150 citations
- Universitätsmedizin GöttingenDE33 papers
- University of GöttingenDE26 papers
- Graz University of TechnologyAT12 papers
- BioTechMed-GrazAT7 papers
- Nanoscale Microscopy and Molecular Physiology of the Brain Cluster of Excellence 171 — DFG Research Center 103DE7 papers
- Heidelberg UniversityDE6 papers
- University Hospital HeidelbergDE6 papers
- Charité - Universitätsmedizin BerlinDE5 papers
- Max Planck Institute for Dynamics and Self-OrganizationDE5 papers
- Ludwig-Maximilians-Universität MünchenDE4 papers
- Max Planck Institute for Biophysical ChemistryDE4 papers
- Advanced Imaging Research (United States)US3 papers
5 papers · 1 filter
Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations
Nick Scholand, Xiaoqing Wang, Volkert Roeloffs +2
Purpose: Development of a generic model-based reconstruction framework for multi-parametric quantitative MRI that can be used with data from different pulse sequences. Methods: Gen…
In silico modeling for personalized stenting in aortic coarctation
Dandan Ma, Yong Wang, Mueed Azhar +3
Stent intervention is a recommended therapy to reduce the pressure gradient and restore blood flow for patients with coarctation of the aorta (CoA). In this work, we developed a fr…
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
Lalith Sharan, Halvar Kelm, Gabriele Romano +3
Multi-point tracking is a challenging task that involves detecting points in the scene and tracking them across a sequence of frames. Computing detection-based measures like the F-…
Deep, Deep Learning with BART
Moritz Blumenthal, Guanxiong Luo, Martin Schilling +2
Purpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibra…
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
Guanxiong Luo, Moritz Blumenthal, Martin Heide +1
We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. Different from conventional deep learning-based MRI reconstr…