DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy
| Autor: | Y.M. Yang, Yangsibo Huang, Ryan Neph, Ke Sheng, Qihui Lyu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Magnetic Resonance Spectroscopy
Computer science medicine.medical_treatment Noise reduction Monte Carlo method Graphics processing unit Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Deep Learning 0302 clinical medicine Sampling (signal processing) Neoplasms medicine Humans Computer Simulation Radiology Nuclear Medicine and imaging Adaptive radiotherapy Radiation treatment planning Simulation Radiological and Ultrasound Technology medicine.diagnostic_test Phantoms Imaging Noise (signal processing) Radiotherapy Planning Computer-Assisted Radiation dose Radiotherapy Dosage Magnetic resonance imaging Radiation therapy 030220 oncology & carcinogenesis Monte Carlo Method Algorithms Radiotherapy Image-Guided |
| Zdroj: | Phys Med Biol |
| ISSN: | 1361-6560 0031-9155 |
| DOI: | 10.1088/1361-6560/abca01 |
| Popis: | Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation. |
| Databáze: | OpenAIRE |
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