Automated Determination of Axonal Orientation in the Deep White Matter of the Human Brain

Bartsch, Hauke; Maechler, Paul

doi:10.1089/brain.2012.0096

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…Microscopy techniques to image the brain in three-dimensions have been demonstrated for small tissue samples, such as rodent brains (Dodt et al, 2007; Keller and Dodt, 2011; Mikula et al, 2012). Large-scale application to the human brain is challenging, but not impossible (Bartsch et al, 2012). DTI and other models of characterizing brain structure with diffusion MRI have shown tremendous potential to study the normal and diseased microscopic anatomy.…”

Section: Discussionmentioning

confidence: 99%

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Quantification of anisotropy and fiber orientation in human brain histological sections

Budde¹,

Annese²

2013

Front. Integr. Neurosci.

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137

119

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Diffusion weighted imaging (DWI) has provided unparalleled insight into the microscopic structure and organization of the central nervous system. Diffusion tensor imaging (DTI) and other models of the diffusion MRI signal extract microstructural properties of tissues with relevance to the normal and injured brain. Despite the prevalence of such techniques and applications, accurate and large-scale validation has proven difficult, particularly in the human brain. In this report, human brain sections obtained from a digital public brain bank were employed to quantify anisotropy and fiber orientation using structure tensor analysis. The derived maps depict the intricate complexity of white matter fibers at a resolution not attainable with current DWI experiments. Moreover, the effects of multiple fiber bundles (i.e., crossing fibers) and intravoxel fiber dispersion were demonstrated. Examination of the cortex and hippocampal regions validated-specific features of previous in vivo and ex vivo DTI studies of the human brain. Despite the limitation to two dimensions, the resulting images provide a unique depiction of white matter organization at resolutions currently unattainable with DWI. The method of analysis may be used to validate tissue properties derived from DTI and alternative models of the diffusion signal.

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Section: Discussionmentioning

confidence: 99%

“…Accordingly, sections were obtained approximately perpendicular to a line intersecting the anterior and posterior commissures (Annese, 2012). Further details of tissue processing, including sectioning, staining, and digitization are provided in Bartsch et al (2012). …”

Section: Methodsmentioning

confidence: 99%

Quantification of anisotropy and fiber orientation in human brain histological sections

Budde¹,

Annese²

2013

Front. Integr. Neurosci.

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137

119

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“…There are several different approaches to calculate orientedness in digital images, including integral transformations like Fourier or Wavelet decomposition (Kemao and Asundi, 2002 ; Lefebvre et al, 2011 ), oriented filters (Michelet et al, 2007 ), or template matching (Bartsch et al, 2012 ). Because of its recent success, we chose the structure tensor approach (Bigün and Granlund, 1987 ), which is based on local gray level gradients.…”

Section: Methodsmentioning

confidence: 99%

Histological validation of high-resolution DTI in human post mortem tissue

et al. 2015

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Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.

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“…Examples of published techniques seeking to validate diffusion MRI with histology include those using 2D structure tensors [19, 20, 21], Fourier analysis [22], wavelet decomposition [23], oriented filtering [24], micrographs [25], and template matching [26]. While the resolutions achievable with DTI are much lower than those in images of histological sections, and DTI inherently provides three-dimensional information, the extraction of two-dimensional directionality of white matter tracts from images of histological slices is nevertheless an important step in a direct comparison of DTI data to the underlying white matter architecture.…”

Section: Introductionmentioning

confidence: 99%

Methodology for computing white matter nerve fiber orientation in human histological slices

Nazaran

Wisco

Hageman

et al. 2016

Journal of Neuroscience Methods

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Background The gold standard for mapping nerve fiber orientation in white matter of the human brain is histological analysis through biopsy. Such mappings are a crucial step in validating non-invasive techniques for assessing nerve fiber orientation in the human brain by using diffusion MRI. However, the manual extraction of nerve fiber directions of histological slices is tedious, time consuming, and prone to human error. New Method The presented semi-automated algorithm first creates a binary-segmented mask of the nerve fibers in the histological image, and then extracts an estimate of average directionality of nerve fibers through a Fourier-domain analysis of the masked image. It also generates an uncertainty level for its estimate of average directionality. Results and Comparison with Existing Methods The average orientations of the semi-automatic method were first compared to a qualitative expert opinion based on visual inspection of nerve fibers. A weighted RMS difference between the expert estimate and the algorithmically-determined angle (weighted by expert's confidence in his estimate) was 15.4 degrees, dropping to 9.9 degrees when only cases with an expert confidence level of greater than 50% were included. The algorithmically-determined angles were then compared with angles extracted using a manual segmentation technique, yielding an RMS difference of 11.2 degrees. Conclusion The presented semi-automated method is in good agreement with both qualitative and quantitative manual expert-based approaches for estimating directionality of nerve fibers in white matter from images of stained histological slices of the human brain.

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Automated Determination of Axonal Orientation in the Deep White Matter of the Human Brain

Cited by 7 publications

References 26 publications

Quantification of anisotropy and fiber orientation in human brain histological sections

Quantification of anisotropy and fiber orientation in human brain histological sections

Histological validation of high-resolution DTI in human post mortem tissue

Methodology for computing white matter nerve fiber orientation in human histological slices

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