Low-Field MRI
Motivation
Over the years, magnetic resonance imaging (MRI) technology has significantly improved by focusing on increasing the strength of magnetic fields. Higher magnetic fields enhance the signal quality, resulting in clearer images and better diagnostic accuracy. However, these so-called high-field MRI systems, which typically operate at 1.5 Tesla or higher, are costly and require a specialized infrastructure. This makes them less accessible, especially in areas with limited resources.
Contrasting, the low-field MRI utilizes operates at lower field strengths. These systems do not require dedicated infrastructure and offer a portable, compact build. Thus, the main challenges of the traditional high-field MRI are addressed as the low-field system is affordable, portable and accessible. The prior restrictions, i.e. the low signal quality, imaging contrast and resolution, are overcome by means of innovations in magnet designs, imaging techniques and AI methods, making low-field MRI increasingly effective while still providing reliable diagnostic capabilities. Therefore, by means of advanced methods, low-field MRI is paving the way towards a more comprehensive and accessible medical imaging in the future.
Research Topics
Innovative Magnetic Design
This research field focuses on developing advanced methods for magnetic designs for both static and gradient fields using resistive and permanent magnets. By optimizing magnetic configurations, such as Halbach arrays and adaptive shielding, the goal is to achieve improved field homogeneity, stability, and cost-efficiency, which are crucial for enhancing the performance of low-field MRI systems.
Enhanced Imaging Techniques
We explore innovative imaging methods, including pulse sequence designs, to maximize the potential of low-field MRI. These techniques reduce off-resonance effects and specific absorption rates (SAR), enabling more flexible and precise imaging. Improved visualization of lung tissue, heart structures and regions in the vicinity of metal implants are, thus, possible, expanding the diagnostic possibilities of low-field MRI. In particular, we are focusing on research into highly specialized MRI sequences for screen applications.
Signal Modeling with Machine Learning and Physical Models
By integrating artificial intelligence and physics-based models, this research direction enhances signal processing in low-field MRI. Machine learning algorithms, such as universal noise reduction, overcome challenges such as low signal-to-noise ratios (SNR) and lead to faster, clearer and more reliable imaging. These methods ensure that low-field MRI remains a competitive and robust tool for medical diagnostics.
DeLoRI-Project
The LfB RWTH Aachen University, in collaboration with Fraunhofer MEVIS, is conducting cutting-edge research in the field of low-field MRI for new applications. Within the framework of the DeLoRI (Dedicated low-field MRI for breast) project, our focus is on developing a system specifically tailored for cancer screening. This project seeks to showcase the transformative potential of low-field MRI in enhancing early detection and diagnosis, particularly for underserved communities. By integrating advanced technologies and innovative methodologies, the project aims to redefine medical imaging, paving the way for a more inclusive, accessible, and impactful future in healthcare.
Thesis Topics
We offer a wide variety of theses along the entire Low-Field MRI development chain. These can be tailored to the candidate profile in the direction of magnetic systems, electronics and firmware development, characterization work or with a focus on software development, data analysis and algorithm development.
A list of all current topics is available here.
Design and Evaluation of whole FDM 3D-Printed RF Coils made of conductive Materials for use in low-field MRI Systems Contact: Marcel Ochsendorf
More information about the research project
Design and Implementation of an NMR Field Probe for Comprehensive Characterization of Low-Field MRI Magnet Systems Contact: Marcel Ochsendorf
More information about the research project
Development and Evaluation of a framework for real-time synchronization of microcontrollers in distributed Low-Field MRI control systems Contact: Marcel Ochsendorf
More information about the research project
Cooperation partner
Contact
Low-Field MRI
Motivation
Over the years, magnetic resonance imaging (MRI) technology has significantly improved by focusing on increasing the strength of magnetic fields. Higher magnetic fields enhance the signal quality, resulting in clearer images and better diagnostic accuracy. However, these so-called high-field MRI systems, which typically operate at 1.5 Tesla or higher, are costly and require a specialized infrastructure. This makes them less accessible, especially in areas with limited resources.
Contrasting, the low-field MRI utilizes operates at lower field strengths. These systems do not require dedicated infrastructure and offer a portable, compact build. Thus, the main challenges of the traditional high-field MRI are addressed as the low-field system is affordable, portable and accessible. The prior restrictions, i.e. the low signal quality, imaging contrast and resolution, are overcome by means of innovations in magnet designs, imaging techniques and AI methods, making low-field MRI increasingly effective while still providing reliable diagnostic capabilities. Therefore, by means of advanced methods, low-field MRI is paving the way towards a more comprehensive and accessible medical imaging in the future.
Research Topics
Innovative Magnetic Design
This research field focuses on developing advanced methods for magnetic designs for both static and gradient fields using resistive and permanent magnets. By optimizing magnetic configurations, such as Halbach arrays and adaptive shielding, the goal is to achieve improved field homogeneity, stability, and cost-efficiency, which are crucial for enhancing the performance of low-field MRI systems.
Enhanced Imaging Techniques
We explore innovative imaging methods, including pulse sequence designs, to maximize the potential of low-field MRI. These techniques reduce off-resonance effects and specific absorption rates (SAR), enabling more flexible and precise imaging. Improved visualization of lung tissue, heart structures and regions in the vicinity of metal implants are, thus, possible, expanding the diagnostic possibilities of low-field MRI. In particular, we are focusing on research into highly specialized MRI sequences for screen applications.
Signal Modeling with Machine Learning and Physical Models
By integrating artificial intelligence and physics-based models, this research direction enhances signal processing in low-field MRI. Machine learning algorithms, such as universal noise reduction, overcome challenges such as low signal-to-noise ratios (SNR) and lead to faster, clearer and more reliable imaging. These methods ensure that low-field MRI remains a competitive and robust tool for medical diagnostics.
DeLoRI-Project
The LfB RWTH Aachen University, in collaboration with Fraunhofer MEVIS, is conducting cutting-edge research in the field of low-field MRI for new applications. Within the framework of the DeLoRI (Dedicated low-field MRI for breast) project, our focus is on developing a system specifically tailored for cancer screening. This project seeks to showcase the transformative potential of low-field MRI in enhancing early detection and diagnosis, particularly for underserved communities. By integrating advanced technologies and innovative methodologies, the project aims to redefine medical imaging, paving the way for a more inclusive, accessible, and impactful future in healthcare.
Thesis Topics
We offer a wide variety of theses along the entire Low-Field MRI development chain. These can be tailored to the candidate profile in the direction of magnetic systems, electronics and firmware development, characterization work or with a focus on software development, data analysis and algorithm development.
A list of current topics is available here.
Cooperation partner
Contact