58 citations · 65 across the 6 of their papers we have counts for
6 papers
Dual-Tuned 31P-1H Dual-Row Loop/Dipole 32-element Transceiver Array for Human Brain Spectroscopy at 9.4T
G. A. Solomakha, R. Pohmann, F. Glang +8
Purpose The goal of this work is to develop and evaluate a single-layer tight-fit 32-element double-tuned loop/dipole transceiver (TxRx) array for human brain 31P MRS at 9.4T, achi…
Overcoming the limitations of NMR Field Probes: A Novel Integrated Sensor Utilizing Pre-Polarization for (Ultra) Low Field MRI
Pavel Povolni, Dominique Goerner, Praveen Iyyappan Valsala +9
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…
Coaxial Dipole Array with Switching Transmit Sensitivities for ultrahigh field MRI
Dario Bosch, Georgiy A. Solomakha, Felix Glang +3
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…
MR sequence design to account for non-ideal gradient performance
Daniel J West, Felix Glang, Jonathan Endres +4
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 cu…
High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced SSFP frequency profile
Florian Birk, Felix Glang, Alexander Loktyushin +4
We suggest to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimat…
MRzero -- Fully automated discovery of MRI sequences using supervised learning
Alexander Loktyushin, Kai Herz, Nam Dang +7
Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with…