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The challenge of radiation therapy is to maximize the dose to the tumor while simultaneously minimizing the dose elsewhere. Proton therapy is well suited to this challenge due to the way protons slow down in matter. As the proton slows down, the rate of energy loss per unit path length

The challenge of radiation therapy is to maximize the dose to the tumor while simultaneously minimizing the dose elsewhere. Proton therapy is well suited to this challenge due to the way protons slow down in matter. As the proton slows down, the rate of energy loss per unit path length continuously increases leading to a sharp dose near the end of range. Unlike conventional radiation therapy, protons stop inside the patient, sparing tissue beyond the tumor. Proton therapy should be superior to existing modalities, however, because protons stop inside the patient, there is uncertainty in the range. “Range uncertainty” causes doctors to take a conservative approach in treatment planning, counteracting the advantages offered by proton therapy. Range uncertainty prevents proton therapy from reaching its full potential.

A new method of delivering protons, pencil-beam scanning (PBS), has become the new standard for treatment over the past few years. PBS utilizes magnets to raster scan a thin proton beam across the tumor at discrete locations and using many discrete pulses of typically 10 ms duration each. The depth is controlled by changing the beam energy. The discretization in time of the proton delivery allows for new methods of dose verification, however few devices have been developed which can meet the bandwidth demands of PBS.

In this work, two devices have been developed to perform dose verification and monitoring with an emphasis placed on fast response times. Measurements were performed at the Mayo Clinic. One detector addresses range uncertainty by measuring prompt gamma-rays emitted during treatment. The range detector presented in this work is able to measure the proton range in-vivo to within 1.1 mm at depths up to 11 cm in less than 500 ms and up to 7.5 cm in less than 200 ms. A beam fluence detector presented in this work is able to measure the position and shape of each beam spot. It is hoped that this work may lead to a further maturation of detection techniques in proton therapy, helping the treatment to reach its full potential to improve the outcomes in patients.
ContributorsHolmes, Jason M (Author) / Alarcon, Ricardo (Thesis advisor) / Bues, Martin (Committee member) / Galyaev, Eugene (Committee member) / Chamberlin, Ralph (Committee member) / Arizona State University (Publisher)
Created2019
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Description
The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor

The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor edge detection method was therefore developed to realize an edge detector directly from spectral data. This thesis explores the possibilities of detecting edges from the phase of the spectral data, that is, without the magnitude of the sampled spectral data. Prior work has demonstrated that the spectral phase contains particularly important information about underlying features in a signal. Furthermore, the concentration factor method yields some insight into the detection of edges in spectral phase data. An iterative design approach was taken to realize an edge detector using only the spectral phase data, also allowing for the design of an edge detector when phase data are intermittent or corrupted. Problem formulations showing the power of the design approach are given throughout. A post-processing scheme relying on the difference of multiple edge approximations yields a strong edge detector which is shown to be resilient under noisy, intermittent phase data. Lastly, a thresholding technique is applied to give an explicit enhanced edge detector ready to be used. Examples throughout are demonstrate both on signals and images.
ContributorsReynolds, Alexander Bryce (Author) / Gelb, Anne (Thesis director) / Cochran, Douglas (Committee member) / Viswanathan, Adityavikram (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05