arXiv subjects

Felix Glang

Publications and source records attributed to Felix Glang.

5 recordsLinked to original sources

arXiv2026

Overcoming the limitations of NMR Field Probes: A Novel Integrated Sensor Utilizing Pre-Polarization for (Ultra) Low Field MRI

Access to magnetic resonance imaging (MRI) remains severely limited in low- and middle-income countries, especially in sub-Saharan Africa, despite rising rates of non-communicable diseases. Low-field MRI presents an affordable, locally developable diagnostic solution, but its performance is constrained by magnetic field instability. We present a novel NMR field probe designed to overcome these challenges using a rapid non-adiabatic switch-off of a pre-polarization field resulting in precessing spin magnetization. Achieved by first use of high-voltage silicon carbide transistors operating in controlled avalanche breakdown, it measures the Larmor frequency without prior field knowledge, unlike conventional probes. This capability is crucial during magnet development with often unknown fields, allowing early detection of magnet issues, and offering an urgently needed tool for magnet design and image-quality improvement. Validated from 1 mT to 45 mT (up to 1,000 times stronger than similar systems) its low-cost, modular design supports replication, upgrades, and enhanced field control, helping expand global MRI access.

physics.med-ph↗

arXiv2025

MR sequence design to account for non-ideal gradient performance

MRI systems are traditionally engineered to produce close to idealized performance, enabling a simplified pulse sequence design philosophy. An example of this is control of eddy currents produced by gradient fields; usually these are compensated by pre-emphasizing demanded waveforms. This process typically happens invisibly to the pulse sequence designer, allowing them to assume achieved gradient waveforms will be as desired. Whilst convenient, this requires system specifications exposed to the end-user to be substantially down-rated, since pre-emphasis adds an extra overhead to the waveforms. This strategy is undesirable for lower performance or resource-limited hardware. Instead, we propose an optimization-based method to design pre-compensated gradient waveforms that: (i) explicitly respect hardware constraints and (ii) improve imaging performance by correcting k-space samples directly. Gradient waveforms are numerically optimized by including a model for system imperfections. This is investigated in simulation using an exponential eddy current model, then experimentally using an empirical gradient system transfer function on a 7T MRI system. Our proposed method discovers solutions that produce negligible reconstruction errors while satisfying gradient system limits, even when classic pre-emphasis produces infeasible results. Substantial reduction in ghosting artefacts from EPI imaging was observed, including an average reduction of 77% in ghost amplitude in phantoms. This work demonstrates numerical optimization of gradient waveforms, yielding substantially improved image quality when given a model for system imperfections. While the method as implemented has limited flexibility, it could enable more efficient hardware usage, and may prove particularly important for maximizing performance of lower-cost systems.

physics.med-ph↗

arXiv2025

Coaxial Dipole Array with Switching Transmit Sensitivities for ultrahigh field MRI

Purpose: To investigate dipole antennas with electronically switchable transmit field patterns to improve flip angle homogeneity in ultra-high field MRI Methods: An array of eight coaxial dipoles with electronically switchable $B_{1}^{\!+}$ field profiles was constructed. Alteration of the field profiles was accomplished by modulating the currents along the dipoles using a combination of PIN diodes and lumped inductances. The behavior of these reconfigurable elements was studied in numerical electromagnetic simulations and 9.4T MRI measurements, investigating rapid switching of transmit sensitivities during excitation pulses in both single-channel and pTx mode operation. Results: For the simulated dipole elements, modulating the current densities along the dipole's axis causes a $\sim$30% change of the $B_{1}^{\!+}$ field between superior and inferior regions of the brain. When rapidly switched during excitation pulses, this degree of freedom can improve flip angle homogeneity, e.g. by a factor of $\sim$2.2 for a two kT points pTx pulse. For the constructed prototype array, the switching effect was observable but weaker, causing $\sim$10% superior-inferior $B_{1}^{\!+}$ variation. Conclusion: The proposed coaxial dipole array with switchable transmit sensitivities offers a novel degree of freedom for designing excitation pulses. The approach has the potential to improve flip angle homogeneity without necessitating an expensive increase in the number of independent transmit channels.

physics.med-ph↗

arXiv2021

High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced SSFP frequency profile

We suggest to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 T and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, i.e., mean, axial, and radial diffusivity (MD, AD, RD), fractional anisotropy (FA) as well as the spherical coordinates (azimuth $Φ$ and inclination $Θ$) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter (GM) structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased, e.g., in corpus callosum. The inclination $Θ$ was well predicted for anisotropic WM structures while the azimuth $Φ$ was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultra-high field strengths in clinically relevant scan times.

physics.med-ph↗

arXiv2021

MRzero -- Fully automated discovery of MRI sequences using supervised learning

Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient exploration of novel MR sequence strategies. Methods: The scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T1 and T2, and $Δ$B0. As a proof of concept, we use both conventional MR images and T1 maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time. Results: In a first attempt, \textit{MRzero} learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain \textit{in vivo}. Conclusions: Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.

physics.med-ph↗