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20242026
most citedBART Streams: Real-time Reconstruction Using a Modular Framework for Pipeline Processing

1 citations · 1 across the 3 of their papers we have counts for

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physics.med-ph2026

BART Online Open-Source Sequence Toolbox for Computational MRI

Daniel Mackner, Philip Schaten, Markus Huemer +4

Purpose In advanced computational MRI techniques, acquisition and reconstruction techniques are jointly designed. For reproducibility, it is therefore important to provide an open…

physics.med-ph2026

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm

Moritz Blumenthal, Tina Holliber, Jonathan I. Tamir +1

Purpose: The Unadjusted Langevin Algorithm (ULA) in combination with diffusion models can generate high quality MRI reconstructions with uncertainty estimation from highly undersam…

physics.med-ph20261 cited

BART Streams: Real-time Reconstruction Using a Modular Framework for Pipeline Processing

Philip Schaten, Moritz Blumenthal, Bernhard Rapp +2

Purpose: To create modular solutions for interactive real-time MRI using reconstruction algorithms implemented in BART. Methods: A new protocol for streaming of multidimensional ar…

physics.med-ph2025

Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion

Moritz Blumenthal, Martin Uecker

Purpose: Phase singularities are a common problem in image reconstruction with auto-calibrated sensitivities due to an inherent ambiguity of the estimation problem. The purpose of…

physics.med-ph2024

Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI

Nick Scholand, Philip Schaten, Christina Graf +6

Purpose: To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle bet…

physics.med-ph2024

Self-Supervised Learning for Improved Calibrationless Radial MRI with NLINV-Net

Moritz Blumenthal, Chiara Fantinato, Christina Unterberg-Buchwald +3

Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a…