Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder-decoder network.
Gerd HeilemannLukas ZimmermannRaphael SchotolaWolfgang LechnerMarco PeerJoachim WidderGregor GoldnerDietmar GeorgPeter KuessPublished in: Medical physics (2023)
The deep learning-based model could predict MLC motion sequences in prostate VMAT plans, eliminating the need for sequencing inside a TPS, thus revolutionizing autonomous treatment planning workflows. This research completes the loop in deep learning-based treatment planning processes, enabling more efficient workflows for real-time or online adaptive radiotherapy.
Keyphrases
- deep learning
- prostate cancer
- benign prostatic hyperplasia
- health insurance
- artificial intelligence
- convolutional neural network
- early stage
- high speed
- machine learning
- social media
- radiation induced
- transcription factor
- locally advanced
- health information
- radiation therapy
- healthcare
- high resolution
- smoking cessation