Shape-constrained deformable brain segmentation: Methods and quantitative validation.

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

Patient-Specific Sensor Registration for Electrical Source Imaging Using a Deformable Head Model.

OBJECTIVE: Electrical source imaging of brain activity is most accurate when using individualized bioelectric head models. Constructing these models requires identifying electrode positions on the scalp surface. Current methods such as photogrammetry involve significant user interaction that limits integration in clinical workflows. This work introduces and validates a new, fully-automatic method for sensor registration. METHODS: Average electrode coordinates are registered to the mean scalp mesh of a shape-constrained deformable head model used for tissue segmentation. Patient-specific electrode positions can be identified on the deformed scalp surface using point-based correspondence after model adaptation. RESULTS: The performance of the proposed method for sensor registration is evaluated with simulated and real data. Electrode variability is quantified for a photogrammetry-based solution and compared against the proposed sensor registration. CONCLUSION: A fully-automated model-based approach can identify electrode locations with similar accuracy as a current state-of-the-art photogrammetry system. SIGNIFICANCE: The new method for sensor registration presented in this work is rapid and fully automatic. It eliminates any user dependent inaccuracy introduced in sensor registration and ensures reproducible results. More importantly, it can more easily be integrated in clinical workflows, enabling broader adoption of electrical source imaging technologies.

Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation.

This work presents a novel approach for the rapid segmentation of clinically relevant subcortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other approaches for segmenting brain structures, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation and volume computation can be achieved within a minute of processing time. Furthermore, adaptation is driven by local and not global contrast, potentially relaxing requirements with respect to preprocessing steps such as bias-field correction. Detailed evaluation experiments on more than 1000 subjects, including comparisons to FSL FIRST and FreeSurfer as well as a clinical assessment, demonstrate high accuracy and test-retest consistency of the presented segmentation approach, leading, for example, to an average segmentation error of less than 0.5 mm. The presented approach might be useful in both, research as well as clinical routine, for automated segmentation and volume quantification of subcortical brain structures in order to increase confidence in the diagnosis of neuro-degenerative disorders, such as Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, or clinical applications for other neurologic and psychiatric diseases.

Differences in Regional Brain Volumes Two Months and One Year after Mild Traumatic Brain Injury.

Conventional structural imaging is often normal after mild traumatic brain injury (mTBI). There is a need for structural neuroimaging biomarkers that facilitate detection of milder injuries, allow recovery trajectory monitoring, and identify those at risk for poor functional outcome and disability. We present a novel approach to quantifying volumes of candidate brain regions at risk for injury. Compared to controls, patients with mTBI had significantly smaller volumes in several regions including the caudate, putamen, and thalamus when assessed 2 months after injury. These differences persisted but were reduced in magnitude 1 year after injury, suggesting the possibility of normalization over time in the affected regions. More pronounced differences, however, were found in the amygdala and hippocampus, suggesting the possibility of regionally specific responses to injury.

Fibroblast growth factor-2 is required for vasa vasorum plexus stability in hypercholesterolemic mice.

OBJECTIVE: Vasa vasorum are angiogenic in advanced stages of human atherosclerosis and hypercholesterolemic mouse models. Fibroblast growth factor-2 (FGF-2) is the predominant angiogenic growth factor in the adventitia and plaque of hypercholesterolemic low-density lipoprotein receptor-deficient/apolipoprotein B(100/100) mice (DKO). FGF-2 seems to play a role in the formation of a distinct vasa vasorum network. This study examined the vasa vasorum structure and its relationship to FGF-2. METHODS AND RESULTS: DKO mice treated with saline, antiangiogenic recombinant plasminogen activator inhibitor-1(23) (rPAI-1(23)), or soluble FGF receptor 1 were perfused with fluorescein-labeled Lycopersicon esculentum lectin. Confocal images of FGF-2-probed descending aorta adventitia show that angiogenic vasa vasorum form a plexus-like network in saline-treated DKO similar to the FGF-2 pattern of distribution. Mice treated with rPAI-1(23) and soluble FGF receptor 1 lack a plexus; FGF-2 and vasa vasorum density and area are significantly reduced. A perlecan/FGF-2 complex is critical for plexus stability. Excess plasmin produced in rPAI-1(23)-treated DKO mice degrades perlecan and destabilizes the plexus. Plasmin activity and plaque size measured in DKO and DKO/plasminogen activator inhibitor-1(-)(/-) mice demonstrate that elevated plasmin activity contributes to reduced plaque size. CONCLUSIONS: An FGF-2/perlecan complex is required for vasa vasorum plexus stability. Elevated plasmin activity plays a significant inhibitory role in vasa vasorum plexus and plaque development.

A Curvature-Adaptive Implicit Surface Reconstruction for Irregularly Spaced Points.

A curvature-adaptive implicit surface reconstruction for noisy and irregularly spaced points in 3D is introduced. The reconstructed surface traces the zero crossings of a signed field obtained from the sum of first-derivative anisotropic Gaussians centered at the points. The standard deviations of the anisotropic Gaussians are adapted to surface curvatures estimated from local data. A key characteristic of the formulation is its ability to smooth more along edges than across them, thereby preserving shape details while smoothing noise. The behavior of the proposed method under various density and organization of points is investigated and surface reconstruction results are compared with those obtained by well-known methods in the literature.

Micro computed tomography for vascular exploration.

Vascular exploration of small animals requires imaging hardware with a very high spatial resolution, capable of differentiating large as well as small vessels, in both in vivo and ex vivo studies. Micro Computed Tomography (micro-CT) has emerged in recent years as the preferred modality for this purpose, providing high resolution 3D volumetric data suitable for analysis, quantification, validation, and visualization of results. The usefulness of micro-CT, however, can be adversely affected by a range of factors including physical animal preparation, numerical quantification, visualization of results, and quantification software with limited possibilities. Exacerbating these inherent difficulties is the lack of a unified standard for micro-CT imaging. Most micro-CT today is aimed at particular applications and the software tools needed for quantification, developed mainly by imaging hardware manufacturers, lack the level of detail needed to address more specific aims. This review highlights the capabilities of micro-CT for vascular exploration, describes the current state of imaging protocols, and offers guidelines and suggestions aimed at making micro-CT more accurate, replicable, and robust.

Molecular imaging of vessels in mouse models of disease.

Vascular imaging of angiogenesis in mouse models of disease requires multi modal imaging hardware capable of targeting both structure and function at different physical scales. The three dimensional (3D) structure and function vascular information allows for accurate differentiation between biological processes. For example, image analysis of vessel development in angiogenesis vs. arteriogenesis enables more accurate detection of biological variation between subjects and more robust and reliable diagnosis of disease. In the recent years a number of micro imaging modalities have emerged in the field as preferred means for this purpose. They provide 3D volumetric data suitable for analysis, quantification, validation, and visualization of results in animal models. This review highlights the capabilities of microCT, ultrasound and microPET for multimodal imaging of angiogenesis and molecular vascular targets in a mouse model of tumor angiogenesis. The basic principles of the imaging modalities are described and experimental results are presented.

The antiangiogenic activity of rPAI-1(23) inhibits vasa vasorum and growth of atherosclerotic plaque.

Plaque vascularity has been implicated in its growth and stability. However, there is a paucity of information regarding the origin of plaque vasculature and the role of vasa vasorum in plaque growth. To inhibit growth of vasa vasorum in atherogenic mice and assess its effect on plaque growth, we used a truncated plasminogen activator inhibitor (PAI)-1 protein, rPAI-1(23), that has significant antiangiogenic activity. Female LDLR(-/-)ApoB-48-deficient mice fed Paigen's diet without cholate for 20 weeks received rPAI-1(23) treatment (n=21) for the last 6 weeks. Plaque size and vasa vasorum density were compared to 2 controls: mice fed Paigen's diet and treated with saline for the last 6 weeks (n=16) and mice fed Paigen's diet until the onset of treatment (n=14). The rPAI-1(23) treatment significantly reduced plaque area and plaque cholesterol in the descending aorta and plaque area in the innominate artery. Measurements of reconstructed confocal microscopy images of vasa vasorum demonstrate that rPAI-1(23) treatment decreased vasa vasorum area and length, which was supported by microCT images. Confocal images provide evidence for vascularized plaque in the saline-treated group but not in rPAI-1(23)-treated mice. The increased vessel density in saline-treated mice is attributable, in part, to upregulated fibroblast growth factor-2 expression, which is inhibited by rPAI-1(23). In conclusion, rPAI-1(23) inhibits growth of vasa vasorum, as well as vessels within the adjacent plaque and vessel wall, through inhibition of fibroblast growth factor-2, leading to reduced plaque growth in atherogenic female LDLR(-/-)ApoB-48-deficient mice.

A comparative study of transformation functions for nonrigid image registration.

Transformation functions play a major role in nonrigid image registration. In this paper, the characteristics of thin-plate spline (TPS), multiquadric (MQ), piecewise linear (PL), and weighted mean (WM) transformations are explored and their performances in nonrigid image registration are compared. TPS and MQ are found to be most suitable when the set of control-point correspondences is not large (fewer than a thousand) and variation in spacing between the control points is not large. When spacing between the control points varies greatly, PL is found to produce a more accurate registration than TPS and MQ. When a very large set of control points is given and the control points contain positional inaccuracies, WM is preferred over TPS, MQ, and PL because it uses an averaging process that smoothes the noise and does not require the solution of a very large system of equations. Use of transformation functions in the detection of incorrect correspondences is also discussed.