tracking Performance of a Multi-Layer Electronic Portal Imaging Device". United States. doi:10.1118/1.4956930.
@article{osti_22649294,
title = {SU-G-BRA-06: Quantification of Tracking Performance of a Multi-Layer Electronic Portal Imaging Device},
author = {Hu, Y and Rottmann, J and Myronakis, M and Berbeco, R},
abstractNote = {Purpose: The purpose of this study was to quantify the improvement in tumor tracking, with and without fiducial markers, afforded by employing a multi-layer (MLI) electronic portal imaging device (EPID) over the current state-of-the-art, single-layer, digital megavolt imager (DMI) architecture. Methods: An ideal observer signal-to-noise ratio (d’) approach was used to quantify the ability of an MLI EPID and a current, state-of-the-art DMI EPID to track lung tumors from the treatment beam’s-eye-view. Using each detector modulation transfer function (MTF) and noise power spectrum (NPS) as inputs, a detection task was employed with object functions describing simple three-dimensional Cartesian shapes (spheres and cylinders). Marker-less tumor tracking algorithms often use texture discrimination to differentiate benign and malignant tissue. The performance of such algorithms is simulated by employing a discrimination task for the ideal observer, which measures the ability of a system to differentiate two image quantities. These were defined as the measured textures for benign and malignant lung tissue. Results: The NNPS of the MLI ∼25% of that of the DMI at the expense of decreased MTF at intermediate frequencies (0.25≤},
doi = {10.1118/1.4956930},
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: To develop an accurate and quick multileaf collimator (MLC) calibration and quality assurance technique using an electronic portal imaging device (EPID) Methods: The MLC models used include the MLCi and Agility (Elekta Ltd). This technique consists of two 22(L)x10(W) cm{sup 2} fields with 0{sup 0} and 180{sup 0} collimator angles centered to an offset EPID. The MLC opening is estimated by calculating the profile at the image’s center in the image’s horizontal direction. Scans in the image’s vertical direction were calculated every 20 pixels in the inner 70% of estimated MLC opening. The profiles’ edges were fitted with linearmore » equations to determine the image’s rotation angle. Then, crossline profiles were scanned at the center of each leaf taking into account the leaf’s width at isocenter and the rotation angle. The profiles’ edges determine the location of the leaves’ edges and these were subtracted from the reference leaf’s position in order to determine the relative leaf offsets. The edge location of all profiles was determined by using the parameterized gradient of the penumbra region. The technique was tested against an established diode array-based method, and for different MLC systems, patterns, gantry angles, days, energies, beam modalities and MLC openings. Results: The differences between the proposed and established methods were 0.26±0.19mm. The leaf offsets’ deviation was <0.3mm (5 months period). For pattern fields, the differences between predetermined and calculated offsets were 0.18±0.18mm. The leaf offset deviation of measurements with different energies and MLC openings were <0.1mm and <0.3mm, respectively. The differences between offsets of FF and FFF beams were 0.01±0.02mm (<0.07mm). The differences between the offsets at different gantry angles were 0.08±0.15mm. Conclusion: The proposed method proved to be accurate and efficient in calculating the relative leaf offsets. Parameterized field edge is essential to obtain accurate result by eliminating the noise from EPID.« less
  • Purpose: To test a method to reconstruct a four-dimensional (4D) dose distribution using the correlation of pre-calculated 4D electronic portal imaging device (EPID) images and measured cine-EPID images. Methods: 1. A phantom designed to simulate a tumor in lung (a polystyrene block with 3.0 cm diameter embedded in cork) was placed on a sinusoidally moving platform with 2 cm amplitude and 4 sec/cycle. Ten-phase 4D CT images were acquired for treatment planning and dose reconstruction. A 6MV photon beam was irradiated on the phantom with static (field size=5×8.5 cm{sup 2}) and dynamic fields (sliding windows, 10×10 cm{sup 2}, X1 MLCmore » closing in parallel with the tumor movement). 2. 4D and 3D doses were calculated forwardly on PTV (1 cm margin). 3. Dose images on EPID under the fields were calculated for 10 phases. 4. Cine EPID images were acquired during irradiation. 5. Their acquisition times were correlated to the phases of the phantom at which irradiation occurred by inter-comparing calculated “reference” EPID images with measured images (2D gamma comparison). For the dynamic beam, the tumor was hidden under MLCs during a portion of irradiation time; the correlation performed when the tumor was visible was extrapolated. 6. Dose for each phase was reconstructed on the 4D CT images and summed over all phases. The summation was compared with forwardly calculated 4D and 3D dose distributions. Monte Carlo methods were used for all calculations. Results: For the open and dynamic beams, the 4D reconstructed doses showed the pass rates of 92.7 % and 100 %, respectively, at the isocenter plane given 3% / 3 mm criteria. The better agreement of the dynamic beam was from its dose gradient which blurred the otherwise sharp difference between forward and reconstructed doses. This also contributed slightly better agreement in DVH of PTV. Conclusion: The feasibility of 4D reconstruction was demonstrated.« less
  • 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: 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 uncertaintymore » 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.« less
  • Purpose: Fast and accurate transit portal dosimetry was investigated by developing a density-scaled layer model of electronic portal imaging device (EPID) and applying it to a clinical environment. Methods: The model was developed for fast Monte Carlo dose calculation. The model was validated through comparison with measurements of dose on EPID using first open beams of varying field sizes under a 20-cm-thick flat phantom. After this basic validation, the model was further tested by applying it to transit dosimetry and dose reconstruction that employed our predetermined dose-response-based algorithm developed earlier. The application employed clinical intensity-modulated beams irradiated on a Randomore » phantom. The clinical beams were obtained through planning on pelvic regions of the Rando phantom simulating prostate and large pelvis intensity modulated radiation therapy. To enhance agreement between calculations and measurements of dose near penumbral regions, convolution conversion of acquired EPID images was alternatively used. In addition, thickness-dependent image-to-dose calibration factors were generated through measurements of image and calculations of dose in EPID through flat phantoms of various thicknesses. The factors were used to convert acquired images in EPID into dose. Results: For open beam measurements, the model showed agreement with measurements in dose difference better than 2% across open fields. For tests with a Rando phantom, the transit dosimetry measurements were compared with forwardly calculated doses in EPID showing gamma pass rates between 90.8% and 98.8% given 4.5 mm distance-to-agreement (DTA) and 3% dose difference (DD) for all individual beams tried in this study. The reconstructed dose in the phantom was compared with forwardly calculated doses showing pass rates between 93.3% and 100% in isocentric perpendicular planes to the beam direction given 3 mm DTA and 3% DD for all beams. On isocentric axial planes, the pass rates varied between 95.8% and 99.9% for all individual beams and they were 98.2% and 99.9% for the composite beams of the small and large pelvis cases, respectively. Three-dimensional gamma pass rates were 99.0% and 96.4% for the small and large pelvis cases, respectively. Conclusions: The layer model of EPID built for Monte Carlo calculations offered fast (less than 1 min) and accurate calculation for transit dosimety and dose reconstruction.« less