Search Results for author:
Found 5 papers, 3 papers with code
Generalizable synthetic MRI with physics-informed convolutional networks
In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisition and investigate its ability to generalize to arbitrary contrasts to accelerate neuroimaging protocols.
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered CBCT and MRI images to facilitate the development and evaluation of sCT generation for radiotherapy planning.
Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis
Conclusions: DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI.
Deep learning-based synthetic-CT generation in radiotherapy and PET: a review
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones.
CBCT-to-CT synthesis with a single neural network for head-and-neck, lung and breast cancer adaptive radiotherapy
Another network was trained with all the anatomical sites together.
