116 citations · 237 across the 8 of their papers we have counts for
9 papers
3D-GlioPREDICT: 3D Latent Diffusion for Post-Radiotherapy Brain MRI Prediction in Patients with Glioma
Nordin Belkacemi, Selena Huisman, Vera C. Keil +2
Radiotherapy is a cornerstone of glioma treatment inducing complex structural changes in brain tissue that are difficult to anticipate. Predicting these changes from pretreatment d…
Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
Selena Huisman, Nordin Belkacemi, Vera C. Keil +2
Brain tumors result in 20 years of lost life on average. Standard therapies induce complex structural changes in the brain that are monitored through MRI. Recent developments in ar…
Deep learning-based brain segmentation model performance validation with clinical radiotherapy CT
Selena Huisman, Matteo Maspero, Marielle Philippens +2
Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, i…
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon +6
Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomo…
Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis
Lotte Nijskens, Cornelis, AT van den Berg +2
Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (…
Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks
Maarten Terpstra, Matteo Maspero, Joost Verhoeff +1
Background: Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. Howe…