title: In vivo high angular resolution diffusion-weighted imaging of mouse brain at 16.4 Tesla.
abstract: Magnetic Resonance Imaging (MRI) of the rodent brain at ultra-high magnetic fields (> 9.4 Tesla) offers a higher signal-to-noise ratio that can be exploited to reduce image acquisition time or provide higher spatial resolution. However, significant challenges are presented due to a combination of longer T1 and shorter T2/T2* relaxation times and increased sensitivity to magnetic susceptibility resulting in severe local-field inhomogeneity artefacts from air pockets and bone/brain interfaces. The Stejskal-Tanner spin echo diffusion-weighted imaging (DWI) sequence is often used in high-field rodent brain MRI due to its immunity to these artefacts. To accurately determine diffusion-tensor or fibre-orientation distribution, high angular resolution diffusion imaging (HARDI) with strong diffusion weighting (b >3000 s/mm2) and at least 30 diffusion-encoding directions are required. However, this results in long image acquisition times unsuitable for live animal imaging. In this study, we describe the optimization of HARDI acquisition parameters at 16.4T using a Stejskal-Tanner sequence with echo-planar imaging (EPI) readout. EPI segmentation and partial Fourier encoding acceleration were applied to reduce the echo time (TE), thereby minimizing signal decay and distortion artefacts while maintaining a reasonably short acquisition time. The final HARDI acquisition protocol was achieved with the following parameters: 4 shot EPI, b = 3000 s/mm2, 64 diffusion-encoding directions, 125×150 μm2 in-plane resolution, 0.6 mm slice thickness, and 2h acquisition time. This protocol was used to image a cohort of adult C57BL/6 male mice, whereby the quality of the acquired data was assessed and diffusion tensor imaging (DTI) derived parameters were measured. High-quality images with high spatial and angular resolution, low distortion and low variability in DTI-derived parameters were obtained, indicating that EPI-DWI is feasible at 16.4T to study animal models of white matter (WM) diseases.
authors.affiliation: Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia; College of Applied Medical Science, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Othman I
authors.initials: OI
authors.lastname: Alomair
authors.affiliation: Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia.
authors.forename: Ian M
authors.initials: IM
authors.lastname: Brereton
authors.affiliation: School of Pharmacy, University of Queensland, Brisbane, Queensland, Australia.
authors.forename: Maree T
authors.initials: MT
authors.lastname: Smith
authors.affiliation: Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia.
authors.forename: Graham J
authors.initials: GJ
authors.lastname: Galloway
authors.affiliation: Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia.
authors.forename: Nyoman D
authors.initials: ND
authors.lastname: Kurniawan
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods; D017352:Echo-Planar Imaging / Q000379:methods; D005583:Fourier Analysis; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D056784:Leukoencephalopathies / Q000473:pathology; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D012984:Software