An Artificial Intelligence-Driven Agent for Real-Time Head-and-Neck IMRT Plan Generation using Conditional Generative Adversarial Network (cGAN)
Purpose: To develop an Artificial Intelligence (AI) agent for fully-automated rapid head and neck (H&N) IMRT plan generation without time-consuming inverse planning.$$$ Methods: This AI agent was trained using a conditional Generative Adversarial Network architecture. The generator, PyraNet, is...
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Main Authors | , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
27.09.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Purpose: To develop an Artificial Intelligence (AI) agent for fully-automated
rapid head and neck (H&N) IMRT plan generation without time-consuming inverse
planning.$$$
Methods: This AI agent was trained using a conditional Generative Adversarial
Network architecture. The generator, PyraNet, is a novel Deep Learning network
that implements 28 classic ResNet blocks in pyramid-like concatenations. The
discriminator is a customized 4-layer DenseNet. The AI agent first generates
customized 2D projections at 9 template beam angles from 3D CT volume and
structures of a patient. These projections are then stacked as 4D inputs of
PyraNet, from which 9 radiation fluence maps are generated simultaneously.
Finally, the predicted fluence maps are imported into a commercial treatment
planning system (TPS) for plan integrity checks. The AI agent was built and
tested upon 231 oropharyngeal plans from a TPS plan library. Only the primary
plans in the sequential boost regime were studied. A customized Harr wavelet
loss was adopted for fluence map comparison. Isodose distributions in test AI
plans and TPS plans were qualitatively evaluated. Key dosimetric metrics were
statistically compared.$$$
Results: All test AI plans were successfully generated. Isodose gradients
outside of PTV in AI plans were comparable with TPS plans. After PTV coverage
normalization, $D_{mean}$ of parotids and oral cavity in AI plans and TPS plans
were comparable without statistical significance. AI plans achieved comparable
$D_{max}$ at 0.01cc of brainstem and cord+5mm without clinically relevant
differences, but body $D_{max}$ was higher than the TPS plan results. The AI
agent needs ~3s per case to predict fluence maps.$$$
Conclusions: The developed AI agent can generate H&N IMRT plans with
satisfying dosimetry quality. With rapid and fully automated implementation, it
holds great potential for clinical applications. |
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DOI: | 10.48550/arxiv.2009.12898 |