Abstract
Extraocular muscle pulleys, now well known to be kinematically significant extraocular structures, have been noted in passing and described in fragments several times over the past two centuries. They were late to be fully appreciated because biomechanical modeling of the orbit was not available to derive their kinematic consequences, and because pulleys are distributed condensations of collagen, elastin and smooth muscle (SM) that are not sharply delineated. Might other mechanically significant distributed extraocular structures still be awaiting description? p]An imaging approach is useful for describing distributed structures, but does not seem suitable for assessing mechanical properties. However, an image that distinguished types and densities of constituent tissues could give strong hints about mechanical properties. Thus, we have developed methods for producing three dimensional (3D) images of extraocular tissues based on thin histochemically processed slices, which distinguish collagen, elastin, striated muscle and SM. Overall tissue distortions caused by embedding for sectioning, and individual-slice distortions caused by thin sectioning and subsequent histologic processing were corrected by ordered image warping with intrinsic fiducials.
We describe an extraocular structure, partly included in Lockwood’s ligament, which contains dense elastin and SM bands, and which might refine horizontal eye alignment as a function of vertical gaze, and torsion in down-gaze. This active structure might therefore be a factor in strabismus and a target of therapeutic intervention.
Tissue imaging methods can be ordered from low distortion methods which, like MRI, provide a physiologically realistic view, albeit with low spatial resolution and poor tissue differentiation, through those which, like thick slices, provide moderate resolution, differentiation and distortions, to high resolution methods which, like stained thin sections, provide high spatial resolution and exquisite tissue differentiation, at the cost of significant spatial distortions. Instead of abandoning the thin slice histologic approach, as did Koornneef, or abandoning computer reconstruction, as did Dutton & Waldrop, we used computer-aided image processing to compensate for distortions in the slice plane caused by histologic processing, and 3D reconstruction techniques to restore the spatial relationships perpendicular to the slice plane. We correct the distortions in high-resolution slices by warping them to fit correlated, low distortion slices, guided by features that are visible in every slice.
In the present work, we distinguish several types of tissue (striated muscle, SM, collagen, and elastin) as well as familiar anatomic structures (EOMs, globe, optic nerve, blood vessels and nerves). Our results, presented as manipulable 3D objects, support and extend the results of Kono et al (
2002), who used more conventional methods to study these tissues.
We used magnetic resonance images (MRI) of whole, and digital images of serially sectioned human cadaveric orbits. Specimens were identified only by code, and were accompanied only by aspects of the donor’s medical history that bore on orbital structure and histology, such as race, age, diseases with extraocular manifestations, and ocular surgeries.
Cadaveric materials were obtained from UCLA Medical Center and a tissue bank (IIAM, Scranton, PA). Study of cadaveric specimens was conducted in compliance with state and local law.
We report here results from 2 orbits: (1) Specimen “H5” was harvested at autopsy 20 hrs after death from complications of cardiac transplantation of a 44-year-old white male with Marfan syndrome. Marfan syndrome is caused by a mutation in the gene that codes for the glycoprotein fibrillin-1, which forms the core of elastic fibrils and bonds together SM cells. For this reason, and on the basis of previous histological examinations of Marfan’s orbits, we expected elastin to be abnormal, and so did not analyze its distribution. Although the fine structure of smooth muscle is probably abnormal in Marfan’s, its overall distribution probably is not (
Oh, Clark, Velez, Rosenbaum, & Demer, 2002). MRI was done after exenteration. (2) Specimen “H7” was harvested from a 17-month-old male victim of “Sudden Infant Death Syndrome”, obtained from a tissue bank. The whole head was frozen, and MRI was done after thawing but prior to exenteration.
Three-dimensional (3D) reconstruction from slice data has become familiar in connection with tomography, confocal microscopy, and such projects as the Visible Human (National Library of Medicine), so it is easy to imagine that reconstruction problems have all been solved. However, these applications require only relatively straightforward reconstruction methods: registration of each slice with the next is unambiguous, shape distortions are small or non-existent and, in any case, neighboring slices are similarly distorted. In contrast, if we wish to utilize histochemical and immunohistochemical processing to reveal the fine structure and constituent tissues in a sample, we have a much more difficult reconstruction problem, because the requisite reagents and stains can effectively penetrate only thin sections. Consequently: (1) surrounding bone must be removed to avoid damage to the microtome knife, which tends to cause soft elastic tissues, such those of the orbit, to collapse, and spatial relationships to be lost; (2) imbedding compounds, used to support the specimen during thin sectioning, introduce distortions as they harden and shrink, especially when the tissue contains distinct compartments, such as the globe; (3) registration of sequential slices is lost when they are cut; (4) each slice may be uniquely and non-linearly distorted by cutting and subsequent processing.
As an orbit or other tissue passes from life, through the stages of histologic processing, it can be imaged with increasing resolution and tissue differentiation, at the cost of accumulating distortions (
Figure 1). This tradeoff is the main problem of
thin-slice reconstruction, which we have approached with a method of
ordered warping with intrinsic fiducials. Briefly, we used non-linear 2-dimensional image warping algorithms to bring the images from a given processing stage into alignment with corresponding images from the preceding stage. Warping must be guided by reference points, or fiducials, for which we distinguished
strong structures, the globe, optic nerve & EOMs (see
Figure 2), which could be found in every slice, from the remaining
weak structures, the distributed collagen, elastin and SM. We then identified and correlated the strong structures across tomographic, block-face and thin-slice images, and with these structures as references or fiducials, warped each block-face image into alignment with nearby tomographic images (tomographic image planes were sparser than block-face image planes), approximately correcting the block-face images for imbedding distortions. We repeated this procedure, except now warping thin-slice images to corrected block-face images, reducing the idiosyncratic distortions of the thin-slice images. Corrected thin-slice images were then aligned, and 3D orbits were reconstructed, fitting smooth surfaces to the strong structures, and using volumetric rendering for the weak structures, so as to visualize the distributions of collagen, SM and elastin with reference to the globe, optic nerve and EOMs. Details of the reconstruction procedure are given in
.
We have described circumferential distributions of SM and elastin in the equatorial orbit, extending from the superior margin of the MR to the crossing of the IR and IO. In the inferior orbit, elastin, but not SM, continues anteriorly. We returned to the histologically prepared tissue slices to assess whether the orientations of SM cells and fascicles (bundles) followed the overall distribution of SM.
In the inferior orbit, anterior to the IR-IO crossing, SM fascicles tended to have an anteroposterior orientation, with individual cells oriented in various directions. More posteriorly, in the equatorial region, SM fascicles and cells were more circumferentially organized, with some cells running radially toward the medial orbital wall. Thus, in the equatorial region, individual cells and fascicles tend to be aligned with the overall distribution of SM, suggesting that this muscle could modulate the separation between the MR pulley and the crossing of the IR and IO.
In summary, we used a method of ordered warping with intrinsic fiducials to reconstruct the 3D architecture of histochemically identified extraocular connective tissues, and thereby identified substantial bands of SM and elastin extending from the region between the IR-IO crossing to the MR pulley. We propose that these two roughly coincident tissue bands compose a single functional structure, and call it the inframedial peribulbar muscle (IMPM).
As with EOM pulleys, the IMPM has not gone completely unnoticed, but apart from the earlier work of our group (
Kono et al., 2002), we know of no description of the heavy concentration of elastin in this region, and it is fair to say that classical descriptions do not suggest that this region contains the most substantial component of the peribulbar SM. Our findings (with the caveat that they are derived from only two samples) do not confirm the classically described continuities of peribulbar SM with the superior and inferior palpebral muscles (
Duke-Elder & Wybar, 1961).
The present study and that of
Kono et al. (2002) both drew histological data from the same set, but where we used mainly graphical methods of data analyses and presentation, Kono et al used mainly numerical methods, yielding two quite different and largely independent analyses. Still, there are no substantial inconsistencies between the 2 studies, which tends to validate the methods of both, and each offers unique findings, which shows some of the relative strengths of the two approaches.
Both studies agree that the structure we have called the IMPM is the most significant equatorial intermuscular connection, and we are in essential agreement on its dimensions, although the current study makes clear that it does not have a simple shape, as can be seen particularly in the H7 elastin distribution (
Figure 6). The H7 reconstruction also shows that the SM and elastin distributions only partly overlap, with elastin extending more posteriorly at the MR, and so coursing anteriorly as it extends to the IR-IO crossing.
There is further agreement that the remaining three quadrants of the capsulopalpebral muscle contain little SM. The H7 reconstruction clarifies that smooth muscle in the superior quadrants is mostly vascular, and SM in the infero-medial quadrant tends to follow Lockwood’s ligament to the inner canthus, rather than contribute to an intermuscular connection.
It would not be wrong to say that the IMPM is a specialized part of Lockwood’s ligament, keeping in mind that the IMPM’s elastin and SM components are feeble lateral to the IR, where Lockwood’s is well defined, and well defined to the superior margin of the MR, where only some authors consider Lockwood’s to extend. But it seems preferable to describe the IMPM as a distinct musculo-elastic structure, joining the MR pulley with the coupled pulleys of the IR and IO.
Details of our reconstruction procedure follow:
In an alert subject, it would be possible to collect MR or CT images during voluntary fixation, showing physiologic muscle paths, muscle cross-sections, and globe positions (all functions of EOM innervation), as well as some of the main connective tissue structures (which may move because of EOM activity or changes in SM tone). Alert-subject tomography introduces essentially no distortion, but scan times are limited to the periods over which stable fixation can be maintained, giving modest spatial resolution in the scan plane of about 200 – 800 µm, and poor spatial resolution perpendicular to the scan plane of 2 – 3 mm. Bone, fat, muscle, and connective tissue, can usually be differentiated, but types of connective tissue cannot.
In the present study, we began with cadaveric orbits, scanned in quasi-coronal planes by MRI with bone intact (specimen 7) or after exenteration en bloc with periorbita intact (specimen 5), using 3” phased array surface coils, a T1-weighted pulse sequence and multiple excitations in a 1.5 T scanner (Signa, General Electric, Milwaukee WI), achieving pixel resolutions of 156 or 195 µm. In the absence of normal innervation patterns, EOM paths, EOM cross-sections, and the positions of dependent structures would be somewhat abnormal, and other abnormalities may have resulted from post-mortem changes. However, such cadaveric tomographic images are free of tissue processing distortions.
Bone was thinned or removed mechanically (specimen 7) and residua decalcified (
Demer et al., 2000), staining with fluorscein to improve contrast of the embedded tissue while allowing the subsequently cut thin slices to be washed clean, and embedding in paraffin. Each block was then mounted on a microtome for sectioning perpendicular to the orbital axis.
The planed block face was then digitally photographed at 200µm intervals at a spatial resolution of 520 pixels/cm and color resolution of 24 bits/pixel using a Lumina digital camera (Leaf Systems), yielding a series of block-face images, which had modest tissue differentiation and certain overall distortions associated with exenteration and embedding, but no distortions associated with cutting or processing individual thin slices.
Each block-face image was compared to the nearest cadaveric tomographic image. Adobe PhotoshopTM 6.0 (Adobe Systems, San Jose CA) was used to correct the block-face image for linear distortions using the “scale” tool, and for non-linear embedding distortions that affected surrounding tissue using the “liquefy” warping tool. The most prominent non-linear distortion was an invagination of the globe caused by shrinkage of the embedding compound used to fill it. This stage yielded block-face images at 200 µm intervals corrected for exenteration and embedding distortions.
Twenty 10 µm slices were cut between each imaged block-face, and were subsequently stained and mounted: the first section in each series of 10 was stained with Mason’s trichrome stain (which shows muscle and collagen), the second with EVG (for elastin), and the third, with a stain constructed from an antibody to SM α-actin linked to a blue chromogen (
Demer et al., 1997). Two more sections were saved for possible future use, and the rest were discarded.
Stained sections were digitally photographed at a spatial resolution of 520 pixels/cm and color resolution of 24 bits/pixel using the Lumina camera. This resolution allowed each slice to be captured as a single image file of manageable size, and provided adequate resolution to resolve collagen and SM, but not elastin. For sample H7 the EVG series was therefore also imaged through a microscope at 10,400 pixels/cm. These high-resolution EVG images were assembled into montages using the 520 pixels/cm images as templates. The resulting files were processed using Photoshop to increase contrast between the black-brown stained elastin fibrils and surrounding tissues, so that the elastin survived down-sampling to 520 pixels/cm.
This processing stage yielded 3 series of thin-slice images at 100µm intervals with high spatial resolution and tissue differentiation, but with non-linear distortions that were unique to each slice.
Using MorphTM 2.5 (Gryphon Software, San Diego CA), we then chose reference points, or fiducials, on the strong structures visible in all slices, and warped each thin-slice image to its neighboring corrected block-face image, thereby correcting for non-linear distortions introduced by microtome slicing, and histologic processing.
Photoshop was then used to isolate and extract the desired soft tissues. Level adjustment was used to correct for color variation between slices. Background tinting was removed using color filtration. Images were then edited manually using selection tools to remove any remaining tissues other than those of interest.
Custom software (“TSR” for Thin-Slice Reconstruction) was developed to combine the 3D surfaces derived from the strong structures (globe, optic nerve and EOMs), with overlapping volumetric data from the weak structures (distributed collagen, elastin and SM) in the 3 series of stained thin slices. Photoshop files were imported into TSR, and color correction filters were applied to compensate for differences in staining effectiveness across slices of a given series. Contours were traced to outline the strong structures. Slices were translated into register using an automatic method that maximized the cross-correlation of pixels in adjacent slices, or manually when the automatic method failed. For strong structures, surfaces were constructed to “skin” correlated contours across slices. For weak structures, pixels were thickened to voxels (volume elements) and TSR filters were applied to smooth them within and between slices, as necessary, to reasonably represent the raw slice data, while minimizing distracting artifacts of reconstruction. Spatial resolution was limited by computer memory, but was generally sufficient to show the detail preserved in the reconstructions. Surfaces representing strong structures and volumetric data representing weak structures were then adjusted for color and transparency to maximize visibility of structures of interest, and rendered to enhance three-dimensionality. The resulting composite object was viewed from 180 different angles (360° in 20° horizontal intervals and 180° in 18° vertical intervals), and the snapshots transferred to QuicktimeTM VR Authoring Studio 1.0 (Apple Computer, Cupertino CA) to produce the final QTVR objects.