254 citations · 710 across the 10 of their papers we have counts for
10 papers
Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy
Mianyong Ding, Matteo Maspero, Annemieke S Littooij +5
Purposes: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and…
Generalizable synthetic MRI with physics-informed convolutional networks
Luuk Jacobs, Stefano Mandija, Hongyan Liu +3
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 acquisitio…
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…
Deep learning-based synthetic-CT generation in radiotherapy and PET: a review
Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino +1
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical one…