Biomechanical models that describe soft-tissue deformations provide a relatively inexpensive way to correct registration
errors in image guided neurosurgical systems caused by non-rigid brain shifts. Quantifying the factors that cause this
deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based method have been
developed recently which allow for uncertainty yet still capture the first order effects associated with brain deformations.
More specifically, the technique involves building an atlas of solutions to account for the statistical uncertainty in factors
that control the direction and magnitude of brain shift. The inverse solution is driven by a sparse intraoperative surface
measurement. Since this subset of data only provides surface information, it could bias the reconstruction and affect the
subsurface accuracy of the model prediction. Studies in intraoperative MR have shown that the deformation in the
midline, tentorium, and contralateral hemisphere is relatively small. The falx cerebri and tentorium cerebelli, two of the
important dural septa, act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain.
Accounting for these structures in models may be an important key to improving subsurface shift accuracy. The goals of
this paper are to describe a novel method developed to segment the tentorium cerebelli, develop the procedure for
modeling the dural septa and study the effect of those membranes on subsurface brain shift.
A patient specific finite element biphasic brain model has been utilized to codify a surgeon's experience by establishing
quantifiable biomechanical measures to score orientations for optimal planning of brain tumor resection. When faced
with evaluating several potential approaches to tumor removal during preoperative planning, the goal of this work is to
facilitate the surgeon's selection of a patient head orientation such that tumor presentation and resection is assisted via
favorable brain shift conditions rather than trying to allay confounding ones. Displacement-based measures consisting of
area classification of the brain surface shifting in the craniotomy region and lateral displacement of the tumor center
relative to an approach vector defined by the surgeon were calculated over a range of orientations and used to form an
objective function. The objective function was used in conjunction with Levenberg-Marquardt optimization to find the
ideal patient orientation. For a frontal lobe tumor presentation the model predicts an ideal orientation that indicates the
patient should be placed in a lateral decubitus position on the side contralateral to the tumor in order to minimize
unfavorable brain shift.
Measurement of intra-operative cortical brain movement is necessary to drive mechanical models developed to predict
sub-cortical shift. At our institution, this is done with a tracked laser range scanner. This device acquires both 3D range
data and 2D photographic images. 3D cortical brain movement can be estimated if 2D photographic images acquired
over time can be registered. Previously, we have developed a method, which permits this registration using vessels
visible in the images. But, vessel segmentation required the localization of starting and ending points for each vessel
segment. Here, we propose a method, which automates the segmentation process further. This method involves several
steps: (1) correction of lighting artifacts, (2) vessel enhancement, and (3) vessels' centerline extraction. Result obtained
on 5 images obtained in the operating room suggests that our method is robust and is able to segment vessels reliably.
In the past years different models have been formulated to explain the growth of gliomas in the brain. The most
accepted model is based on a reaction-diffusion equation that describes the growth of the tumor as two separate
components- a proliferative component and an invasive component. While many improvements have been made to this
basic model, the work exploring the factors that naturally inhibit growth is insufficient. It is known that stress fields
affect the growth of normal tissue. Due to the rigid skull surrounding the brain, mechanical stress might be an important
factor in inhibiting the growth of gliomas. A realistic model of glioma growth would have to take that inhibitory effect
into account. In this work a mathematical model based on the reaction-diffusion equation was used to describe tumor
growth, and the affect of mechanical stresses caused by the mass effect of tumor cells was studied. An initial tumor cell
concentration with a Gaussian distribution was assumed and tumor growth was simulated for two cases- one where
growth was solely governed by the reaction-diffusion equation and second where mechanical stress inhibits growth by
affecting the diffusivity. All the simulations were performed using the finite difference method. The results of
simulations show that the proposed mechanism of inhibition could have a significant affect on tumor growth predictions.
This could have implications for varied applications in the imaging field that use growth models, such as registration and
model updated surgery.
Image to physical space registration is a very challenging problem in image guided surgical procedures for the
liver, due to deformation and paucity of prominent surface anatomical landmarks. Iterative closest point (ICP) algorithm,
the surface registration method used for registering the intraoperative laser range scanner (LRS) data with the
preoperative CT data in image guided liver surgery, requires a good starting pose to reduce the number of iterations.
Currently anatomical landmarks such as vessel bifurcations are used for an initial registration. This paper presents a
computational approach to obtain the initial alignment that would reduce contact with probes for registration during
surgical procedures. A priori user information about the anatomical orientation of the liver is incorporated and used to
orient the point clouds for segmented CT data and LRS liver data. Four points are computationally selected on the
anatomical anterior surface of CT point cloud data and corresponding points are localized on the LRS data using the
orientation information. These four points are then used to find the rigid transformation using the singular value
decomposition method. Nine datasets were tested using the computational approach and the results were evaluated using
the anatomical landmarks method as the "gold standard". Seven of the nine datasets converged to the same solution
using both the methods. The computational method, being an approximated approach, may increase the number of
iterations to converge to the solution. However since the method does not require precise localization of anatomical
landmarks, it could potentially reduce OR time.
Chronic obstructive pulmonary diseases (COPD) are debilitating conditions of the lung and are the fourth leading cause of death in the United States. Early diagnosis is critical for timely intervention and effective treatment. The ability to quantify particular imaging features of specific pathology and accurately assess progression or response to treatment with current imaging tools is relatively poor. The goal of this project was to develop automated segmentation techniques that would be clinically useful as computer assisted diagnostic tools for COPD. The lungs were segmented using an optimized segmentation threshold and the trachea was segmented using a fixed threshold characteristic of air. The segmented images were smoothed by a morphological close operation using spherical elements of different sizes. The results were compared to other segmentation approaches using an optimized threshold to segment the trachea. Comparison of the segmentation results from 10 datasets showed that the method of trachea segmentation using a fixed air threshold followed by morphological closing with spherical element of size 23x23x5 yielded the best results. Inclusion of greater number of pulmonary vessels in the lung volume is important for the development of computer assisted diagnostic tools because the physiological changes of COPD can result in quantifiable anatomic changes in pulmonary vessels. Using a fixed threshold to segment the trachea removed airways from the lungs to a better extent as compared to using an optimized threshold. Preliminary measurements gathered from patient’s CT scans suggest that segmented images can be used for accurate analysis of total lung volume and volumes of regional lung parenchyma. Additionally, reproducible segmentation allows for quantification of specific pathologic features, such as lower intensity pixels, which are characteristic of abnormal air spaces in diseases like emphysema.
Arterial thrombosis causes death or paralysis of an organ, as it migrates to and localizes in different parts of the body. Massive pulmonary emboli cause 50,000 deaths per year. The cause and origin of arterial thrombosis is not well understood nor objectively characterized. The object of this study was to investigate the microscopic structure of arterial thrombus to better understand this pathology. Confocal microscopy cross-sectional images of an embolized thrombus in the coronary artery were obtained. Adjacent pairs of sections were stained with two different stains, fibrin and CD61, to reveal mutually complementary information. The very thin adjacent slices were treated as one slice.
Adjacent slices were registered by a combination of manual and automatic techniques using Analyze software developed in the Biomedical Imaging Resource at Mayo. After smoothing the images with a median filter, the CD61 and fibrin stained section images were used together to segment the tissues by multispectral classification. The image volume was classified into background, platelets and surrounding tissue, and thrombus. The segmented volume was then rendered for visualization and analysis of structure of the thrombus in three dimensions. Preliminary results are promising. Such correlation of structural and histological information may be helpful in determining the origin of the thrombus.
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