5 papers · 1 filter
Agentic Autoresearch for CT Reconstruction
Andreas Maier, Lucas Kachelriess, Siming Bayer +4
Comparing CT reconstruction methods fairly is labor-intensive and largely manual, and many benchmarks use idealized data. We ask whether a large language model (LLM) agent can do t…
Agentic MR sequence development: leveraging LLMs with MR skills for automatic physics-informed sequence development
Moritz Zaiss, Amr Aly, Jonathan Endres +3
Purpose: Novel MR sequence developments still today allow generation of new diagnostic tools or novel imaging biomarkers. Programming MRI pulse sequences, however, is time-consumin…
Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning
Alex Finkelstein, Nikita Vladimirov, Moritz Zaiss +1
Biophysical model fitting plays a key role in obtaining quantitative parameters from physiological signals and images. However, the model complexity for molecular magnetic resonanc…
Optimization of pulsed saturation transfer MR fingerprinting (ST MRF) acquisition using the Cramér-Rao bound and sequential quadratic programming
Nikita Vladimirov, Moritz Zaiss, Or Perlman
Purpose: To develop a method for optimizing pulsed saturation transfer MR fingerprinting (ST MRF) acquisition. Methods: The Cramér-Rao bound (CRB) for variance assessment was empl…
Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework
Dinor Nagar, Moritz Zaiss, Or Perlman
Magnetic resonance imaging (MRI) relies on radiofrequency (RF) excitation of proton spin. Clinical diagnosis requires a comprehensive collation of biophysical data via multiple MRI…