| Autor: |
Carlos A. Barajas, Jerimy C. Polf, Matthias K. Gobbert |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Frontiers in Physics, Vol 11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2296-424X |
| DOI: |
10.3389/fphy.2023.903929 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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