Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization
Filip Szczepankiewicz1,2,3, Jens Sjölund4,5, Erica Dall’Armellina6, Sven Plein6, Jürgen E. Schneider6, Irvin Teh6, and Carl-Fredrik Westin1,2
- Harvard Medical School, Boston, MA, USA
- Radiology, Brigham and Women’s Hospital, Boston, MA, USA
- Clinical Sciences Lund, Lund University, Lund, Sweden
- Elekta Instrument AB, Kungstensgatan 18, Box 7593, SE-103 93 Stockholm, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
Corresponding author
E-mail: filip.szczepankiewicz@med.lu.se
We present a gradient waveform design that is both more flexible and efficient than previous methods, facilitating tensor-valued diffusion encoding in tissues where motion would otherwise confound the signal. The proposed design exploits asymmetric encoding times, a single or multiple refocusing pulses, and integrates compensation for concomitant gradient effects throughout the imaging volume.
This repository contains all the waveforms shown in the paper and supporting information, along with the sampling protocols used for in vivo experiments. The optimization framework, including the proposed moment nulling, is available in open source at: https://github.com/jsjol/NOW
If you use these resources, please cite:
Szczepankiewicz et al., Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization. Magnetic Resonance in Medicine, 2021;85:2117–2126