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A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.

Publication ,  Journal Article
Jiang, Z; Polf, JC; Barajas, CA; Gobbert, MK; Ren, L
Published in: Phys Med Biol
March 20, 2023

Background and objective. Range uncertainty is a major concern affecting the delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3Din vivorange verification. However, the conventional back-projected PG images suffer from severe distortions due to the limited view of the CC, significantly limiting its clinical utility. Deep learning has demonstrated effectiveness in enhancing medical images from limited-view measurements. But different from other medical images with abundant anatomical structures, the PGs emitted along the path of a proton pencil beam take up an extremely low portion of the 3D image space, presenting both the attention and the imbalance challenge for deep learning. To solve these issues, we proposed a two-tier deep learning-based method with a novel weighted axis-projection loss to generate precise 3D PG images to achieve accurate proton range verification.Materials and methods: the proposed method consists of two models: first, a localization model is trained to define a region-of-interest (ROI) in the distorted back-projected PG image that contains the proton pencil beam; second, an enhancement model is trained to restore the true PG emissions with additional attention on the ROI. In this study, we simulated 54 proton pencil beams (energy range: 75-125 MeV, dose level: 1 × 109protons/beam and 3 × 108protons/beam) delivered at clinical dose rates (20 kMU min-1and 180 kMU min-1) in a tissue-equivalent phantom using Monte-Carlo (MC). PG detection with a CC was simulated using the MC-Plus-Detector-Effects model. Images were reconstructed using the kernel-weighted-back-projection algorithm, and were then enhanced by the proposed method.Results. The method effectively restored the 3D shape of the PG images with the proton pencil beam range clearly visible in all testing cases. Range errors were within 2 pixels (4 mm) in all directions in most cases at a higher dose level. The proposed method is fully automatic, and the enhancement takes only ∼0.26 s.Significance. Overall, this preliminary study demonstrated the feasibility of the proposed method to generate accurate 3D PG images using a deep learning framework, providing a powerful tool for high-precisionin vivorange verification of proton therapy.

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Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

March 20, 2023

Volume

68

Issue

7

Location

England

Related Subject Headings

  • Protons
  • Proton Therapy
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Monte Carlo Method
  • Imaging, Three-Dimensional
  • Image Processing, Computer-Assisted
  • Gamma Rays
  • Feasibility Studies
  • Deep Learning
 

Citation

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Jiang, Z., Polf, J. C., Barajas, C. A., Gobbert, M. K., & Ren, L. (2023). A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning. Phys Med Biol, 68(7). https://doi.org/10.1088/1361-6560/acbf9a
Jiang, Zhuoran, Jerimy C. Polf, Carlos A. Barajas, Matthias K. Gobbert, and Lei Ren. “A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.Phys Med Biol 68, no. 7 (March 20, 2023). https://doi.org/10.1088/1361-6560/acbf9a.
Jiang Z, Polf JC, Barajas CA, Gobbert MK, Ren L. A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning. Phys Med Biol. 2023 Mar 20;68(7).
Jiang, Zhuoran, et al. “A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.Phys Med Biol, vol. 68, no. 7, Mar. 2023. Pubmed, doi:10.1088/1361-6560/acbf9a.
Jiang Z, Polf JC, Barajas CA, Gobbert MK, Ren L. A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning. Phys Med Biol. 2023 Mar 20;68(7).
Journal cover image

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

March 20, 2023

Volume

68

Issue

7

Location

England

Related Subject Headings

  • Protons
  • Proton Therapy
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Monte Carlo Method
  • Imaging, Three-Dimensional
  • Image Processing, Computer-Assisted
  • Gamma Rays
  • Feasibility Studies
  • Deep Learning