Efficient diffusion MRI measurements of tissue microstructure with spherical and planer tensor encoding

Introduction:: Recently, non-gaussian microstructural MRI methods such as mean apparent propagator (MAP-MRI) have emerged with sensitivity to spatial features on the order of microns and may detect changes in the brain tissue environment like cellularity, morphology, or protein accumulation that are more sensitive and specific than the conventional diffusion tensor imaging (DTI) method [1]. However, implementing these methods into a clinical setting is challenging due to long acquisition times, hardware requirements, and complex image processing needed to support these higher-order models for extrapolating the microstructural information. Alternative diffusion acquisition methods have recently been developed such as q-space trajectory imaging (QTI) [2] that use continuous waveforms instead of traditional diffusion weighting and may generate diffusion weighted images (DWIs) that report targeted microstructural information about microscale-anisotropy and restriction in a model-free manner without the need to collect an extensive range of b-values and directions [3]. While QTI is a relatively new acquisition method compared to traditional diffusion schemes, it has shown strong promise in sensitivity and distinguishing microenvironments at a fraction of the time required for conventional diffusion MRI. This study aims to evaluate a range of QTI DWI encoding by comparison to ground truth diffusion microscale-anisotropy and restriction metrics.

Materials and Methods:: Imaging was performed for a healthy-aged formalin fixed post-mortem human temporal lobe specimen received from the Banner Sun Health Brain and Body Donation Program [4]. The specimen was prepared for imaging in a 50 ml falcon tube vacuum sealed with Fluorinert for scanning. Images were acquired using a 7T Bruker Biospec MRI scanner, the conventional diffusion used a multi-shell diffusion acquisition with 201 DWI volumes over b =0-6,000 s/mm2 at an isotropic resolution of 250 microns for an acquisition time of ~48 hours. Diffusion pre-processing and MAP-MRI calculations were performed using TORTOISE 3.2.0 [1, 5, 6] to generate propagator anisotropy (PA), the micro-anisotropic metric, and return-to-origin probability (RTOP), the restriction metric. Continuous waveforms used in the QTI acquisition were developed in the NOW software [7] for spherical tensor encoding (STE) and planar tensor encoding (PTE) both with timings of D=36,30 ms and d=12 ms [8]. Both STE and PTE were acquired at gradient strengths of G=26-166 mT/m over 6 directions for 24 DWIs volumes each at an isotropic resolution of 200 microns for an acquisition time of ~20 minutes per scan. Unprocessed QTI DWI volumes were averaged with their respective gradient strengths over the 6 directions for both STE and PTE waveforms. Histogram analysis was performed for the whole specimen for the PA and RTOP^1/3 maps and QTI values were plotted over the full range of gradient strengths for both PTE and STE waveforms.

Results, Conclusions, and Discussions:: Histogram analysis in PA and RTOP were used as “ground truth” references for microscale anisotropy and restriction respectively and revealed two dominant peaks with lower values corresponding with gray matter neurites and the higher values with the white matter myelinated axon bundle pathways. PA demonstrated a third intermediate peak associated with axonal pathways within gray matter regions. Histogram analysis in PTE and STE both showed that as gradient strengths increased, the intensity values decreased. However, between the two QTI waveforms, PTE was more influenced by higher gradient strengths than STE. This effect is apparent when observing the PTE gradient strength of 83 mT/m (blue line) and 117 mT/m (pink line) in STE, where PTE more readily observed to shift towards the left. While we expected similarities between PA and PTE distributions, the gradient strengths and directional averaging used in the current work for PTE were not able to fully capture the PA distribution. On the other hand, our expectation that RTOP and STE would closely resemble each other was supported for the highest gradient strength of 166 mT/m suggesting that strong STE weighting may adequately probe isotropic restriction.

The objective of this study is to develop QTI waveforms capable of probing similar compartments that are mapped in the high-order maps of microscale anisotropy and restriction. Similarities are already observable between the STE waveform and the RTOP map, but additional future validations will need to be done. Unfortunately, while there are similarities for PA and PTE, more work is needed on acquisition development to determine the gradient weightings, timings, and angular sampling scheme needed for the middle and right peaks. In addition to examining the dependence of DWI contrast on QTI encoding, we also plan to extend these methods for the detection of neuropathology such as in Alzheimer’s disease. QTI has promise to provide a method for early diagnosis of neurodegenerative disease in a clinical setting, and potential to report highly restricted pools and micro-anisotropy at a fraction of time required in traditional diffusion.


Acknowledgements (Optional): : This research was supported by the NIA/NIH grant R03 780250. All imaging was performed in the UA translational bioimaging resource (TBIR) and made possible by the NIH small instrumentation grant: S10 OD025016. The authors would like to thank High Performance Computing (HPC) for providing the resources needed. Thank you to all the MBSIL members for their support.


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