Synopsis

Multi-shell diffusion MRI (dMRI) allows for analyzing the water diffusion signal using multi-compartment diffusion models, providing more specific characterization of tissue microstructure in grey matter and white matter than standard diffusion tensor imaging (DTI). However, the traditional multi-shell dMRI data acquisition usually demands high gradient strength and long scanning duration and its application is hindered for subjects like fetuses and infants in which fast imaging and reduced gradient strength is required. In the present study, the quantification analysis of NODDI and DBSI was evaluated using different hybrid diffusion imaging (HYDI) acquisition strategy for fast imaging on a clinical 3T setting. The results demonstrated that the data acquisition time for multi-shell dMRI can be reduced dramatically using HYDI gradient encoding strategy, while the quality of derived NODDI, DBSI, and DTI indices is generally maintained, suggesting quantitative analysis of multi-compartment models are applicable for developmental study of whole brain fetuses and infants by using multi-shell dMRI with HYDI encoding scheme.

Purpose

To evaluate the effects of multi-shell dMRI with different gradient encoding schemes for fast data acquisition on the quantification analysis of multi-compartment models on a 3T clinical scanner.

Introduction

Multi-shell diffusion MRI (dMRI) allows for analyzing the water diffusion signal using multi-compartment diffusion models, providing more specific characterization of tissue microstructure in grey matter and white matter than standard diffusion tensor imaging (DTI). However, the traditional multi-shell dMRI data acquisition usually demands high gradient strength and long scanning duration and its application is hindered for subjects like fetuses and infants in which fast imaging and reduced gradient strength is required. The multi-compartment diffusion models including composite hindered and restricted model of diffusion (CHARMED), neurite orientation dispersion and density imaging (NODDI) and diffusion basis spectrum imaging (DBSI), et al [1-3] have been increasingly used in clinical and preclinical researches in recent years. In the present study, the quantification analysis of NODDI and DBSI was evaluated using different hybrid diffusion imaging (HYDI) acquisition strategy for fast imaging on a clinical 3T setting.

Methods and materials

Multi-shell diffusion weighted images (DWI) were acquired with a Siemens 3T TIM Trio scanner using 8-channel phased-array volume coil and an echo planar imaging (EPI) sequence with the voxel size = 1.3 mm × 1.3 mm × 1.3 mm, FOV = 91 mm × 67.6 mm, TE/TR = 99 ms/4.8s, GRAPPA factor = 3, 34 slices to cover the whole brain. Adult macaque monkeys were scanned under 1-1.5% isoflurane anaesthesia. Phase-reversal data acquisition was applied for susceptibility artefact correction of EPI images. The gradient tables for HYDI data collection were generated with the multi-shell sampling schemes reported previously [5] and the encoding schemes include the directions of 32 (b=0, 400, 750, 1100, 1500 s/mm2, time of acquisition (TA) =3 minutes ), 59 (b=0, 300, 600, 900, 1200, 1500 s/mm2, TA=5 minutes), and 80 (b = 0,350,700, 1000,1300,1700,2000 s/mm2, TA=8 minutes). Also, images with 30 directions in each shell (b=0, 1000, 2000 s/mm2, TA=5 minutes) were acquired. Each scheme was repeated 4 times for average. Images were pre-processed with FSL software package for motion, eddy-current, and distortion correction. NODDI, DBSI and DTI indices were obtained using reported customer-built Matlab packages [1, 2]. The DWI data of the four gradient encoding schemes were collected from the same adult macaque monkey in each scan session.

Results

The maps of NODDI microstructural indices including neurite orientation dispersion index (ODI), intra-cellular volume fraction (ICVF), and volume fraction of Gaussian isotropic diffusion (FISO) from each encoding scheme are displayed in Fig 1. The maps of DBSI indices including restricted and hindered diffusion fractions and water ratio are displayed in Fig 2. The maps of mean diffusivity (MD), axial diffusivity (AD) and fractional anisotropy (FA) are shown in Fig 3. The illustrated maps are from the same monkey and same scanning session. As seen in Fig 1-3, the NODDI indices (ODI and FISO), and the hindered diffusion fractions (Hin) and water ratio (Wat) of DBSI resemble with each other when the gradient encoding scheme was changed from 32 directions (b=1500 s/mm2) to 80 directions (b=2000 s/mm2). In contrast, the ICVF index of NODDI and restricted diffusion fractions of DBSI showed low intensity in the 32-direction encoding scheme. In addition, the DTI diffusivity indices (MD, FA, AD, and RD (data not shown)) are consistent across these different encoding schemes.

Discussions and Conclusions

Acknowledgements

References

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