A novel method for fast image simulation of flat panel detectors

Shi, Mengze; Myronakis, Marios; Hu, Yue‐Houng; Jacobson, M.; Lehmann, Mathias; Fueglistaller, Rony; Huber, Pascal; Baturin, Paul; Wang, Adam; Ferguson, D.; Harris, Thomas C.; Morf, Daniel; Berbeco, R.

doi:10.1088/1361-6560/ab12aa

Cited by 12 publications

(22 citation statements)

References 31 publications

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“…This work has demonstrated that with cine EPID image frame‐by‐frame analysis, multiple beam delivery error modes can be detected with high sensitivity and low FPR by comparing observed with expected MLC formed apertures. The method is applicable to both pretreatment and during‐treatment real‐time QA, and avoids the need to compute through‐patient transmission fluences in real time at 100 ms per frame 29 for the delivery specific dMU intervals. MLC motion during frame delivery and MLC in‐tolerance position variations can be accounted for in real time via masking of the permissible MLC leaf positions from the frame image.…”

Section: Discussionmentioning

confidence: 99%

An error detection method for real‐time EPID‐based treatment delivery quality assurance

et al. 2020

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To quantify the error detection power of a new treatment delivery error detection method. The method validates monitor unit (MU) resolved beam apertures using real-time EPID images. Methods: The on-board EPID imager was used to measure cine-EPID (~10 Hz) images for 27 beams from 15 VMAT/SBRT clinical treatment plans and five nonclinical plans. For each frame acquisition, planned apertures were interpolated from the treatment plan multileaf collimator (MLC) positions expected during the frame acquisition interval. Inaccurate deliveries were identified by monitoring in-aperture missed fluence and out-of-aperture excess fluence beyond a specified buffer. Delivery errors were simulated by perturbing the planned MLC positions before comparison with nonperturbed measured apertures. Systematic 1-5 mm MLC leaf shifts were used to train a logistic regression model to determine the error detection threshold. Model accuracy was monitored using tenfold cross-validation. The model's error detection ability was tested with other error modes: plan control point (CP) weight perturbations, collimator rotations, random MLC leaf position errors, EPID imager shift, and stuck MLC leaf. The error detection accuracy was evaluated using the Matthews correlation coefficient (MCC) and the false positive rate (FPR). Per-beam error thresholds of >1, >5, and >10% errant frames were tested to label per-beam errors. The model also was tested for its ability to distinguish five cases with highly similar plans and compared with gamma analysis. Results: Delivery errors were detected by monitoring intended per-frame images with a 2 mm MLC buffer. Frame-by-frame aperture errors were identified with an optimal threshold of 0.3% of the expected aperture area. The per-frame FPR was 0.02%. The MCC was 1.00 (perfect classification) for detection based on 1% of frames for random CP weight shift, 3 mm random MLC shifts, 90°and 180°collimator rotations, and an MLC leaf stuck after 10% of the beam delivery. The MCC for 2°, 4°, and 8°collimator rotation were 0.53, 0.76, and 0.96, respectively, for the 1% of beam delivery threshold. The 3 mm EPID shift had poor detection, with a minimum MCC of 0.14. The highly similar plans were reliably detected by the aperture check but were not detectable with gamma analysis. Conclusion: The high error detection sensitivity and low FPR makes the aperture check error detection method well suited to pretreatment and during-treatment beam delivery quality assurance (QA). The aperture check detects subtle beam delivery errors, including those resulting from MLC leaf positioning deviations, CP MU shifts, and stuck MLC leaves. Furthermore, the method can distinguish between highly similar treatment plans. Since the aperture check method monitors for the aperture shapes over a given MU interval, it is also sensitive to errors in MU per CP, without requiring dosimetric calibration of the EPID. The aperture check is one part of a Swiss cheese error detection scheme, which provides redundant error testing of multiple error modes, in...

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Section: Discussionmentioning

confidence: 99%

An error detection method for real‐time EPID‐based treatment delivery quality assurance

et al. 2020

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“…These two factors result in simulation times often as long as 3,000 core‐hours for a 10 7 primary x‐ray simulation of one EPID projection 9 . Further, to produce a clinically equivalent image of 1 MU with a 10

\times

10 cm 2 field size a simulation with more than 10 11 photons is required 10–12 . This problem is compounded in CBCT simulation, where it is necessary to simulate many projections of the object for CBCT image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, Shi et al. introduced the fastEPID framework which pre‐calculates energy deposition efficiency (

η

) and optical spread function (OSF) to remove particle transport in the detector entirely without loss of image quality 10 . Where

η

is defined as the ratio of the total energy deposition in the scintillator and the total x‐ray photon energy incident on the detector.…”

Section: Introductionmentioning

confidence: 99%

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fastCAT: Fast cone beam CT (CBCT) simulation

O’Connell

Bazalova‐Carter

2021

Medical Physics

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Purpose To develop fastCAT, a fast cone‐beam computed tomography (CBCT) simulator. fastCAT uses pre‐calculated Monte Carlo (MC) CBCT phantom‐specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. Methods Pre‐calculated x‐ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x‐ray beam spectra are simulated with EGSnrc for 2.5‐ and 6 MeV electron beams incident on a variety of target materials and kV x‐ray beam spectra are calculated analytically for an x‐ray tube with a tungsten anode. Detectors were modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled—a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. Energy‐dependent scatter kernels were created in Topas for two 16 cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1–5 mm in diameter of six different tissue types: brain, deflated lung, compact and cortical bone, adipose, and B‐100. Results fastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95% confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024 × 1024 × 10 voxels took 61 s on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. Conclusions We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git.

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“…Processing the images of slits, [1][2][3][4][5] bar patterns [6][7][8][9] and edge objects [10][11][12][13][14][15][16][17][18] are commonly used methods for measuring the MTF. The edge method is the one recommended by the International Electrotechnical Commission.…”

Section: Introductionmentioning

confidence: 99%

Performance of a new star‐bar phantom designed for MTF calculations in x‐ray imaging systems

et al. 2020

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Purpose: A new phantom, designed and manufactured for modulation transfer function (MTF) calculations is presented in this work. The phantom has a star-bar pattern and is manufactured in stainless steel. Modulation transfer function determinations are carried out with the new phantom and with an edge phantom to compare their performance and to compare them with previous theoretical predictions. Methods: The phantoms are imaged in an x-ray imaging system using different beam qualities and different entrance air KERMA. Methods, previously developed for synthetic images and simulations, are adapted to real measurements, solving practical implementation issues. Results: In the case of the star-bar, in order to obtain optimal MTF determinations it is necessary to accurately determine the center of the pattern. Also, to avoid underestimates in MTF calculations, the length in pixels of each of the scanning circumferences must be an integer multiple of the number of cycles in the pattern. Both methods, star-bar and edge, give similar mean values of the MTF in all cases analyzed. Also, the dependence with frequency of the experimental MTF standard deviation (SD) agrees with the theoretical expressions presented in previous works. In this regard, the precision is better for the star-bar method than for the edge and differences in precision between both methods are higher for the lowest beam quality. Conclusions: The star-bar phantom can be used for MTF determinations with the advantage of having an improved precision. However, precision is reduced when the radiation quality increases. This fact suggests that, for the highest beam qualities, materials with an attenuation coefficient greater than that of steel should be used to manufacture the phantom.

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A novel method for fast image simulation of flat panel detectors

Cited by 12 publications

References 31 publications

An error detection method for real‐time EPID‐based treatment delivery quality assurance

An error detection method for real‐time EPID‐based treatment delivery quality assurance

fastCAT: Fast cone beam CT (CBCT) simulation

Performance of a new star‐bar phantom designed for MTF calculations in x‐ray imaging systems

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