Using a Monolithic Active Pixel Sensor for Monitoring Multileaf Collimator Positions in Intensity Modulated Radiotherapy

Page, Ryan; Abbott, Natalie L.; Davies, J.; Dyke, Emma; Randles, Heather J.; Velthuis, J. J.; Fletcher, Sally; Gregory, Stephen; Hall, Christopher D.; John, Anne-Marie; Lawrence, Henry; Stevens, P.; Hugtenburg, Richard P.; Tunbridge, Victoria

doi:10.1109/tns.2013.2287609

Cited by 10 publications

(12 citation statements)

References 9 publications

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“…From this a point along the MLC edge can be extracted by fitting a gaussian and evaluating the mean. The final position is found by fitting a linear function to the set of points that make up the MLC edge, a more detailed description can be found here [14]. To check the precision of this method the distribution of 100 single frames were analysed.…”

Section: Reconstruction Methods and Resultsmentioning

confidence: 99%

Upstream Dosimetry using a Monolithic Active Pixel Sensor (MAPS)

Page¹

2015

Proceedings of Technology and Instrumentation in Particle Physics 2014 — PoS(TIPP2014)

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Section: Reconstruction Methods and Resultsmentioning

confidence: 99%

Upstream Dosimetry using a Monolithic Active Pixel Sensor (MAPS)

Page¹

2015

Proceedings of Technology and Instrumentation in Particle Physics 2014 — PoS(TIPP2014)

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“…Use of log files is technically not verification, as it does not take into account the possibility that the MLC monitoring system is incorrectly calibrated or misaligned. Monolithic Active Pixel Sensor (MAPS) devices have been proven effective for upstream verification of radiotherapy treatments [7]- [9], as they can be made very thin and MAPS based systems can be produced with an attenuation below 1% [10].…”

Section: Radiotherapy Verificationmentioning

confidence: 99%

“…This work was done using 0.1 s of data taken at 400 Monitor Units (M.U.) per minute, for leaves with a width of 1 cm at isocentre [10]. The algorithm used to calculate the leaf profile was based on a Sobel filter.…”

Section: A Monolithic Active Pixel Sensorsmentioning

confidence: 99%

r-UNet: Leaf Position Reconstruction in Upstream Radiotherapy Verification

Sio

Velthuis

Beck

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Monolithic Active Pixel Sensor (MAPS) devices are an effective tool for upstream verification of Intensity Modulated Radiotherapy (IMRT) treatments. It is crucial to measure with high precision the positions of the multi leaf collimators (MLC) used to shape the beam in real time, in order to enhance the quality and safety of treatments. This work describes r-UNet, a deep learning based solution for leaf position reconstruction. The model is used to analyse the high-resolution images produced by a Lassena MAPS device in order to automatically determine the leaf positions. Image segmentation and leaf position estimation are performed simultaneously in a multi-task setting. r-UNet obtained an average Dice coefficient of 0.96 ± 0.03 for the reconstructed image masks in the held-out test set; whilst the mean squared error (MSE) resulting from the estimation of the MLC positions is 0.003 mm, with a resolution ranging between 45 and 53 µm for leaf extensions between 1 and 35 mm. On unseen leaf positions, r-UNet yielded a single-leaf resolution between 54 and 88 µm depending on the leaf extension, and an average MSE of 0.07 mm. These results were obtained using single frames of data collected at 34 frames per second.

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“…The electric motor used in this system includes stepping motor, DC servomotor, and brushless DC motor. To ensure the accuracy of the leaves in place, the following methods are used: (1) accurately control the speed, acceleration, number of revolutions or steps of the drive motor to achieve control accuracy of the leaf in position (this requires the use of encoders to detect the operating status of the motor); and (2) direct detection of the position of the leaves [16] and identification the next running requirements of the motor (commonly used methods in two-dimensional ionization chamber matrix to verify the precision of the leaves position of MLC). The BLDC motor can be controlled through the drive file of MLC that is generated by the radiation treatment planning system and contains the location information for each leaf.…”

Section: Working Principle Multi-leaf Collimatormentioning

confidence: 99%

Iterative Learning Synchronized Control of Multi-leaf Collimator Based on Cross-coupled Control

Zhang¹,

Lu²,

Zhang³

et al. 2019

EJEE

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The purpose of this research was to ensure that the leaves of multi-leaf collimator (MLC) move along the trajectory set in the radiotherapy plan. Aiming at the characteristics of multileaf control of multi-leaf collimator, the model of leaf control system is established. Taking the synchronized error between leaves as the input of the system, combined with PD CCC and PD ILC, an iterative learning control strategy based on cross-coupled is proposed, and verified by experiments, then compared with the multi-leaf control strategy that adopts iterative learning control algorithm independently. The experimental results show that the control strategy proposed in this study can reduce the synchronized error between leaves, and the initial value of the maximum absolute value of the error proposed reduces the synchronous position error of the leaves from the beginning, which meets the requirements of radiation therapy. The results provide a guarantee for the synchronous control of MLC to improve the efficiency of conformal radiotherapy.

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Using a Monolithic Active Pixel Sensor for Monitoring Multileaf Collimator Positions in Intensity Modulated Radiotherapy

Cited by 10 publications

References 9 publications

Upstream Dosimetry using a Monolithic Active Pixel Sensor (MAPS)

Upstream Dosimetry using a Monolithic Active Pixel Sensor (MAPS)

r-UNet: Leaf Position Reconstruction in Upstream Radiotherapy Verification

Iterative Learning Synchronized Control of Multi-leaf Collimator Based on Cross-coupled Control

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