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Deep Learning Augmented Proton Portal Imaging: A Phantom Study

S Charyyev1*, Y Lei2, J Harms3, B Eaton4, M McDonald5, W Curran6, T Liu7, J Zhou8, R Zhang9, X Yang10, (1) Emory University, Atlanta, GA, (2) Emory University, Atlanta, GA, (3) Emory University, Atlanta, GA, (4) Emory University, Atlanta, ,(5) Emory University, Atlanta, GA, (6) Emory University, Atlanta, GA, (7) Emory University, Atlanta, GA, (8) Emory University, Atlanta, GA, (9) Dartmouth College, Lebanon, NH, (10) Emory University, Atlanta, GA

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam’s-eye-view (BEV) at native gantry angles. However, PPI has poor inherent contrast and spatial resolution. To deal with this issue, we propose a deep-learning-based method to use kV digitally reconstructed radiographs (DRR) to improve PPI image quality.

Methods: We used a residual generative adversarial network framework to learn the nonlinear mapping between PPIs and DRRs. Residual blocks were used to force the model to focus on the structural differences between DRR and PPI. To assess the accuracy of our method, we used 149 images to train the method and 30 images to test the method. PPIs were acquired using a double-scattering system. The DRRs acquired from CT acted as learning targets in the training process and were used to evaluate results from the proposed method using a six-fold cross-validation scheme.


Results: Qualitatively, the corrected PPIs showed enhanced spatial resolution and captured fine details present in the DRRs that are missed in the PPIs. The quantitative results for corrected PPIs show average normalized mean error, normalized mean absolute error, peak signal-to-noise ratio and structural similarity index of 0.1%, 0.3%, 39.14 dB, and 0.987, respectively.


Conclusion: The results indicate the proposed method can generate high quality corrected PPIs and this work shows the potential to use a deep-learning model to make PPI available in proton radiotherapy. This will allow for BEV imaging with the particle used for treatment, leading to enhanced patient position verification.

Download ePoster [PDF]

Keywords

Portal Imaging, DRRs, Protons

Taxonomy

IM- Particle (e.g., Proton) CT: Machine learning, computer vision

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