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Tendler, B. C.

Publications and source records attributed to Tendler, B. C..

2 recordsLinked to original sources

Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP

Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. DW-SSFP datasets were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise across the brain. We compared one and two flip-angle DW-SSFP data using a diffusion tensor model that incorporates the full DW-SSFP Buxton signal model. The two-flip angle data were subsequently fitted using a modified DW-SSFP signal model that incorporates a Gamma distribution of diffusivities. This allowed us to generate tensor maps at a single, SNR-optimal effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments. HighlightsO_LIB1 inhomogeneity at 7T leads to spatially varying SNR & ADC estimates in DW-SSFP C_LIO_LI2-flip angle DW-SSFP data can address B1 effects in a cohort of post-mortem brains C_LIO_LIOur approach reduces degradations in PDD estimates & improves whole brain coverage C_LIO_LIOur approach provides a means to define ADCs at an SNR-optimal effective b-value C_LI

neuroscience

Tensor Image Registration Library: Automated Non-Linear Registration of Sparsely Sampled Histological Specimens to Post-Mortem MRI of the Whole Human Brain

There is a need to understand the histopathological basis of MRI signal characteristics in complex biological matter. Microstructural imaging holds promise for sensitive and specific indicators of the early stages of human neurodegeneration but requires validation against traditional histological markers before it can be reliably applied in the clinical setting. Validation relies on a precise and preferably automatic method to align MRI and histological images of the same tissue, which poses unique challenges compared to more conventional MRI-to-MRI registration. A customisable open-source platform, Tensor Image Registration Library (TIRL) is presented. Based on TIRL, a fully automated pipeline was implemented to align small stained histological images with dissection photographs of corresponding tissue blocks and coronal brain slices, and further with high-resolution (0.5 mm) whole-brain post-mortem MRI data. The pipeline performed three separate deformable registrations to achieve accurate mapping between whole-brain MRI and small-slide histology coordinates. The robustness and accuracy of the individual registration steps were evaluated using both simulated data and real-life images from 6 different anatomical locations of one post-mortem human brain. The automated registration method demonstrated sub-millimetre accuracy in all steps, robustness against tissue damage, and good reproducibility between experiments. The method also outperformed manual landmark-based slice-to-volume registration, also correcting for curvatures in the slicing plane. Due to the customisability of TIRL, the pipeline can be conveniently adapted for other research needs and is therefore suitable for the large-scale comparison of routinely collected histology and MRI data. HighlightsO_LITIRL: new framework for prototyping bespoke image registration pipelines C_LIO_LIPipeline for automated registration of small-slide histology to whole-brain MRI C_LIO_LISlice-to-volume registration accounting for through-plane deformations C_LIO_LINo need for serial histological sampling C_LI

neuroscience