AIMC Topic: Radiotherapy Dosage
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Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions.
Physics in medicine and biology
Oct 21, 2020
This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image information, into a densely-connected network (DCNN). Firstly, a four-channel feature map, consisting of a PT...
[Not Available].
Zeitschrift fur medizinische Physik
Oct 20, 2020
BACKGROUND: Currently there is an ever increasing interest in Lu-177 targeted radionuclide therapies, which target neuro-endocrine and prostate tumours. For a patient-specific treatment, an individual dosimetry based on SPECT/CT imaging is necessary....
A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Oct 17, 2020
PURPOSE: Artificial intelligence (AI) can play a significant role in Magnetic Resonance guided Radiotherapy (MRgRT), especially to speed up the online adaptive workflow. The aim of this study is to set up a Deep Learning (DL) approach able to generat...
A preliminary study of a photon dose calculation algorithm using a convolutional neural network.
Physics in medicine and biology
Oct 16, 2020
The aim of dose calculation algorithm research is to improve the calculation accuracy while maximizing the calculation efficiency. In this study, the three-dimensional distribution of total energy release per unit mass (TERMA) and the electron densit...
A deep learning model to predict dose-volume histograms of organs at risk in radiotherapy treatment plans.
Medical physics
Oct 15, 2020
PURPOSE: To develop a deep learning-based model to predict achievable dose-volume histograms (DVHs) of organs at risk (OARs) for automation of inverse planning.
Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Oct 7, 2020
PURPOSE: To assess the feasibility of magnetic resonance imaging (MRI)-only treatment planning for photon and proton radiotherapy in children with abdominal tumours.
Verification of the machine delivery parameters of a treatment plan via deep learning.
Physics in medicine and biology
Sep 30, 2020
We developed a generative adversarial network (GAN)-based deep learning approach to estimate the multileaf collimator (MLC) aperture and corresponding monitor units (MUs) from a given 3D dose distribution. The proposed design of the adversarial netwo...
Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Sep 23, 2020
BACKGROUND AND PURPOSE: To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a he...
A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy.
Physics in medicine and biology
Sep 14, 2020
We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information o...
Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.
Physics in medicine and biology
Sep 8, 2020
The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning effi...
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