Entwicklung einer neuartigen Bilderfassungs- und modellbasierten Rekonstruktionsmethode zur schnellen, genauen und robusten multiparametrischen quantitativen MRT bei ultrahoher Feldstärke

• Antragsteller: Dr. Markus Zimmermann
• Fachliche Zuordnung: Medizinische Physik, Biomedizinische Technik ; Nuklearmedizin, Strahlentherapie, Strahlenbiologie
• Förderung: Förderung von 2020 bis 2022
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 446320670

• Erstellungsjahr: 2023

Zusammenfassung der Projektergebnisse

The development of fast, accurate, and robust methods for multiparametric quantitative MRI (qMRI) at ultrahigh field strength remains an important topic of research. With this project, we have successfully developed a novel qMRI technique, termed QRAGE, for the simultaneous quantification of multiple quantitative MRI parameters – water content, T1, T2*, and magnetic susceptibility. The proposed method combines a multi-echo MPnRAGE sequence with a novel model-based reconstruction technique. Within an acquisition time of 7:20 min, it acquires 171 different contrasts at different inversion and echo times with full brain coverage and 1 mm isotropic resolution from which the quantitative parameter maps are estimated. The model-based reconstruction technique allows a very high acceleration factor to be used, which facilitates a short measurement time while acquiring multiple contrast images. Thereby, it remains clinically feasible without compromising on accuracy and precision of parametric estimates. Comparison of the QRAGE method to gold-standard reference methods showed excellent agreement. As an additional advantage, QRAGE also provides a T1-weighted image with contrast comparable to that of the MP2RAGE sequence, making it suitable as a drop-in replacement. As the MP2RAGE sequence is used in almost every neuroscientific research protocol, ready acceptance of the QRAGE method is anticipated. A remaining challenge is the computationally time-consuming image reconstruction. Future work will focus on developing faster image reconstructing techniques to improve clinical applicability. To this end, we will investigate the suitability of distributed and GPU computing and novel deep learning-based approaches.

Projektbezogene Publikationen (Auswahl)

• QRAGE - Multi-Echo MPnRAGE and Model-Based Reconstruction for Quantitative MRI of Water Content, T1, T2* and Magnetic Susceptibility at 7T. ISMRM Annual Meeting. American Geophysical Union (AGU).
  Zimmermann, Markus; Abbas, Zaheer; Sommer, Yannic; Lewin, Alexander; Ramkiran, Shukti; Oros-Peusquens, Ana-Maria; Yun, Seong Dae & Shah, N. Jon