Brachytherapy Dosimetry: Working towards in-vivo and end-to-end diametric checks in modern HDR brachytherapy
Treatment errors can occur in high dose rate (HDR) brachytherapy, but, currently, there is a lack of real-time treatment verification systems that are clinically available and thus many errors are only determined retrospectively, or not at all. Due to the rapid dose fall-off in HDR brachytherapy, small uncertainties can result in large dose variations. These errors can have a large impact on the patient if not detected during the treatment. In-vivo dosimetry is one potential way to detect these errors in real-time. The purpose of this work is to explore further the use of a nano-fiber optic detector (NanoFOD) for real-time dosimetry during HDR brachytherapy treatments. The NanoFOD consists of an inorganic nano-crystalline scintillator fixed on an optical fiber. It is small enough to be placed into clinical catheters and needles, allowing for in-vivo measurements, and is able to measure point doses to sub-millimeter resolution. Previous studies proved the feasibility of using the NanoFOD for real-time measurements in both cylinder and T&O HDR treatments. The purpose of this work is to: (1) determine a way to calibrate the NanoFOD at small source to detector distances, with the end goal to make the NanoFOD usable in more types of HDR deliveries; (2) test the feasibility of using the NanoFOD to measure real-time, in-vivo dose measurements during an US-based HDR prostate phantom end-to-end-test; (3) design and test a novel platform to allow for real time tracking of HDR treatments.
In this thesis a new calibration technique was developed and tested for the calibration of the NanoFOD at short distances from an Ir-192 HDR source. This calibration provides a way to convert the real-time measured voltage to a dose-rate-to-water value for a range of source-to-detector distances, beginning at 0.5 cm, allowing for more accurate dose measurements in HDR brachytherapy applications in which there are typically small volumes, such as prostate HDR, or any other interstitial type implant. Verification of this calibration found accuracy within 7% of the expected cumulative dose values, with a potential uncertainty being the accuracy with which we can position the NanoFOD versus the known location of the source. A second potential uncertainty is the assumption that the entire NanoFOD is water equivalent; when in reality some components are not. Preliminary Monte Carlo simulations were able to determine a relative dose rate value when the NanoFOD material was present, with results indicating the NanoFOD impacts the calculated dose.
This NanoFOD calibration was then used to test the ability of the fiber to take real-time measurements during an HDR prostate phantom end-to-end. Dose differences between the planned and measured cumulative doses when using flexi needles were found to be approximately 18%. This differences between the measured and planned dose values include both uncertainties in the NanoFOD system, as well as US-based HDR prostate brachytherapy workflow uncertainties, and are on the same order of magnitude with other reported in-vivo systems (Mason, Mamo, Al-Qaisieh, Henry, & Bownes, 2016). Through a Type A and Type B uncertainty analysis, the overall uncertainty of the dose measurements achieved by the NanoFOD was determined to be 15.7% and the uncertainty in the measured TPS dose values for HDR prostate treatments was determined to be 15.4%.
For real time tracking of delivered dose during HDR treatments, a novel platform using the NanoFOD detector was designed. The designed prototype interface was successful in displaying in real-time the measured voltage, correctly accounting for background noise, and also automatically detecting the location of the first dwell to start the overlay with the expected signal. When large errors were introduced into a phantom delivery treatment, the interface detected large differences between the measured and expected signals. This platform provides real-time feedback to the user, allowing for in-vivo verification of the dose delivered to the patient, as compared to expected values imported from the TPS. In doing this, the interface has the ability to help identify and reduce potential treatment errors in real-time that could otherwise remain undetected.
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