OC-0361 Treatment planning and 4D robust evaluation for proton therapy of lung tumors with large motion

Taasti, V.; Hattu, D.; Vaassen, Femke; Canters, R.; Velders, M.; Mannens, J.; Loon, J. Van; Rinaldi, I.; Unipan, Mirko; Elmpt, Wouter van

doi:10.1016/s0167-8140(21)06876-6

Cited by 1 publication

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“…Several evaluations have been conducted on the use of sCT in adaptive proton radiotherapy, with most of these studies utilizing deep convolutional neural network (DCNN) and cycleGAN to generate sCT 29–32 . In this study, we aim to assess the suitability of different deep learning models, including the newly proposed cGAN, Unet+cycleGAN, and the conventional Unet and cycleGAN models, for generating sCT in NPC.…”

Section: Introductionmentioning

confidence: 99%

“…28 Several evaluations have been conducted on the use of sCT in adaptive proton radiotherapy, with most of these studies utilizing deep convolutional neural network (DCNN) and cycleGAN to generate sCT. [29][30][31][32] In this study, we aim to assess the suitability of different deep learning models, including the newly proposed cGAN, Unet+cycleGAN, and the conventional Unet and cycleGAN models, for generating sCT in NPC. By analyzing their unique structures and characteristics,we can determine which model is more appropriate for sCT generation and holds potential for further clinical exploration in adaptive proton therapy.…”

Section: Introductionmentioning

confidence: 99%

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Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy

Pang,

Si,

Liu

et al. 2023

Medical Physics

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BackgroundCone‐beam computed tomography (CBCT) scanning is used for patient setup in image‐guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning.PurposeThis study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer.MethodsCBCTs and deformed planning CT (dCT) from 75 patients (60/5/10 for training, validation and testing) were used to compare cycle‐consistent Generative Adversarial Network (cycleGAN), Unet, Unet+cycleGAN and conditionalGenerative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal‐to‐noise ratio (PSNR), spatial non‐uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose‐volume histogram metrics.ResultsThe cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%/2 mm) has reached 99.02%, and the WET difference is only 1.28 mm. The D95 value of CTVs coverage and Dmax value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model.ConclusionsThe cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.

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Section: Introductionmentioning

confidence: 99%