30 citations · 92 across the 8 of their papers we have counts for
4 papers · 1 filter
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Fabian Wagner, Mareike Thies, Felix Denzinger +7
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(…
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning
Mayank Patwari, Ralf Gutjahr, Rainer Raupach +1
The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However,…
A quantitative comparison of plantar soft tissue strainability distribution and homogeneity between ulcerated and non-ulcerated patients using strain elastography
Maaynk Patwari, Panagiotis Chazistergos, Lakshmi Sundar +3
The primary objective of this study was to develop a method that allows accurate quantification of plantar soft tissue stiffness distribution and homogeneity. The secondary aim of…
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Fabian Wagner, Mareike Thies, Mingxuan Gu +9
Computed tomography is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution and radiation dose…