Biology subjects

Miller, K. L.

Publications and source records attributed to Miller, K. L..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Tensor Image Registration Library: Automated Deformable Registration of Stand-Alone Histology Images to Whole-Brain Post-Mortem MRI Data

BackgroundAccurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to automate the registration of stand-alone histology sections to whole-brain MRI data. MethodsOur pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slice photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. ResultsAll 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. ConclusionsOur open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies. HighlightsO_LINew software framework for prototyping bespoke image registration pipelines C_LIO_LIAutomated pipeline to register stand-alone histology sections to whole-brain MRI C_LIO_LINovel deformable slice-to-volume registration algorithm C_LIO_LINo strict necessity for serial histological sectioning for MRI-histology registration C_LI

neuroscience↗

bioRxiv2022

An automated pipeline for extracting quantitative histological metrics for voxelwise MRI-histology comparisons

The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on heavy manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology coregistrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of certain tissue features (e.g. myelin and neurofilaments). Further, we find activated microglia to consistently be the strongest predictor of DTI FA, which may suggest sensitivity of diffusion MRI to neuroinflammation. Taken together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.

neuroscience↗

bioRxiv2021

Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging

A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging MRI technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We developed a QSM processing pipeline to estimate magnetic susceptibility of multiple brain structures in 35,885 subjects from the UK Biobank prospective epidemiological study. We identified phenotypic associations of magnetic susceptibility that include body iron, disease, diet, and alcohol consumption. Genome-wide associations related magnetic susceptibility to genetic variants with biological functions involving iron, calcium, myelin, and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers world-wide, creating potential to discover novel, non-invasive markers of brain health.

neuroscience↗

bioRxiv2021

The Forget-Me-Not dHCP study: 7 Tesla high resolution diffusion imaging in the unfixed post-mortem neonatal brain

Diffusion MRI of the neonatal brain allows investigation of the organisational structure of maturing fibres during brain development. Post-mortem imaging has the potential to achieve high resolution by using long scan times, enabling precise assessment of small structures. The Forget-Me-Not study, part of the Developing Human Connectome Project (dHCP), aims to acquire and publicly distribute high-resolution diffusion MRI data for unfixed post-mortem neonatal brain at 7T with a custom-built head coil. This paper describes how the study addressed logistical, technical and ethical challenges relating to recruitment pipeline, care pathway, tissue preservation, scan setup and protocol optimisation. Results from the first subject recruited to the study demonstrate high-quality diffusion MRI data. Preliminary voxel-wise and tractography-based analyses are presented for the cortical plate, subplate and white matter pathways, with comparison to age-matched in vivo dHCP data. These results demonstrate that high quality post-mortem data can be acquired and provide a sensitive means to explore the developing human brain, as well as altered diffusion properties consistent with post-mortem changes, at high resolution.

bioengineering↗

bioRxiv2021

The Digital Brain Bank: an open access platform for post-mortem datasets

Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes - Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our labs investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides crossscale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.

neuroscience↗

bioRxiv2020

Subspace-constrained approaches to low-rank fMRI acceleration

Acceleration methods in fMRI aim to reconstruct high fidelity images from undersampled k-space, allowing fMRI datasets to achieve higher temporal resolution, reduced physiological noise aliasing, and increased statistical degrees of freedom. While low levels of acceleration are typically part of standard fMRI protocols through parallel imaging, there exists the potential for approaches that allow much greater acceleration. One such existing approach is k-t FASTER, which exploits the inherent low-rank nature of fMRI. In this paper, we present a reformulated version of k-t FASTER which includes additional L2 constraints within a low-rank framework. We evaluated the effect of three different constraints against existing low-rank approaches to fMRI reconstruction: Tikhonov constraints, low-resolution priors, and temporal subspace smoothness. The different approaches are separately tested for robustness to undersampling and thermal noise levels, in both retrospectively and prospectively-undersampled finger-tapping task fMRI data. Reconstruction quality is evaluated by accurate reconstruction of low-rank subspaces and activation maps. The use of L2 constraints were found to achieve consistently improved results, producing high fidelity reconstructions of statistical parameter maps at higher acceleration factors and lower SNR values than existing methods, but at a cost of longer computation time. In particular, the Tikhonov constraint proved very robust across all tested datasets, and the temporal subspace smoothness constraint provided the best reconstruction scores in the prospectively-undersampled dataset. These results demonstrate that regularized low-rank reconstruction of fMRI data can recover functional information at high acceleration factors without the use of any model-based spatial constraints. Highlights O_LIWe introduce an alternate implementation of low-rank fMRI reconstruction by using alternating minimization, which allows for easy integration of the subspace-specific L2 constraints C_LIO_LIWe use the alternating minimization approach to accelerate FMRI by exploiting coil sensitivity, low-rank structures, and additional L2 constraints C_LIO_LIWe found Tikhonov and Temporal Subspace Smoothness constraints show improved performance over other methods for R=15-30 C_LIO_LITikhonov Constraints were the most robust of the constrained-subspace methods, with the shortest reconstruction time C_LIO_LITemporal Subspace Smoothness produced the highest reconstruction scores in the prospectively under-sampled data C_LI

bioinformatics↗

bioRxiv2020

Estimating intra-axonal axial diffusivity in the presence of fibre orientation dispersion

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d[boxV] = 1.7m2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ~ 2 - 2.5m2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

neuroscience↗

bioRxiv2020

A method to remove the influence of fixative concentration on post-mortem T2 maps using a Kinetic Tensor model

Formalin fixation has been shown to substantially reduce T2 estimates when performing post-mortem imaging, primarily driven by the presence of bulk fixative in tissue. Prior to scanning, post-mortem tissue samples are often placed into a fluid that has more favourable imaging properties, such as matched magnetic susceptibility. This study investigates whether there is any evidence for a change in T2 in regions close to the tissue surface in post-mortem T2 maps due to fixative outflux into this surrounding fluid. Furthermore, we investigate whether a simulated spatial map of fixative concentration can be used as a confound regressor to reduce T2 inhomogeneity. To achieve this, T2 maps and diffusion tensor estimates were obtained in 14 whole, formalin fixed post-mortem brains placed in fluorinert approximately 48 hours prior to scanning. This consisted of 7 brains fixed with 10% formalin and 7 brains fixed with 10% neutral buffered formalin (NBF). Fixative outflux was modelled using a Kinetic Tensor (KT) model, which incorporates voxelwise diffusion tensor estimates to account for diffusion anisotropy and tissue-specific diffusion coefficients. Brains fixed with 10% NBF revealed a spatial T2 pattern consistent with the modelled fixative outflux. Confound regression of fixative concentration reduced T2 inhomogeneity across both white and grey matter, with the greatest reduction attributed to the KT model vs simpler models of fixative outflux. No such effect was observed in brains fixed with 10% formalin. Correlations with ferritin and myelin proteolipid protein (PLP) histology lead to an increased similarity for the relationship between T2 and PLP for the two fixative types after KT correction. Only small correlations were identified between T2 and ferritin before and after KT correction.

neuroscience↗

bioRxiv2020

Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T

Susceptibility weighted magnetic resonance imaging (MRI) is sensitive to the local concentration of iron and myelin. Here, we describe a robust image processing pipeline for quantitative susceptibility mapping (QSM) and R2* mapping of fixed post-mortem, whole-brain data. Using this pipeline, we compare the resulting quantitative maps in brains from patients with amyotrophic lateral sclerosis (ALS) and controls, with validation against iron and myelin histology. Twelve post-mortem brains were scanned with a multi-echo gradient echo sequence at 7T, from which susceptibility and R2* maps were generated. Semi-quantitative histological analysis for ferritin (the principal iron storage protein) and myelin proteolipid protein was performed in the primary motor, anterior cingulate and visual cortices. Magnetic susceptibility and R2* values in primary motor cortex were higher in ALS compared to control brains. Magnetic susceptibility and R2* showed positive correlations with both myelin and ferritin estimates from histology. Four out of nine ALS brains exhibited clearly visible hyperintense susceptibility and R2* values in the primary motor cortex. Our results demonstrate the potential for MRI-histology studies in whole, fixed post-mortem brains to investigate the biophysical source of susceptibility weighted MRI signals in neurodegenerative diseases like ALS.

neuroscience↗

bioRxiv2020

A semi-automated approach to dense segmentation of 3D white matter electron microscopy

PurposeNeuroscience methods working on widely different scales can complement and inform each other. At the macroscopic scale, magnetic resonance imaging methods that estimate microstructural measures have much to gain from ground truth validation and models based on accurate measurement of that microstructure. We present an approach to generate rich and accurate geometric models of white matter microstructure through dense segmentation of 3D electron microscopy (EM). MethodsVolumetric data of the white matter of the genu of the corpus callosum of the adult mouse brain were acquired using serial blockface scanning electron microscopy (SBF-SEM). A segmentation pipeline was developed to separate the 3D EM data into compartments and individual cellular and subcellular constituents, making use of established tools as well as newly developed algorithms to achieve accurate segmentation of various compartments. ResultsThe volume was segmented into six compartments comprising myelinated axons (axon, myelin sheath, nodes of Ranvier), oligodendrocytes, blood vessels, mitochondria, and unmyelinated axons. The myelinated axons had an average inner diameter of 0.56 m and an average outer diameter of 0.87 m. The diameter of unmyelinated axons was 0.43 m. A mean g-ratio of 0.61 was found for myelinated axons, but the g-ratio was highly variable between as well as within axons. ConclusionThe approach for segmentation of 3D EM data yielded a dense annotation of a range of white matter compartments that can be interrogated for their properties and used for in silico experiments of brain structure. We provide the resulting dense annotation as a resource to the neuroscience community.

neuroscience↗

bioRxiv2020

Confound modelling in UK Biobank brain imaging

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and nonimaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions) that researchers may wish to consider for their studies using such data. We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

neuroscience↗

bioRxiv2019

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↗

bioRxiv2019

Brain aging comprises multiple modes of structural and functional change with distinct genetic and biophysical associations

Brain imaging can be used to study how individuals brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single "brain age" is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

neuroscience↗

bioRxiv2019

Improved Statistical Efficiency of Simultaneous Multi-Slice fMRI by Reconstruction with Spatially Adaptive Temporal Smoothing

We introduce an approach to reconstruction of simultaneous multi-slice (SMS)-fMRI data that improves statistical efficiency. The method incorporates regularization to adjust temporal smoothness in a spatially varying, encoding-dependent manner, reducing the g-factor noise amplification per temporal degree of freedom. This results in a net improvement in tSNR and GLM efficiency, where the efficiency gain can be derived analytically as a function of the encoding and reconstruction parameters. Residual slice leakage and aliasing is limited when fMRI signal energy is dominated by low frequencies. Analytical predictions, simulated and experimental results demonstrate a marked improvement in statistical efficiency in the temporally regularized reconstructions compared to conventional slice-GRAPPA reconstructions, particularly in central brain regions. Furthermore, experimental results confirm that residual slice leakage and aliasing errors are not noticeably increased compared to slice-GRAPPA reconstruction. This approach to temporally regularized image reconstruction in SMS-fMRI improves statistical power, and allows for explicit choice of reconstruction parameters by directly assessing their impact on noise variance per degree of freedom.

neuroscience↗

bioRxiv2019

Joint modelling of diffusion MRI and microscopy

The combination of diffusion MRI with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate diffusion MRI microstructure models, addressing the indirect nature of dMRI signals. Typically, these modalities are analysed separately, and microscopy is taken as a gold standard against which dMRI-derived parameters are validated. Here we propose an alternative approach in which we combine diffusion MRI and microscopy data obtained from the same tissue sample to drive a single, joint model. This simultaneous analysis allows us to take advantage of the breadth of information provided by complementary data acquired from different modalities. By applying this framework to a spherical-deconvolution analysis, we are able to overcome a known degeneracy between fibre dispersion and radial diffusion. Spherical-deconvolution based approaches typically estimate a global fibre response function to determine the fibre orientation distribution in each voxel. However, the assumption of a brain-wide fibre response function may be challenged if the diffusion characteristics of white matter vary across the brain. Using a generative joint dMRI-histology model, we demonstrate that the fibre response function is dependent on local anatomy, and that current spherical-deconvolution based models may be overestimating dispersion and underestimating the number of distinct fibre populations per voxel.

neuroscience↗

bioRxiv2019

Estimation of Brain Age Delta from Brain Imaging

It is of increasing interest to study "brain age" - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the "delta") is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank.

neuroscience↗

bioRxiv2019

White matter changes in the perforant path in patients with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive and incurable motor neuron disease. Some ALS patients are affected by a level of cognitive or behavioural decline that meets the criteria for frontotemporal dementia (FTD). ALS and FTD share genetic and pathological features; for example, the deposition of phosphorylated 43 kDa TAR DNA-binding protein (pTDP-43) in the brain. Spreading of pTDP-43 pathology in ALS towards brain areas that connect via the Papez circuit is a possible indicator of progression towards FTD. For example, pTDP-43 aggregates in the granule cells of the hippocampus correlate well with clinically manifest FTD. Here, we test the hypothesis that white matter degeneration of the perforant path - as part of the Papez circuit - in the hippocampus is a feature of ALS, even in the absence of fully developed FTD or deposition of pTDP-43 inclusions in hippocampal granule cells. We used diffusion MRI (dMRI), polarized light imaging (PLI) and immunohistochemical analysis of hippocampus sections from controls (n=5) and ALS patients (n=14) to perform an in-depth study of white matter in the perforant path. The dMRI results show a significant decrease in fractional anisotropy (p=0.01) and an increase in mean diffusivity (p=0.01), axial diffusivity (p=0.03) and radial diffusivity (p=0.03) in the perforant path in ALS patients compared to controls, possibly indicating a loss of white matter fibres. Myelin density (measured with PLI retardance) was lower in ALS patients compared to controls (p=0.05) and correlated with dMRI fractional anisotropy (r=0.52, p=0.03). The dMRI and PLI results were confirmed by the immunohistochemistry; both myelin (proteolipid protein, p=0.03) and neurofilaments (SMI-312, p=0.02) were lower in ALS patients. The activated microglial (CD68) density was similar in ALS and controls. Only two out of the fourteen ALS cases showed pTDP-43 pathology in the dentate gyrus; however, while these two ALS-FTD cases showed reduced myelination in the perforant path, the values were comparable to other ALS cases. We conclude that degeneration of the perforant path occurs in ALS patients and that this may occur before, or independent of, pTDP-43 aggregation in the dentate gyrus of the hippocampus. Future research should focus on correlating the degree of clinically observed cognitive decline to the amount of white matter atrophy in the perforant path.

neuroscience↗

bioRxiv2018

Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER)

PurposeImage acceleration provides multiple benefits to diffusion MRI (dMRI), with in-plane acceleration reducing distortion and slice-wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill-conditioned, particularly when using both in-plane acceleration and simultaneous multi-slice (SMS) imaging. In this work, we develop a novel reconstruction method for in-vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve.\n\nTheory and MethodsWe propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q-space using Gaussian processes, as has previously been exploited in post-reconstruction analysis. We demonstrate in-plane acceleration exceeding the theoretical parallel imaging limits, and SMS combined with in-plane acceleration at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper-parameters, with no need for manual tuning.\n\nResultsSimulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences.\n\nConclusionThe proposed method provides improvements over existing methods for diffusion acceleration, particularly for high SMS acceleration with in-plane undersampling.

neuroscience↗