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Med Phys. 2020 Feb;47(2):753-758. doi: 10.1002/mp.13953. Epub 2019 Dec 25.

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

Author information

1
Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Abstract

PURPOSE:

Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the accurate dose engines are often time consuming. In this work, we try to resolve this dilemma by exploring deep learning (DL) for dose calculation.

METHODS:

We developed a new radiotherapy dose calculation engine based on a modified Hierarchically Densely Connected U-net (HD U-net) model and tested its feasibility with prostate intensity-modulated radiation therapy (IMRT) cases. Mapping from an IMRT fluence map domain to a three-dimensional (3D) dose domain requires a deep neural network of complicated architecture and a huge training dataset. To solve this problem, we first project the fluence maps to the dose domain using a broad beam ray-tracing (RT) algorithm, and then we use the HD U-net to map the RT dose distribution into an accurate dose distribution calculated using a collapsed cone convolution/superposition (CS) algorithm. The model is trained on 70 patients with fivefold cross validation, and tested on a separate 8 patients.

RESULTS:

It takes about 1 s to compute a 3D dose distribution for a typical 7-field prostate IMRT plan, which can be further reduced to achieve real-time dose calculation by optimizing the network. The average Gamma passing rate between DL and CS dose distributions for the 8 test patients are 98.5% (±1.6%) at 1 mm/1% and 99.9% (±0.1%) at 2 mm/2%. For comparison of various clinical evaluation criteria (dose-volume points) for IMRT plans between two dose distributions, the average difference for dose criteria is less than 0.25 Gy while for volume criteria is <0.16%, showing that the DL dose distributions are clinically identical to the CS dose distributions.

CONCLUSIONS:

We have shown the feasibility of using DL for calculating radiotherapy dose distribution with high accuracy and efficiency.

KEYWORDS:

deep learning; dose calculation; radiotherapy

PMID:
31808948
DOI:
10.1002/mp.13953

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