Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)

Stolp, Helen B.; Ball, Gareth; So, Po‐Wah; Tournier, Jacques‐Donald; Jones, Marilyn M.; Thornton, Claire; Edwards, A. David

doi:10.1038/s41598-018-22295-9

Cited by 55 publications

(71 citation statements)

References 46 publications

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“…The present study focused on inferring maps of key cellular structures in the mouse brain from MRI signals. Previous works on this problem include: new MRI contrasts that capture specific aspects of cellular structures of interest [21][22][23][24] ; carefully constructed tissue models for MR signals [25][26][27][28] ; statistical methods to extract relevant information from multi-contrast MRI 8 ; and techniques to register histology and MRI data [29][30][31] to produce ground truth for validation [32][33][34] . Here, we built on these efforts by demonstrating that deep learning networks trained by co-registered histological and MRI data can improve our ability to detect target cellular structures.…”

Section: Discussionmentioning

confidence: 99%

Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning

Liang

Lee

Arefin

et al. 2020

Preprint

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words):1 H MRI maps brain anatomy and pathology non-invasively through contrasts generated by exploiting inhomogeneities in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to the absence of direct links between MRI signals and specific tissue compartments. Here, we show that convolutional neural networks, developed using coregistered multi-contrast MRI and histological data of the mouse brain, can generate virtual histology from MRI results. Our networks provide maps that mirror histological stains for axons and myelin with enhanced specificity compared to existing MRI markers. Furthermore, by introducing random perturbations to the inputs, the relative contribution of each MRI contrast within the networks can be estimated and guide the optimization of MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for developing novel MRI contrasts. Introduction:Magnetic resonance imaging (MRI) is one of a few techniques that can image the brain non-invasively and without ionizing radiation. This advantage is further augmented by a large and still increasing array of versatile tissue contrasts (e.g., diffusion 1 , magnetization transfer 2 , and manganese enhanced MRI 3 ).While the rich tissue contrasts provide unparalleled insights into brain structure and function at the macroscopic level 4 , inferring the spatial organization of microscopic structures (e.g. axons and myelin) and their integrity in the brain based on MRI findings remains an ill-posed inverse problem. Aside from the resolution differences, most MRI contrasts are not tied to specific cellular structures and therefore susceptible to confounding factors, especially under pathological conditions. As a result, even though MRI has been widely used to detect certain neuropathology (e.g. ischemic stroke and demyelination), uncertainty arises when the exact pathological events and their severities need to be determined 5-7 . The lack of specificity hinders the direct translation of MRI findings into histopathology and limits its diagnostic value.Tremendous efforts have been devoted to discover new mechanisms to amplify the affinity of MRI signals to unique physical and chemical properties of target cellular structures and, by doing so, generate new contrasts with improved sensitivity and specificity. Recent progress in multi-modal MRI promises enhanced specificity by integrating multiple MRI contrasts that target distinct aspects of a particular

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Section: Discussionmentioning

confidence: 99%

Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning

Liang

Lee

Arefin

et al. 2020

Preprint

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“…Several studies have proposed combined MRI-histology approaches for the prostate 36,37 , lymphoidstructures 38 , mammary glands 39 , and kidneys 40 . Another comparable application is the study of small animal brains, in particular rodents [41][42][43] and small monkeys 44,45 .…”

Section: Related Workmentioning

confidence: 99%

A multimodal computational pipeline for 3D histology of the human brain

Mancini

Casamitjana

Peter

et al. 2020

Sci Rep

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Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of whole human hemispheres from two donors. Motivation. One of the major challenges in the quest to understand the human brain as a complex system is characterizing its multiscale organization. From a macroscopic anatomical perspective, the brain is subdivided into distinct structures and presents well-defined landmarks, for instance its gyri and sulci. At a much finer scale, neurons are interconnected to form complex circuits. These circuits give rise to features at coarser scales, e.g., the laminar organization of the cortex 1. Although these distinct levels of organization may seem separate, they are, in fact, deeply linked and mutually influential. One clear example is given by Alzheimer's disease, which can be described by the interactions between proteinopathy at the microscale and distributed, network-level disruptions at the macroscale 2. Understanding these multi-scale mechanisms requires a multi-scale map of the brain. However, the current concept of brain mapping is closely linked to the specific tool used to construct cartographic representations, and thus to the spatial scale of the tool. As a result, the different organizational principles can only be observed in a scale-specific fashion with dedicated tools 3. Multi-scale imaging of the human brain is therefore necessarily multimodal, as different modalities are needed to study different scales. While a macroscopic anatomical picture of the brain is easily acquired non invasively or ex vivo with magnetic resonance imaging (MRI), finer characterization requires histological procedures and microscopy. Histology and MRI are highly complementary modalities: histology produces excellent contrast at the microscopic scale using dedicated stains that target different microanatomical or cytoarchitectural features, but it is a 2D modality that also inevitably introduces distortions in the tissue during blocking and sectioning. MRI does not yield microscopic resolution, but produces undistorted 3D volumes. Therefore, the combination of these two modalities offers a solution to the problem of imaging the human brain at high resolution in 3D.

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“…Once acquired, the tissue was sectioned and stained with fluorescent dil and imaged on a LSM710 Confocal microscope following procedures outlined in [24]. A similar procedure is outlined by [15]. The histological fiber orientation distribution (HFOD) was extracted using 3D structure tensor analysis.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Inter-Scanner Harmonization of High Angular Resolution DW-MRI Using Null Space Deep Learning

Nath

Parvathaneni

Hansen

et al. 2019

Mathematics and Visualization

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Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical approaches have been proposed to detect both single (e.g., tensors) and multiple (e.g., constrained spherical deconvolution, CSD) fiber population orientations per voxel. However, existing techniques generally exhibit low reproducibility across MRI scanners. Herein, we propose a data-driven technique using a neural network design which exploits two categories of data. First, training data were acquired on three squirrel monkey brains using ex-vivo DW-MRI and histology of the brain. Second, repeated scans of human subjects were acquired on two different scanners to augment the learning of the network proposed. To use these data, we propose a new network architecture, the null space deep network (NSDN), to simultaneously learn on traditional observed/truth pairs (e.g., MRI-histology voxels) along with repeated observations without a known truth (e.g., scan-rescan MRI). The NSDN was tested on twenty percent of the histology voxels that were kept completely blind to the network. NSDN significantly improved absolute performance relative to histology by 3.87% over CSD and 1.42% over a recently proposed deep neural network approach. Moreover, it improved reproducibility on the paired data by 21.19% over CSD and 10.09% over a recently proposed deep approach. Finally, NSDN improved generalizability of the model to a third in vivo human scanner (which was not used in training) by 16.08% over CSD and 10.41% over a recently proposed deep learning approach. This work suggests that data-driven approaches for local fiber reconstruction are more reproducible, informative and precise and offers a novel, practical method for determining these models.

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Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)

Cited by 55 publications

References 46 publications

Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning

Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning

A multimodal computational pipeline for 3D histology of the human brain

Inter-Scanner Harmonization of High Angular Resolution DW-MRI Using Null Space Deep Learning

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