The Intramural Research Program of NINDS supported this study. We thank the NIH Functional Magnetic Resonance Imaging Facility. We thank Jennifer Lefeuvre and Cecil Chern-Chyi Yen for assistance with postmortem MRI acquisition. We thank John Ostuni and the Section on Instrumentation Core Facility for assistance with 3D printing. Figure 1 of this work used snapshots from MeshLab, a tool developed with the support of the 3D-CoForm project.

<ol><li>Evans, A. C., Frank, J. A., Antel, J., Miller, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+MRI+in+clinical+trials+of+multiple+sclerosis%3A+comparison+of+image+processing+techniques%5BTitle%5D)+AND+%22Ann+Neurol%22%5BJournal%5D)">The role of MRI in clinical trials of multiple sclerosis: comparison of image processing techniques.</a> Ann Neurol. 41 (1), 125-132 (1997).</li>
<li>'t Hart, B. A., van Kooyk, Y., Geurts, J. J. G., Gran, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+primate+autoimmune+encephalomyelitis+model%3B+a+bridge+between+mouse+and+man%5BTitle%5D)+AND+%22Ann+Clin+Transl+Neurol%22%5BJournal%5D)">The primate autoimmune encephalomyelitis model; a bridge between mouse and man.</a> Ann Clin Transl Neurol. 2 (5), 581-593 (2015).</li>
<li>Ontaneda, D., Hyland, M., Cohen, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multiple+sclerosis%3A+new+insights+in+pathogenesis+and+novel+therapeutics%5BTitle%5D)+AND+%22Annu+Rev+Med%22%5BJournal%5D)">Multiple sclerosis: new insights in pathogenesis and novel therapeutics.</a> Annu Rev Med. 63, 389-404 (2012).</li>
<li>Guy, J. R., Sati, P., Leibovitch, E., Jacobson, S., Silva, A. C., Reich, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Custom+fit+3D-printed+brain+holders+for+comparison+of+histology+with+MRI+in+marmosets%5BTitle%5D)+AND+%22J+Neurosci+Methods%22%5BJournal%5D)">Custom fit 3D-printed brain holders for comparison of histology with MRI in marmosets.</a> J Neurosci Methods. 257, 55-63 (2016).</li>
<li>Breen, M. S., Lazebnik, R. S., Wilson, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Three-dimensional+registration+of+magnetic+resonance+image+data+to+histological+sections+with+model-based+evaluation%5BTitle%5D)+AND+%22Ann+Biomed+Eng%22%5BJournal%5D)">Three-dimensional registration of magnetic resonance image data to histological sections with model-based evaluation.</a> Ann Biomed Eng. 33 (8), 1100-1112 (2005).</li>
<li>Dauguet, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Three-dimensional+reconstruction+of+stained+histological+slices+and+3D+non-linear+registration+with+in-vivo+MRI+for+whole+baboon+brain%5BTitle%5D)+AND+%22J+Neurosci+Methods%22%5BJournal%5D)">Three-dimensional reconstruction of stained histological slices and 3D non-linear registration with in-vivo MRI for whole baboon brain.</a> J Neurosci Methods. 164 (1), 191-204 (2007).</li>
<li>McGrath, D. M., Vlad, R. M., Foltz, W. D., Brock, K. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Technical+note%3A+fiducial+markers+for+correlation+of+whole-specimen+histopathology+with+MR+imaging+at+7+tesla%5BTitle%5D)+AND+%22Med+Phys%22%5BJournal%5D)">Technical note: fiducial markers for correlation of whole-specimen histopathology with MR imaging at 7 tesla.</a> Med Phys. 37, 2321-2328 (2010).</li>
<li>Schormann, T., Zilles, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Three-Dimensional+linear+and+nonlinear+transformations%3A+An+integration+of+light+microscopical+and+MRI+data%5BTitle%5D)+AND+%22Hum+Brain+Mapp%22%5BJournal%5D)">Three-Dimensional linear and nonlinear transformations: An integration of light microscopical and MRI data.</a> Hum Brain Mapp. 6, 339-347 (1998).</li>
<li>Jiang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Combined+MR%2C+fluorescence+and+histology+imaging+strategy+in+a+human+breast+tumor+xenograft+model%5BTitle%5D)+AND+%22NMR+Biomed%22%5BJournal%5D)">Combined MR, fluorescence and histology imaging strategy in a human breast tumor xenograft model.</a> NMR Biomed. 26 (3), 285-298 (2013).</li>
<li>Absinta, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Postmortem+magnetic+resonance+imaging+to+guide+the+pathologic+cut%3A+individualized%2C+3-dimensionally+printed+cutting+boxes+for+fixed+brains%5BTitle%5D)+AND+%22J+Neuropathol+Exp+Neurol%22%5BJournal%5D)">Postmortem magnetic resonance imaging to guide the pathologic cut: individualized, 3-dimensionally printed cutting boxes for fixed brains.</a> J Neuropathol Exp Neurol. 73 (8), 780-788 (2014).</li>
<li>Gaitán, M. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Perivenular+brain+lesions+in+a+primate+multiple+sclerosis+model+at+7-tesla+magnetic+resonance+imaging%5BTitle%5D)+AND+%22Mult+Scler%22%5BJournal%5D)">Perivenular brain lesions in a primate multiple sclerosis model at 7-tesla magnetic resonance imaging.</a> Mult Scler. 20 (1), 64-71 (2014).</li>
</ol>

The authors declare that they have no competing financial interests.

The protocol outlined here enables an accurate comparison between MRI and histology sections. The protocol is presented in a unified format that can be applied to brains of humans or small animals, such as marmosets or rodents. Differences specific to large (human) and small (nonhuman primate and rodent) brains are highlighted, and in the accompanying video and figures we demonstrate the application in the marmoset. Although the approach is straightforward, the method requires many steps as well as the use of several types of software. Moreover, several issues potentially affecting the accuracy of this method are important to mention.
The image quality of the in vivo MRI is an important factor. To minimize the disparity in image resolution between MRI and digitalized histology images, the smallest possible MRI voxel size should be used. This concept also applies to the image quality of the postmortem MRI. While the increased acquisition time in postmortem MRI allows much higher image resolution, the preparation can introduce image artifacts such as focal signal dropouts related to air bubbles. These artifacts can obscure areas of the tissue as well as affect its contour. Moreover, the dimensions of the tissue on the postmortem MRI are likely to be affected by the fixation process and duration. While the in vivo to ex vivo MRI match can be closely approximated by utilizing anatomical landmarks in slice geometry setup during acquisition, a non-linear registration would still be necessary to reach a higher degree of accuracy in matching those two MRI images.
The design of the brain holder and slicer is also a crucial step. In creating the digital model of the brain, a smoothing algorithm is applied that slightly enlarges the model relative to the fixed brain. This enables easy insertion of the brain into its holder and slicer and reduces sharp edges in the holder&#39;s contour. However, if the model is too large (e.g., by more than 5%), the brain might move during the postmortem MRI and/or the sectioning. Another important point is to adapt the design of the brain model so that the cerebellum is properly placed inside the 3D printed object. This can be particularly challenging when the cerebellum has been damaged during the brain extraction at autopsy.
When printing the brain slicer and holder, the type of 3D printer must also be chosen carefully. Some multi-jet printers require post-processing using an oven to remove support material. While these printers can produce objects that are watertight and relatively more durable than desktop fused deposition modeling (FDM) printers, the heating process to remove supports can slightly warp the box, creating blade gaps that are not perfectly perpendicular to the brain contour.
The brain sectioning process is another crucial step. Before cutting the whole brain into slabs, it is important to make sure that the brain is sitting tightly inside the brain slicer: there should be no motion when slight pressure is applied onto the brain. This will make it possible for the blades to cut through the brain at the precise location set by the investigators. A continuous, balanced pressure should be applied to both blade holders when cutting. Depending on the sharpness of the blades and the rigidity of the tissue, a slight transverse cutting motion could be advantageous for maintaining flat cut surfaces.
The paraffin-embedding process can also be a source of misalignment between MRI and histology. If the tissue slab is not sitting flat against the cassette during the embedding process, there will be a tilt between the cutting plane of the microtome and the surface place of the slab. This will require cutting unusable sections to find a flat plane in which all the tissue is exposed. One way to correct for the tilt is by changing the angle of the viewing plane on the high-isotropic-resolution postmortem MRI. However, this is nearly impossible to perform on the in vivo MRI that is usually acquired with anisotropic resolution (typically thick coronal slices).
Finally, the tissue can experience some deformation during the formalin fixation period and paraffin embedding (shrinkage), as well as during the preparation of slides (folding, cracking, wrinkles). Some of these deformations can be corrected by putting the 4-5 &#956;m sections in a water bath before transferring onto slides. Other deformations can be partially solved by performing deformable image coregistration of the histological digitized images to the postmortem MRI images. Nevertheless, minimizing the deformations with careful and skilled practice is the most effective approach to matching MRI volumes to histology sections.
In conclusion, the methodology introduced here enables investigators to accurately assess the underlying pathology of MRI findings. More generally, it is a promising approach for identifying and/or validating novel MRI biomarkers for research studies that target specific pathological processes, such as inflammation or remyelination.

In vivo MRI provides a noninvasive and sensitive measure of tissue integrity at the macroscopic level. Changes in MRI signal intensity seen in vivo are outcome measures in many ongoing clinical trials.1 While the intensity changes seen via MRI can identify areas of abnormality in the context of the whole brain, they are often not sufficiently specific to differentiate pathological processes. This is especially true of dynamic processes involving multiple pathologies. For example, in multiple sclerosis (MS) or its animal model, experimental autoimmune encephalomyelitis (EAE), inflammation, edema, myelin degradation, axonal destruction, gliosis, and neuronal death overlap. 2, 3 To obtain the necessary specificity regarding the underlying pathology, context must be taken into account, together with knowledge of the histology of the MRI-identified abnormal tissues.
However, even in well-controlled animal experiments, matching histology with in vivo MRI is fundamentally challenging for various reasons. First, the difference in dimensional scales between histology sections and MRI is of several orders of magnitude.4 Second, for proper comparison, the orientation of MRI slice plane must match the sectioning plane of the brain tissue when cut. Due to the shape of the brain, it is very difficult to make consistently straight and accurate cuts when the brain is sitting on a flat surface. Third, the large size of the brain relative to a potentially small area of interest (lesion, tumor, etc.) creates a "needle-in-a-haystack" scenario for the pathologist processing the tissue. Fourth, even when the target tissue is found, it is commonly processed in such a way as to render virtually impossible an association with the original MRI data. Finally, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, further complicating the comparison between histology sections and MRI images. 
 Previous attempts to overcome these challenges relied on the use of deformational algorithms to coregister the data and/or placement of fiducial markers within or around the tissue as a reference.5, 6, 7, 8 The former approach requires complex computational models that are particularly susceptible to complications due to data formatting, imaging artifacts, and changes caused by tissue processing.4 On the other hand, the latter approach introduces the possibility of contaminating or otherwise harming the tissue itself.9
The approach described here improves the transition between modalities through the use of postmortem MRI to bridge the gap between in vivo MRI and histology. Postmortem MRI provides three-dimensional (3D) images of the brain at higher resolution than can be achieved in vivo and furthermore provides the data needed for producing a morphologically accurate model of the brain surface. This digital model can then be used to create a 3D-printed custom holder for the brain. With careful positioning, the brain holder allows for precise, MRI-oriented brain sectioning, reducing the need for complex mathematical algorithms, and enables a focus on specific regions for targeted sampling. 
Our laboratory recently introduced new methods for creating custom brain holders and slicers using postmortem MRI and 3D-printing technology for human10 and marmoset brains.4 The two methods allow for a more accurate correlation between MRI and histology in a research setting, and ultimately allow a deeper understanding of the specific pathology underlying MRI abnormalities. Carefully designed experiments, in which the brain is sampled repeatedly over time in vivo, can provide context for interpretation of the pathology, which in turn can add specificity to interpretation of the MRI. Here, we present a modified protocol in a unified framework that can be applied to any brain tissue, whether it derive from nonhuman primates, rodents, or humans. We provide detailed instructions, and a corresponding video, for the marmoset sectioning. Although the overall protocol applies to any type of brain, due to differences in MRI acquisition and tissue size, as well as the challenges encountered when dealing with specific brain types, there are some differences in the approach depending on the type of brain being processed. In this presentation, sections with "human" will denote differences in protocol specific to the human brain.

<table><tbody><tr><td>7T/30cm USR AVIII Bruker MRI</td><td>Bruker Biospin</td><td></td><td></td></tr><tr><td>38 mm Bruker Biospin volume coil</td><td>Bruker Biospin</td><td></td><td></td></tr><tr><td>Fomblin</td><td>Solvay Solexis</td><td></td><td></td></tr><tr><td>50 ml Falcon Centrifuge Tubes, Polypropylene, Sterile</td><td>Corning</td><td>21008-951</td><td></td></tr><tr><td>Fisherbrand Gauze Sponges</td><td>Fisher Scientrific</td><td>13-761-52</td><td></td></tr><tr><td>Parafilm M All-Purpose Laboratory Film</td><td>Bemis</td><td></td><td></td></tr><tr><td>Leica RM2235 rotary microtome&amp;nbsp;</td><td>Leica</td><td></td><td></td></tr><tr><td>Leica Disposable Blades, low profile (819)</td><td>Leica</td><td></td><td></td></tr><tr><td>Cresyl Violet Acetate, 0.1% Aqueous</td><td>Electron Microscopy Sciences</td><td>26089-01</td><td></td></tr><tr><td>Luxol Fast Blue, 0.1% in 95% Alcohol</td><td>Electron Microscopy Sciences</td><td>26056-15</td><td></td></tr><tr><td>ETOH</td><td></td><td></td><td></td></tr><tr><td>Ultimaker 2 Extended&amp;nbsp;</td><td>Ultimaker</td><td></td><td></td></tr><tr><td>.75 kg Official Ultimaker Branded PLA&amp;nbsp; Filament, 2.85 mm, Silver Metallic</td><td>Ultimaker</td><td></td><td></td></tr><tr><td>Axio Observer.Z1</td><td>Zeiss</td><td></td><td></td></tr><tr><td>Zen 2 (Blue Edition)</td><td>Zeiss</td><td></td><td></td></tr><tr><td>Netfabb Professional 5.0.1</td><td>Netfabb</td><td>http://www.netfabb.com/professional.php</td><td></td></tr><tr><td>Meshmixer 10.9.332</td><td>Autodesk</td><td>http://www.meshmixer.com/download.html</td><td></td></tr><tr><td>Mipav 7.2</td><td>NIH CIT</td><td>http://mipav.cit.nih.edu</td><td></td></tr><tr><td>Cura</td><td>Ultimaker</td><td>https://ultimaker.com/en/products/cura-software</td><td></td></tr></tbody></table>

utilizing 3d printing technology to merge mri with histology: a protocol for brain sectioning

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume three-dimensionally. While MRI is an extremely sensitive tool for detecting tissue abnormalities, association of signal changes with an underlying pathological process is usually not straightforward. In the central nervous system, for example, inflammation, demyelination, axonal damage, gliosis, and neuronal death may all induce similar findings on MRI. As such, interpretation of MRI scans depends on the context, and radiological-histopathological correlation is therefore of the utmost importance. Unfortunately, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, thus complicating the comparison between histology sections and MRI. This article presents novel methodology for accurately sectioning primate brain tissues and thus allowing precise matching between histology and MRI. The detailed protocol described in this article will assist investigators in applying this method, which relies on the creation of 3D printed brain slicers. Slightly modified, it can be easily implemented for brains of other species, including humans.

The workflow of this method is summarized in Figure 1. Once the brain is sliced, a visual comparison between the MR images and pictures of the superficial surfaces of the slabs shows a good orientation match across multiple slabs (Figure 2). After the slabs are embedded in paraffin, they are sectioned on a microtome and stained. A more thorough comparison between the high resolution postmortem MRI and the stained histology sections demonstrates an accurate and consistent match across all the structures of the marmoset brain (Figure 3).
In this animal model of MS, the animals develop white matter lesions spread throughout the cerebral white matter. These lesions can be detected noninvasively by performing MRI. Figure 4 demonstrates the ability of this technique to elucidate the pathological substrate of the MRI findings. Small lesions detected on in vivo MRI can be tracked on both postmortem MRI and histology. As shown in the insets, demyelination within the lesions is one of the main components driving the MR signal change (hyperintensity compared to surrounding tissue). The histology and postmortem MRI can also show lesions missed on in vivo MRI (Figure 4).
<img alt="Figure 1" src="/files/ftp_upload/54780/54780fig1.jpg" /> 
Figure 1. Workflow for creating a marmoset brain slicer box. The brain is fixed with formalin (A1) and a T2-weighted MRI is acquired with isotropic voxels of 150 &#181;m per edge (A2). Images are processed and thresholded to create a binary mask (A3). The surface is then rendered in 3D modeling software (A4). A Boolean subtraction between a slicer template and the brain model creates a digital model of the brain slicer (B1). The brain slicer box is printed on a 3D printer (B2). The brain is then placed firmly in the slicer box for cutting (B3). <a href="http://cloudflare.jove.com/files/ftp_upload/54780/54780fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54780/54780fig2.jpg" /> 
Figure 2. From left to right: In vivo MRI, postmortem MRI, and tissue slab photograph. Slicing planes were established based on the postmortem MRI (B) and visually compared to the corresponding in vivo MRI slice (A). The brain was then cut, and the resulting slabs were found to be consistent (C). <a href="http://cloudflare.jove.com/files/ftp_upload/54780/54780fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54780/54780fig3.jpg" /> 
Figure 3. High-resolution postmortem MRI and histology section matching. Slabs were embedded in paraffin, cut with a microtome into 4 &#181;m sections, and stained with fast blue and cresyl violet (B). The sections were then visually matched with the 100 &#181;m T2*-weighted MRI based on brain structures (A). Details for acquiring this image are in the supplementary section of the protocol and Table 1. Brain structures: (1) red arrow = internal capsule, blue arrow = anterior commissure; (2) red arrow = putamen, blue arrow = optic tract; (3) red arrow = caudate, blue arrow = hippocampus; (4) red arrow = corpus callosum, blue arrow = cerebral aqueduct; (5) red arrow = inferior colliculus, blue arrow = pyramidal tract. The dashed box in B1 indicates a slice where, either during brain cutting or paraffin embedding, an error caused a slight rotation about the Y axis, leading to mismatch of the anterior commissure on the left. <a href="http://cloudflare.jove.com/files/ftp_upload/54780/54780fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54780/54780fig4.jpg" /> 
Figure 4. Tracking lesions from in vivo MRI to histology section. The in vivo MRI showed no convincing evidence of abnormal hyperintensity signal to suggest lesions in either optic tract (A1). However, the high resolution postmortem MRI shows clear hyper intense lines in both optic tracts (A2). The fast blue/cresyl violet stain of a 4 &#181;m histology section shows that the hyperintense areas seen on the ex vivo MRI are demyelinated (A3). In the cerebral white matter, the in vivo MRI shows subtle hyperintensity bilaterally (B1, enlarged in the insets). The hyperintense areas are more obvious on the high resolution postmortem MRI (B2). The LFB stain of a 4 &#181;m histology section shows that these areas are demyelinated (B3). After comparison with the baseline in vivo MRI and a hemotoxylin-and-eosin stain, the right side was determined to be an anatomical abnormality, not a demyelinated lesion. <a href="http://cloudflare.jove.com/files/ftp_upload/54780/54780fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental code files" src="/files/ftp_upload/54780/54780supplementalfiles.jpg" /> 
Supplemental code files. Brain_Slicer_Parts_Marmoset.stl: <a href="https://www.jove.com/files/ftp_upload/54780/Brain_Slicer_Parts_Marmoset.stl" target="_blank">Please click here to download this file.</a> Brain_Slicer_Parts_Human.stl: <a href="https://www.jove.com/files/ftp_upload/54780/Brain_Slicer_Parts_Human.stl" target="_blank">Please click here to download this file.</a> Cap_Insert.stl: <a href="https://www.jove.com/files/ftp_upload/54780/Cap_Insert.stl" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning at JoVE.com

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

The overall goal of this protocol is to accurately align magnetic resonance imaging (MRI) image volumes with histology sections via the creation of customized 3D-printed brain holders and slicer boxes.

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume ...

utilizing-3d-printing-technology-to-merge-mri-with-histology-protocol

Research

JoVE Journal

Neuroscience

15.0K Views.  National Institute of Neurological Disorders and Stroke.  De algemene doelstelling van dit protocol is om nauwkeurig af te stemmen magnetic resonance imaging (MRI) image volumes met histologie secties via de creatie van op maat gemaakte 3D geprint hersenen houders en snijmachine dozen. 

Video: Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional units of the ventricles are ciliated epithelial cells termed ependymal cells, which line the ventricles and through ciliary action are capable of generating laminar flow of CSF at the ventricle surface. This monolayer of ependymal cells also provides barrier and filtration functions that promote exchange between brain interstitial fluids (ISF) and circulating CSF. Biochemical changes in the brain are thereby reflected in the composition of the CSF and destruction of the ependyma can disrupt the delicate balance of CSF and ISF exchange. In humans there is a strong correlation between lateral ventricle expansion and aging. Age-associated ventriculomegaly can occur even in the absence of dementia or obstruction of CSF flow. The exact cause and progression of ventriculomegaly is often unknown; however, enlarged ventricles can show regional and, often, extensive loss of ependymal cell coverage with ventricle surface astrogliosis and associated periventricular edema replacing the functional ependymal cell monolayer. Using MRI scans together with postmortem human brain tissue, we describe how to prepare, image and compile 3D renderings of lateral ventricle volumes, calculate lateral ventricle volumes, and characterize periventricular tissue through immunohistochemical analysis of en face lateral ventricle wall tissue preparations. Corresponding analyses of mouse brain tissue are also presented supporting the use of mouse models as a means to evaluate changes to the lateral ventricles and periventricular tissue found in human aging and disease. Together, these protocols allow investigations into the cause and effect of ventriculomegaly and highlight techniques to study ventricular system health and its important barrier and filtration functions within the brain.

Using MRI scans (human), 3D imaging software, and immunohistological analysis, we document changes to the brain&#8217;s lateral ventricles. Longitudinal 3D mapping of lateral ventricle volume changes and characterization of periventricular cellular changes that occur in the human brain due to aging or disease are then modeled in mice.

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional ...

Modification of a Colliculo-thalamocortical Mouse Brain Slice, Incorporating 3-D printing of Chamber Components and Multi-scale Optical Imaging

The ability of the brain to process sensory information relies on both ascending and descending sets of projections. Until recently, the only way to study these two systems and how they interact has been with the use of in vivo preparations. Major advances have been made with acute brain slices containing the thalamocortical and cortico-thalamic pathways in the somatosensory, visual, and auditory systems. With key refinements to our recent modification of the auditory thalamocortical slice1, we are able to more reliably capture the projections between most of the major auditory midbrain and forebrain structures: the inferior colliculus (IC), medial geniculate body (MGB), thalamic reticular nucleus (TRN), and the auditory cortex (AC). With portions of all these connections retained, we are able to answer detailed questions that complement the questions that can be answered with in vivo preparations. The use of flavoprotein autofluorescence imaging enables us to rapidly assess connectivity in any given slice and guide the ensuing experiment. Using this slice in conjunction with recording and imaging techniques, we are now better equipped to understand how information processing occurs at each point in the auditory forebrain as information ascends to the cortex, and the impact of descending cortical modulation. 3-D printing to build slice chamber components permits double-sided perfusion and broad access to networks within the slice and maintains the widespread connections key to fully utilizing this preparation.

Using multiple angles to cut the mouse pup brain, we improve upon a previously-described acute brain slice which captures the connections between most of the major auditory midbrain and forebrain structures.

The ability of the brain to process sensory information relies on both ascending and descending sets of projections. Until recently, the only way to study ...

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using data extracted from magnetic resonance imaging (MRI). This protocol allows for the generation of 3D printed anatomically accurate physical models of the brain and skull, as well as an agarose gel model of the brain modeling some of the mechanical properties of the brain. These models can be extracted from MRI using brain extraction software for the model of the brain, and custom code for the model of the skull. The preparation protocol takes advantage of state-of-the-art 3D printing technology to make interfacing brains, skulls, and molds for gel brain models. The skull and brain models can be used to visualize brain tissue inside the skull with the addition of a craniotomy in the custom code, allowing for better preparation for surgeries directly involving the brain. The applications of these methods are designed for surgeries involved in neurological stimulation and recording as well as injection, but the versatility of the system allows for future expansion of the protocol, extraction techniques, and models to a wider scope of surgeries.

The method outlined below aims to provide a comprehensive protocol for the preparation of nonhuman primate (NHP) neurosurgery using a novel combination of three-dimensional (3D) printing methods and MRI data extraction.

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using ...

Bioengineering

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Phantoms are essential tools for clinical training, surgical planning and the development of novel medical devices. However, it is challenging to create anatomically accurate head phantoms with realistic brain imaging properties because standard fabrication methods are not optimized to replicate any patient-specific anatomical detail and 3D printing materials are not optimized for imaging properties. In order to test and validate a novel navigation system for use during brain tumor surgery, an anatomically accurate phantom with realistic imaging and mechanical properties was required. Therefore, a phantom was developed using real patient data as input and 3D printing of molds to fabricate a patient-specific head phantom comprising the skull, brain and tumor with both ultrasound and X-ray contrast. The phantom also had mechanical properties that allowed the phantom tissue to be manipulated in a similar manner to how human brain tissue is handled during surgery. The phantom was successfully tested during a surgical simulation in a virtual operating room.
The phantom fabrication method uses commercially available materials and is easy to reproduce. The 3D printing files can be readily shared, and the technique can be adapted to encompass many different types of tumor.

This protocol describes the fabrication of a patient specific skull, brain and tumor phantom. It uses 3D printing to create molds, and polyvinyl alcohol (PVA-c) is used as the tissue mimicking material.

Phantoms are essential tools for clinical training, surgical planning and the development of novel medical devices. However, it is challenging to create ...

A Micro-CT-based Method for Characterizing Lesions and Locating Electrodes in Small Animal Brains

Lesion and electrode location verification are traditionally done via histological examination of stained brain slices, a time-consuming procedure that requires manual estimation. Here, we describe a simple, straightforward method for quantifying lesions and locating electrodes in the brain that is less laborious and yields more detailed results. Whole brains are stained with osmium tetroxide, embedded in resin, and imaged with a micro-CT scanner. The scans result in 3D digital volumes of the brains with resolutions and virtual section thicknesses dependent on the sample size (12&#8211;15 and 5&#8211;6 &#181;m&#160;per voxel for rat and zebra finch brains, respectively). Surface and deep lesions can be characterized, and single tetrodes, tetrode arrays, electrolytic lesions, and silicon probes can also be localized. Free and proprietary software allows experimenters to examine the sample volume from any plane and segment the volume manually or automatically. Because this method generates whole brain volume, lesions and electrodes can be quantified to a much higher degree than in current methods, which will help standardize comparisons within and across studies.

This article describes a straightforward method to prepare small animal brains for micro-CT imaging, in which lesions can be quantified and electrodes located with high precision in the context of the whole brain.

Lesion and electrode location verification are traditionally done via histological examination of stained brain slices, a time-consuming procedure that ...

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and microscopic neuronal spikes, propagating along axons and dendrites. Both scales complement different aspects of human cognitive and behavioral functions. At the macroscopic level, MRI has been the current standard imaging technology, in which the smallest spatial resolution, voxel size, is 0.1&#8211;1 mm3. Also, at the microscopic level, previous physiological studies were aware of nonuniform neuronal architectures within such voxels. This study develops a powerful way to accurately embed microscopic data into a macroscopic map by interfacing biological scientific research with technological advancements in 3D scanning technology. Since 3D scanning technology has mostly been used for engineering and industrial design until now, it is repurposed for the first time to embed microconnectomes into the whole brain while preserving natural spiking in living brain cells. In order to achieve this purpose, first, we constructed a scanning protocol to obtain accurate 3D images from living bio-organisms inherently challenging to image due to moist and reflective surfaces. Second, we trained to keep speed to prevent the degradation of living brain tissue, which is a key factor in retaining better conditions and recording more natural neuronal spikes from active neurons in the brain tissue. Two cortical surface images, independently extracted from two different imaging modules, namely MRI and 3D scanner surface images, surprisingly show a distance error of only 50 &#956;m as mode value of the histogram. This accuracy is comparable in scale to the microscopic resolution of intercellular distances; also, it is stable among different individual mice. This new protocol, the 3D novel embedding overlapping (3D-NEO) protocol, bridges macroscopic and microscopic levels derived by this integrative protocol and accelerates new scientific findings to study comprehensive connectivity architectures (i.e., microconnectome).

This article introduces an experimental protocol using 3D scanning technology bridging two spatial scales: the macroscopic spatial scale of whole-brain anatomy imaged by MRI at &#62;100 &#956;m and the microscopic spatial scale of neuronal distributions using immunohistochemistry staining and a multielectrode array system and other methods (~10 &#956;m).

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and ...

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Three-dimensional printing allows for the production of highly detailed objects through a process known as additive manufacturing. Traditional, mold-injection methods to create models or parts have several limitations, the most important of which is a difficulty in making highly complex products in a timely, cost-effective manner.1 However, gradual improvements in three-dimensional printing technology have resulted in both high-end and economy instruments that are now available for the facile production of customized models.2 These printers have the ability to extrude high-resolution objects with enough detail to accurately represent in vivo images generated from a preclinical X-ray CT scanner. With proper data collection, surface rendering, and stereolithographic editing, it is now possible and inexpensive to rapidly produce detailed skeletal and soft tissue structures from X-ray CT data. Even in the early stages of development, the anatomical models produced by three-dimensional printing appeal to both educators and researchers who can utilize the technology to improve visualization proficiency. 3, 4 The real benefits of this method result from the tangible experience a researcher can have with data that cannot be adequately conveyed through a computer screen. The translation of pre-clinical 3D data to a physical object that is an exact copy of the test subject is a powerful tool for visualization and communication, especially for relating imaging research to students, or those in other fields. Here, we provide a detailed method for printing plastic models of bone and organ structures derived from X-ray CT scans utilizing an Albira X-ray CT system in conjunction with PMOD, ImageJ, Meshlab, Netfabb, and ReplicatorG software packages.

Using modern plastic extrusion and printing technologies, it is now possible to quickly and inexpensively produce physical models of X-ray CT data taken in a laboratory. The three -dimensional printing of tomographic data is a powerful visualization, research, and educational tool that may now be accessed by the preclinical imaging community.

Three-dimensional printing allows for the production of highly detailed objects through a process known as additive manufacturing.  Traditional, ...

Biology

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D. As these models are often extremely complex, a virtual reality environment is also important to be set up to overcome occlusion and to take full advantage of the 3D structure. Here, a step-by-step guide from image segmentation to reconstruction and analysis is described in detail.

The described pipeline is designed for the segmentation of electron microscopy datasets larger than gigabytes, to extract whole-cell morphologies. Once the cells are reconstructed in 3D, customized software designed around individual needs can be used to perform a qualitative and quantitative analysis directly in 3D, also using virtual reality to overcome view occlusion.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction ...

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. The model can be used for planning and simulating critical steps of a surgical approach. Therefore, it is important that anatomical structures such as blood vessels inside a tumor can be printed to be colored not only on their surface, but throughout their whole volume. During simulation this allows for the removal of certain parts (e.g., with a high-speed drill) and revealing internally located structures of a different color. Thus, diagnostic information from various imaging modalities (e.g., CT, MRI) can be combined in a single compact and tangible object.
However, preparation and printing of such a fully colored anatomical model remains a difficult task. Therefore, a step-by-step guide is provided, demonstrating the fusion of different cross-sectional imaging data sets, segmentation of anatomical structures, and creation of a virtual model. In a second step the virtual model is printed with volumetrically colored anatomical structures using a plaster-based color 3D binder jetting technique. This method allows highly accurate reproduction of patient-specific anatomy as shown in a series of 3D-printed petrous apex chondrosarcomas. Furthermore, the models created can be cut and drilled, revealing internal structures that allow for simulation of surgical procedures.

The protocol describes the fabrication of fully colored three-dimensional prints of patient-specific, anatomical skull models to be used for surgical simulation. The crucial steps of combining different imaging modalities, image segmentation, three-dimensional model extraction, and production of the prints are explained.

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. ...

Medicine

Enhancing Neuroimaging Analysis in Brain Disorders

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for brain maturation, immune/ inflammatory regulation, and behavioral/cognitive functioning. However, current automated neuroimaging segmentation tools are poor at accurately and reliably segmenting the lateral ventricle choroid plexus. Furthermore, there is no existing tool that segments the choroid plexus located in the third and fourth ventricles of the brain. Thus, a protocol delineating how to segment the choroid plexus in the lateral, third, and fourth ventricle is needed to increase the reliability and replicability of studies examining the choroid plexus in neurodevelopmental and brain disorders. This protocol provides detailed steps to create separately labeled files in 3D Slicer for the choroid plexus based on DICOM or NIFTI images. The choroid plexus will be manually segmented using the axial, sagittal, and coronal planes of T1w images making sure to exclude voxels from gray or white matter structures bordering the ventricles. Windowing will be adjusted to assist in the localization of the choroid plexus and its anatomical boundaries. Methods for assessing accuracy and reliability will be demonstrated as part of this protocol. Gold standard segmentation of the choroid plexus using manual delineations can be used to develop better and more reliable automated segmentation tools that can be openly shared to elucidate changes in the choroid plexus across the lifespan and within various brain disorders.

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies 

Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for ...

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

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