We present Compton camera (CC) based PG imaging for proton range verification at clinical dose rates. PG emission from a tissue-equivalent phantom during irradiation with clinical proton beams was measured with a prototype CC. Images were reconstructed of the raw measured data and of data processed with a neural network (NN) trained to identify “true” and “false” PG events. From these images, we determine if PG images produced by the prototype CC could provide clinically useful information about the in vivo range of the proton beams delivered during proton beam radiotherapy. NN processing of the data was found necessary to allow identification of the proton beam path from the PG images. Furthermore, to allow the localization of the end of the proton beam range with a precision of ≤ 3mm with the prototype CC, ~1 x 109 protons would need to be delivered, which is on the order of magnitude delivered for a standard proton radiotherapy treatment field. To obtain higher precision in beam range determination and to allow imaging a single proton pencil beam delivered within the full treatment field, further improvements in PG detection rates by the CC, NN data processing, and image reconstruction algorithms are needed.
Cardiac arrhythmias affect millions of adults in the U.S. each year. This irregularity in the beating of the heart is often caused by dysregulation of calcium in cardiomyocytes, the cardiac muscle cell. Cardiomyocytes function through the interplay between electrical excitation, calcium signaling, and mechanical contraction, an overall process known as calcium induced calcium release (CICR). A system of seven coupled non-linear time-dependent partial differential equations (PDEs), which model physiological variables in a cardiac cell, link the processes of cardiomyocytes. Through parameter studies for each component system at a time, we create a set of values for critical parameters that connect the calcium store in the sarcoplasmic reticulum, the effect of electrical excitation, and mechanical contraction in a physiologically reasonable manner. This paper shows the design process of this set of parameters and then shows the possibility to study the influence of a particular problem parameter using the overall model.
Proton beam radiotherapy is a method of cancer treatment that uses proton beams to irradiate cancerous tissue, while simultaneously sparing healthy tissue. One promising method of real-time imaging during treatment is the use of a Compton camera, which can image prompt gamma rays that are emitted along the beam's path through the patient. However, because of limitations in the Compton camera's ability to detect prompt gammas, the reconstructed images are often noisy and unusable for verifying proton treatment delivery. Machine learning ensemble methods like random forests are able to automatically learn patterns that exist in numerical data, making them a promising method to analyze Compton camera data for the purpose of reducing noise in the reconstructed images. We conduct a hyperparameter search to find an optimal random forest model. We then present the results of the best performing random forest model, which demonstrate that this ensemble method is less effective than competing machine learning techniques for this application.
Proton beam radiotherapy is a cancer treatment method that uses proton beams to irradiate cancerous tissue while simultaneously sparing doses to healthy tissue. In order to optimize radiational doses to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through real-time imaging. One promising method of real-time imaging is through a Compton camera, which can image prompt gamma rays emitted along the beam's path through the patient. However, the images reconstructed with modern reconstruction algorithms are often noisy and unusable for verifying proton treatment delivery due to limitations with the camera. This paper demonstrates the ability of deep learning for removing false prompt gamma couplings and correcting the improperly ordered gamma interactions within the data for the case of Triples and Doubles-to-Triple events.
State-of-the-art distributed-memory computer clusters contain multi-core CPUs with 16 and more cores. The second-generation of the Intel Xeon Phi many-core processor has more than 60 cores with 16 GB of high-performance on-chip memory. We contrast the performance of the second-generation Intel Xeon Phi, code-named Knights Landing (KNL), with 68 computational cores to the latest multi-core CPU Intel Skylake with 18 cores. A special-purpose code solving a system of nonlinear reaction-diffusion partial differential equations with several thousands of point sources modeled mathematically by Dirac delta distributions serves as realistic test bed. The system is discretized in space by the finite volume method and advanced by fully implicit time-stepping, with a matrix-free implementation that allows the complex model to have an extremely small memory footprint. The sample application is a seven variable model of calcium induced calcium release (CICR) that models the interplay between electrical excitation, calcium signaling, and mechanical contraction in a heart cell. The results demonstrate that excellent parallel scalability is possible on both hardware platforms, but that modern multi-core CPUs outperform the specialized many-core Intel Xeon Phi KNL architecture for a large class of problems such as systems of parabolic partial differential equations.
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