An official website of the United States government
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.
more »
« less
Use of machine learning in CARNA proton imager
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
- Award ID(s):
- 1659581
- PAR ID:
- 10092351
- Date Published:
- Journal Name:
- Medical Imaging 2019: Physics of Medical Imaging
- Page Range / eLocation ID:
- 207
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Proton radiography is a promising development in proton therapy, and researchers are currently exploring optimal detector materials to construct proton radiography detector arrays. High-density glass scintillators may improve integrating-mode proton radiography detectors by increasing spatial resolution and decreasing detector thickness. We evaluated several new scintillators, activated with europium or terbium, with proton response measurements and Monte Carlo simulations, characterizing relative luminosity, ionization quenching, and proton radiograph spatial resolution. We applied a correction based on Birks’s analytical model for ionization quenching. The data demonstrate increased relative luminosity with increased activation element concentration, and higher relative luminosity for samples activated with europium. An increased glass density enables more compact detector geometries and higher spatial resolution. These findings suggest that a tungsten and gadolinium oxide-based glass activated with 4% europium is an ideal scintillator for testing in a full-size proton radiography detector.more » « less
-
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
-
Abstract The performance of the electromagnetic calorimeter of the ALICE experiment during operation in 2010–2018 at the Large Hadron Collider is presented. After a short introduction into the design, readout, and trigger capabilities of the detector, the procedures for data taking, reconstruction, and validation are explained. The methods used for the calibration and various derived corrections are presented in detail. Subsequently, the capabilities of the calorimeter to reconstruct and measure photons, light mesons, electrons and jets are discussed. The performance of the calorimeter is illustrated mainly with data obtained with test beams at the Proton Synchrotron and Super Proton Synchrotron or in proton-proton collisions at √s= 13 TeV, and compared to simulations.more » « less
-
Abstract A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2512565
