activity
20242026
most citedMulti-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning

11 citations · 14 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Beating the Style Detector: Three Hours of Agentic Research on the AI-Text Arms Race

Andreas Maier, Moritz Zaiss, Siming Bayer

Reproducing an empirical NLP study used to take weeks. Given the released data and a modern agentic-research harness, we redo every experiment of a recent ACL\,2026 study on person…

physics.med-ph2026

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…

stat.ML2026

Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)

Alex Finkelstein, Ron Moneta, Or Zohar +4

Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating biophysical tissue parameters from si…

physics.med-ph2025★ 3 cited

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 emplo…

physics.med-ph2024★ 11 cited

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…

physics.med-ph2024

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…