التفاصيل البيبلوغرافية
| العنوان: |
DataSheet1_Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy.PDF |
| المؤلفون: |
Jerimy C. Polf, Carlos A. Barajas, Stephen W. Peterson, Dennis S. Mackin, Sam Beddar, Lei Ren, Matthias K. Gobbert |
| سنة النشر: |
2022 |
| المجموعة: |
Frontiers: Figshare |
| مصطلحات موضوعية: |
Biophysics, Astrophysics, Applied Physics, Computational Physics, Condensed Matter Physics, Particle Physics, Plasma Physics, Solar System, Solar Physics, Planets and Exoplanets, Classical and Physical Optics, Photonics, Optoelectronics and Optical Communications, Cloud Physics, Tropospheric and Stratospheric Physics, High Energy Astrophysics, Cosmic Rays, Mesospheric, Ionospheric and Magnetospheric Physics, Space and Solar Physics, Mathematical Physics not elsewhere classified, Physical Chemistry of Materials, Physical Chemistry not elsewhere classified, Classical Physics not elsewhere classified, Condensed Matter Physics not elsewhere classified, Quantum Physics not elsewhere classified, prompt gamma, compton camera, proton radiotherapy, range verification |
| الوصف: |
We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded “bad” PG events arising from background noise during the measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of “good” data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify “good” and “bad” PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (∼1 × 10 9 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ∼5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward ... |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| العلاقة: |
https://figshare.com/articles/dataset/DataSheet1_Applications_of_Machine_Learning_to_Improve_the_Clinical_Viability_of_Compton_Camera_Based_in_vivo_Range_Verification_in_Proton_Radiotherapy_PDF/19569232Test |
| DOI: |
10.3389/fphy.2022.838273.s001 |
| الإتاحة: |
https://doi.org/10.3389/fphy.2022.838273.s001Test
https://figshare.com/articles/dataset/DataSheet1_Applications_of_Machine_Learning_to_Improve_the_Clinical_Viability_of_Compton_Camera_Based_in_vivo_Range_Verification_in_Proton_Radiotherapy_PDF/19569232Test |
| حقوق: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.700955F |
| قاعدة البيانات: |
BASE |
