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Visualizing and Quantifying Radiation Therapy in Real-Time Using a Novel Beam Imaging Technique

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C Jenkins

C Jenkins*, D Naczynski , L Xing , Stanford University, Stanford, CA


TH-C-17A-7 Thursday 10:15AM - 12:15PM Room: 17A

Purpose: Radiation therapy uses invisible high energy X-rays to treat an invisible tumor. Proper positioning of the treatment beam relative to the patient’s anatomy during dose delivery is critically important to the success of treatment. We develop and characterize a novel radiation therapy beam visualization technique for real-time monitoring of patient treatment.

Methods: Custom made flexible scintillator sheets were fabricated from gadolinium oxysulfide (GOS) particles that had been doped with terbium within a silicone elastomer matrix. Sheets of several thicknesses ranging from 0.3 to 1mm were prepared and tested. Sheets were exposed to megavoltage X-ray and electron beams from a Varian linac and the resulting optical signal was collected by multiple CMOS cameras placed in the treatment room. Real-time images were collected for different beam energies and dose rates. Signal intensity and SNR were calculated by processing the acquired images.

Results: All signals were detectable in the presence of full room lighting and at an integration time of 45ms. Average signal intensity and SNR increased with both sheet thickness and dose rate and decreased with beam energy and incident light. For a given sheet thickness and beam energy the correlation between dose rate and signal intensity was highly linear. Increased sheet thickness or dose rate results in a linear increase in the detected signal. All results are consistent with analytical approximations.

Conclusion: The technique offers a means of accurately visualizing a radiation therapy beam shape and fluence in real time. The effects of salient parameters have been characterized and will enable further optimization of the technique as it is implemented into the clinical workflow.

Funding Support, Disclosures, and Conflict of Interest: The project described was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health through UL1 TR001085.

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