Effects of motion on the total dose distribution

Bortfeld, Thomas; Jiang, Steve B; Rietzel, Eike

doi:10.1053/j.semradonc.2003.10.011

Cited by 306 publications

(252 citation statements)

References 31 publications

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“…Respiratory motion varies substantially between patients, (1) and the consequent tumor motion during radiotherapy can compromise the accuracy of dose delivery and produce interplay and blurring effects 2 , 3 . Thus, various methods have been devised to compensate for motion, including dynamic multileaf collimator (DMLC) tracking (4) and the use of a robotic arm with six axes (5) .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of tracking accuracy of the CyberKnife system using a webcam and printed calibrated grid

Sumida

Shiomi

Higashinaka³

et al. 2016

J Applied Clin Med Phys

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Tracking accuracy for the CyberKnife's Synchrony system is commonly evaluated using a film‐based verification method. We have evaluated a verification system that uses a webcam and a printed calibrated grid to verify tracking accuracy over three different motion patterns. A box with an attached printed calibrated grid and four fiducial markers was attached to the motion phantom. A target marker was positioned at the grid's center. The box was set up using the other three markers. Target tracking accuracy was evaluated under three conditions: 1) stationary; 2) sinusoidal motion with different amplitudes of 5, 10, 15, and 20 mm for the same cycle of 4 s and different cycles of 2, 4, 6, and 8 s with the same amplitude of 15 mm; and 3) irregular breathing patterns in six human volunteers breathing normally. Infrared markers were placed on the volunteers’ abdomens, and their trajectories were used to simulate the target motion. All tests were performed with one‐dimensional motion in craniocaudal direction. The webcam captured the grid's motion and a laser beam was used to simulate the CyberKnife's beam. Tracking error was defined as the difference between the grid's center and the laser beam. With a stationary target, mean tracking error was measured at 0.4 mm. For sinusoidal motion, tracking error was less than 2 mm for any amplitude and breathing cycle. For the volunteers’ breathing patterns, the mean tracking error range was 0.78‐1.67 mm. Therefore, accurate lesion targeting requires individual quality assurance for each patient.PACS number(s): 87.55.D‐, 87.55.km, 87.55.Qr, 87.56.Fc

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Section: Introductionmentioning

confidence: 99%

Evaluation of tracking accuracy of the CyberKnife system using a webcam and printed calibrated grid

Sumida

Shiomi

Higashinaka³

et al. 2016

J Applied Clin Med Phys

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“…However, the ability of obtaining highly conformal dose distributions must be accompanied by an increased accuracy in beam targeting. [1][2][3] Both kilovoltage (kV) and megavoltage (MV) x-ray imaging methods have been used for ensuring the beam targeting accuracy in the radiotherapy process. The kV imaging methods provide better image contrast due to the dominance of the photoelectric effect with kV x rays.…”

Section: Introductionmentioning

confidence: 99%

Development and clinical evaluation of automatic fiducial detection for tumor tracking in cine megavoltage images during volumetric modulated arc therapy

Azcona

Mok

et al. 2013

Medical Physics

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Purpose: Real-time tracking of implanted fiducials in cine megavoltage (MV) imaging during volumetric modulated arc therapy (VMAT) delivery is complicated due to the inherent low contrast of MV images and potential blockage of dynamic leaves configurations. The purpose of this work is to develop a clinically practical autodetection algorithm for motion management during VMAT. Methods: The expected field-specific segments and the planned fiducial position from the Eclipse (Varian Medical Systems, Palo Alto, CA) treatment planning system were projected onto the MV images. The fiducials were enhanced by applying a Laplacian of Gaussian filter in the spatial domain for each image, with a blob-shaped object as the impulse response. The search of implanted fiducials was then performed on a region of interest centered on the projection of the fiducial when it was within an open field including the case when it was close to the field edge or partially occluded by the leaves. A universal template formula was proposed for template matching and normalized cross correlation was employed for its simplicity and computational efficiency. The search region for every image was adaptively updated through a prediction model that employed the 3D position of the fiducial estimated from the localized positions in previous images. This prediction model allowed the actual fiducial position to be tracked dynamically and was used to initialize the search region. The artifacts caused by electronic interference during the acquisition were effectively removed. A score map was computed by combining both morphological information and image intensity. The pixel location with the highest score was selected as the detected fiducial position. The sets of cine MV images taken during treatment were analyzed with in-house developed software written in MATLAB (The Mathworks, Inc., Natick, MA). Five prostate patients were analyzed to assess the algorithm performance by measuring their positioning accuracy during treatment. Results: The algorithm was able to accurately localize the fiducial position on MV images with success rates of more than 90% per case. The percentage of images in which each fiducial was localized in the studied cases varied between 23% and 65%, with at least one fiducial having been localized between 40% and 95% of the images. This depended mainly on the modulation of the plan and fiducial blockage. The prostate movement in the presented cases varied between 0.8 and 3.5 mm (mean values). The maximum displacement detected among all patients was of 5.7 mm. Conclusions: An algorithm for automatic detection of fiducial markers in cine MV images has been developed and tested with five clinical cases. Despite the challenges posed by complex beam aperture shapes, fiducial localization close to the field edge, partial occlusion of fiducials, fast leaf and gantry movement, and inherently low MV image quality, good localization results were achieved in patient images. This work provides a technique for enabling real-time accurate fiducial d...

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“…Due to the use of beamlet intensities, this model does not account for the interplay effect between the motion of the MLC leaves and the tumour motion (see, for example, Yu et al [1998] and ); the impact of such an effect, however, is small in practice (Bortfeld et al [2004]). For each tumour voxel v ∈ T , let θ v be the prescribed minimum dose and let γθ v be the prescribed maximum dose, where γ ≥ 1.…”

Section: Modelmentioning

confidence: 99%

“…For instance, if the uncertain effect is interfraction target motion or setup uncertainty, then the spatial probability distribution of the target position can be included in the inverse planning process (Unkelbach and Oelfke [2004], Li and Xing [2000]). For intrafraction organ motion, one can similarly define a probability density function for the displacement of each voxel ) or a probability mass function for different motion 6 "states" (Bortfeld et al [2004], Trofimov et al [2005]), which can then be used to design the treatment. It is also possible to plan for an uncertain effect even if the underlying probability distribution is not known precisely Oelfke [2004, 2005]).…”

Section: Uncertainty In Radiation Therapy Treatmentsmentioning

confidence: 99%

Adaptive and robust radiation therapy optimization for lung cancer

Chan

Mišić

2013

European Journal of Operational Research

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A previous approach to robust intensity-modulated radiation therapy (IMRT) treatment planning for moving tumours in the lung involves solving a single planning problem before treatment and using the resulting solution in all of the subsequent treatment sessions. In this thesis, we develop two adaptive robust IMRT optimization approaches for lung cancer, which involve using information gathered in prior treatment sessions to guide the reoptimization of the treatment for the next session. The first method is based on updating an estimate of the uncertain effect, while the second is based on additionally updating the dose requirements to account for prior errors in dose. We present computational results using real patient data for both methods and an asymptotic analysis for the first method. Through these results, we show that both methods lead to improvements in the final dose distribution over the traditional robust approach, but differ greatly in their daily dose performance.ii AcknowledgementsFirst of all, I would like to thank my advisor and friend, Timothy Chan. I cannot begin to express my gratitude for his thoughtfulness, patience, encouragement and support over the last two years. One of the things that I have learned about being a researcher is that you get a front-row seat to the full spectrum of human emotion: the dizzying highs that come with an exciting new theoretical result or computational result, but also the dark lows when you just cannot push that proof any further or your computational experiments do not work out as you had hoped they would. Whenever

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Effects of motion on the total dose distribution

Cited by 306 publications

References 31 publications

Evaluation of tracking accuracy of the CyberKnife system using a webcam and printed calibrated grid

Evaluation of tracking accuracy of the CyberKnife system using a webcam and printed calibrated grid

Development and clinical evaluation of automatic fiducial detection for tumor tracking in cine megavoltage images during volumetric modulated arc therapy

Adaptive and robust radiation therapy optimization for lung cancer

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