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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.
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Deep residual fully connected neural network classification of Compton camera based prompt gamma imaging for proton radiotherapy
Proton beam radiotherapy is a method of cancer treatment that uses proton beams to irradiate cancerous tissue, while minimizing doses to healthy tissue. In order to guarantee that the prescribed radiation dose is delivered to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through the use of real-time imaging using methods which can image prompt gamma rays that are emitted along the beam’s path through the patient such as Compton cameras (CC). However, because of limitations of the CC, their images are noisy and unusable for verifying proton treatment delivery. We provide a detailed description of a deep residual fully connected neural network that is capable of classifying and improving measured CC data with an increase in the fraction of usable data by up to 72% and allows for improved image reconstruction across the full range of clinical treatment delivery conditions.
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- Award ID(s):
- 2050943
- PAR ID:
- 10402446
- Date Published:
- Journal Name:
- Frontiers in Physics
- Volume:
- 11
- ISSN:
- 2296-424X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fphy.2023.903929
