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The direct detection of 5-MeV protons by flexible organic detectors based on thin films is here demonstrated. The organic devices act as a solid-state detector, in which the energy released by the protons within the active layer of the sensor is converted into an electrical current. These sensors can quantitatively and reliably measure the dose of protons impinging on the sensor both in real time and in integration mode. This study shows how to detect and exploit the energy absorbed both by the organic semiconducting layer and by the plastic substrate, allowing to extrapolate information on the present and past irradiation of the detector. The measured sensitivity, S = (5.15 ± 0.13) pC Gy −1 , and limit of detection, LOD = (30 ± 6) cGy s −1 , of the here proposed detectors assess their efficacy and their potential as proton dosimeters in several fields of application, such as in medical proton therapy.
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CARNA with Artificial Intelligence - Use of Machine Learning for a Proton Imaging
Protons deposit the majority of their energy at the end of their lifetimes, characterized by a Bragg peak. This makes proton therapy a viable way to target cancerous tissue while minimizing damage to surrounding healthy tissue. However, in order to utilize this high precision treatment, greater accuracy in tumor imaging is needed. An approximate uncertainty of ±3% exists in the current practice of proton therapy due to conversions between x-ray and proton stopping power. An imaging system utilizing protons has the potential to eliminate that inaccuracy. This study focuses on developing a proof of concept proton-imaging detector built with a high-density glass scintillator.
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- Award ID(s):
- 1407404
- PAR ID:
- 10144794
- Date Published:
- Journal Name:
- SPIE Medical Imaging Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Proton beam therapy is a unique form of radiotherapy that utilizes protons to treat cancer by irradiating cancerous tumors, while avoiding unnecessary radiation exposure to surrounding healthy tissues. Real-time imaging of the proton beam can make this form of therapy more precise and safer for the patient during delivery. The use of Compton cameras is one proposed method for the real-time imaging of prompt gamma rays that are emitted by the proton beams as they travel through a patient’s body. Unfortunately, some of the Compton camera data is flawed and the reconstruction algorithm yields noisy and insufficiently detailed images to evaluate the proton delivery for the patient. Previous work used a deep residual fully connected neural network. The use of recurrent neural networks (RNNs) has been proposed, since they use recurrence relationships to make potentially better predictions. In this work, RNN architectures using two different recurrent layers are tested, the LSTM and the GRU. Although the deep residual fully connected neural network achieves over 75% testing accuracy and our models achieve only over 73% testing accuracy, the simplicity of our RNN models containing only 6 hidden layers as opposed to 512 is a significant advantage. Importantly in a clinical setting, the time to load the model from disk is significantly faster, potentially enabling the use of Compton camera image reconstruction in real-time during patient treatment.more » « less
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Proton therapy has potential for high precision dose delivery, provided that high accuracy is achieved in imaging. Currently, X-ray based techniques are preferred for imaging prior to proton therapy, and the stopping power conversion tables cause irreducible uncertainty. The proposed proton imaging methods aim to reduce this source of error, as well as lessen the radiation exposure of the patient. CARNA is a homogeneous compact calorimeter that utilizes a novel high density scintillating glass as an active medium. The compact design and unique geometry of the calorimeter eliminate the need for a tracker system and allow it to be directly attached to a gantry. Thus, giving CARNA potential to be used for insitu imaging during the hadron therapy, possibly to detect the prompt gammas. The novel glass development and the traditional image reconstruction studies performed with CARNA have been reported before. However, to improve the image reconstruction, a machine learning implementation with CARNA is reported. A proof-of-concept Artificial Neural Network, is shown to efficiently predict the density and the shape of the tumors.more » « less
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Abstract PurposeWe consider the following scenario: A radiotherapy clinic has a limited number of proton therapy slots available each day to treat cancer patients of a given tumor site. The clinic's goal is to minimize the expected number of complications in the cohort of all patients of that tumor site treated at the clinic, and thereby maximize the benefit of its limited proton resources. MethodsTo address this problem, we extend the normal tissue complication probability (NTCP) model–based approach to proton therapy patient selection to the situation of limited resources at a given institution. We assume that, on each day, a newly diagnosed patient is scheduled for treatment at the clinic with some probability and with some benefit from protons over photons, which is drawn from a probability distribution. When a new patient is scheduled for treatment, a decision for protons or photons must be made, and a patient may wait only for a limited amount of time for a proton slot becoming available. The goal is to determine the thresholds for selecting a patient for proton therapy, which optimally balance the competing goals of making use of all available slots while not blocking slots with patients with low benefit. This problem can be formulated as a Markov decision process (MDP) and the optimal thresholds can be determined via a value‐policy iteration method. ResultsThe optimal thresholds depend on the number of available proton slots, the average number of patients under treatment, and the distribution of values. In addition, the optimal thresholds depend on the current utilization of the facility. For example, if one proton slot is available and a second frees up shortly, the optimal threshold is lower compared to a situation where all but one slot remain blocked for longer. ConclusionsMDP methodology can be used to augment current NTCP model–based patient selection methods to the situation that, on any given day, the number of proton slots is limited. The optimal threshold then depends on the current utilization of the proton facility. Although, the optimal policy yields only a small nominal benefit over a constant threshold, it is more robust against variations in patient load.more » « less
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null (Ed.)Abstract Proton conduction is an important property for fuel cell electrolytes. The search for molecular details on proton transport is an ongoing quest. Here, we show that in hydrated yttrium doped barium zirconate using X-ray and neutron diffraction that protons tend to localize near the dopant yttrium as a conjugated superstructure. The proton jump time measured using quasi-elastic neutron scattering follows the Holstein-Samgin polaron model, revealing that proton hopping is weakly coupled to the high-frequency O-H stretching motion, but strongly coupled to low-frequency lattice phonons. The ratio of the proton polaron effective mass, m * , and the proton mass is m * / m = 2, when coupled to the Zr-O stretching mode, giving experimental evidence of proton pairing in perovskites, as a result of proton-phonon coupling. Possible pathways of a proton pair are provided through Nudge Elastic Band calculations. The pairing of protons, when jumping, is discussed in context of a cooperative protonic charge transport process.more » « less
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