Affiliation:
1. Cardiff University Brain Research Imaging Centre (CUBRIC) Cardiff University Cardiff UK
2. Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK
Abstract
AbstractPurposeThe characterization of tissue microstructure using diffusion MRI (dMRI) signals is rapidly evolving, with increasing sophistication of signal representations and microstructure models. However, this progress often requires signals to be acquired with very high b‐values (e.g., b > 30 ms/μm2), along many directions, and using multiple b‐values, leading to long scan times and extremely low SNR in dMRI images. The purpose of this work is to boost the SNR efficiency of dMRI by combining three particularly efficient spatial encoding techniques and utilizing a high‐performance gradient system (Gmax ≤ 300 mT/m) for efficient diffusion encoding.MethodsSpiral readouts, multiband imaging, and sampling on tilted hexagonal grids (T‐Hex) are combined and implemented on a 3T MRI system with ultra‐strong gradients. Image reconstruction is performed through an iterative cg‐SENSE algorithm incorporating static off‐resonance distributions and field dynamics as measured with an NMR field camera. Additionally, T‐Hex multiband is combined with a more conventional EPI‐readout and compared with state‐of‐the‐art blipped‐CAIPIRINHA sampling. The advantage of the proposed approach is furthermore investigated for clinically available gradient performance and diffusion kurtosis imaging.ResultsHigh fidelity in vivo images with b‐values up to 40 ms/μm2 are obtained. The approach provides superior SNR efficiency over other state‐of‐the‐art multiband diffusion readout schemes.ConclusionThe demonstrated gains hold promise for the widespread dissemination of advanced microstructural scans, especially in clinical populations.
Funder
Engineering and Physical Sciences Research Council
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Wellcome Trust
Wolfson Foundation
Subject
Radiology, Nuclear Medicine and imaging