7 citations · 18 across the 6 of their papers we have counts for
6 papers
A physics-informed, plug-and-play dose engine for gradient-based radiotherapy treatment planning
Attila Simkó, Matthias Kronsteiner, Simon Glatzer +11
Radiotherapy treatment planning remains a time-intensive iterative process requiring expert intervention in commercial treatment planning system (TPS). While machine learning appro…
Reproducibility of the Methods in Medical Imaging with Deep Learning
Attila Simko, Anders Garpebring, Joakim Jonsson +2
Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be i…
A Generalized Network for MRI Intensity Normalization
A. Simkó, T. Löfstedt, A. Garpebring +2
Image normalization, the correction for intra-volume inhomogeneities in magnetic resonance imaging (MRI) data has little significance for visual diagnosis, but is a crucial step be…
Statistical Methods in Computed Tomography Image Estimation
Fekadu L. Bayisa, Xijia Liu, Anders Garpebring +1
Purpose: There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images can be utilised for attenuation cor…
Sparsity estimation in compressive sensing with application to MR images
Jianfeng Wang, Zhiyong Zhou, Anders Garpebring +1
The theory of compressive sensing (CS) asserts that an unknown signal can be accurately recovered from measurements with provided that $\…
Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI
Jianfeng Wang, Anders Garpebring, Patrik Brynolfsson +2
The purpose of this study is to investigate a method, using simulations, to improve contrast agent quantification in Dynamic Contrast Enhanced MRI. Bayesian hierarchical models (BH…