Post-carotid endarterectomy neurocognitive decline is associated with cerebral blood flow asymmetry on post-operative magnetic resonance perfusion brain scans.

OBJECTIVE: Up to 25% of patients experience subtle declines in post-operative neurocognitive function following, otherwise uncomplicated, carotid endarterectomy (CEA). We sought to determine if post-CEA neurocognitive deficits are associated with cerebral blood flow (CBF) abnormalities on post-operative MR perfusion brain scans. METHODS: We enrolled 22 CEA patients to undergo a battery of neuropsychometric tests pre-operatively and on post-operative day 1 (POD 1). Neurocognitive dysfunction was defined as a two standard deviation decline in performance in comparison to a similarly aged control group of lumbar laminectomy patients. All patients received MR perfusion brain scans on POD 1 that were analysed for asymmetries in CBF distribution. One patient experienced a transient ischemic attack within 24 hours before the procedure and was excluded from our analysis. RESULTS: Twenty-nine percent of CEA patients demonstrated neurocognitive dysfunction on POD 1. One hundred percent of those patients with cognitive deficits demonstrated CBF asymmetry, in contrast to only 27% of those patients without cognitive impairment. Post-CEA cognitive dysfunction was significantly associated with CBF abnormalities (RR=3.75, 95% CI: 1.62-8.67, p=0.004). CONCLUSION: Post-CEA neurocognitive dysfunction is significantly associated with post-operative CBF asymmetry. These results support the hypothesis that post-CEA cognitive impairment is caused by cerebral hemodynamic changes. Further work exploring the relationship between CBF and post-CEA cognitive dysfunction is needed.

Asymmetry analysis in rodent cerebral ischemia models.

RATIONALE AND OBJECTIVES: An automated method for identification and segmentation of acute/subacute ischemic stroke, using the inherent bi-fold symmetry in brain images, is presented. An accurate and automated method for localization of acute ischemic stroke could provide physicians with a mechanism for early detection and potentially faster delivery of effective stroke therapy. MATERIALS AND METHODS: Segmentation of ischemic stroke was performed on magnetic resonance (MR) images of subacute rodent cerebral ischemia. Eight adult male Wistar rats weighing 225-300 g were anesthetized with halothane in a mix of 70% nitrous oxide/30% oxygen. Animal core temperature was maintained at 37 degrees C during the entire surgical procedure, including occlusion of the middle cerebral artery (MCA) and the 90-minute post-reperfusion period. To confirm cerebral ischemia, transcranial measurements of cerebral blood flow were performed with laser-Doppler flowmetry, using 15-mm flexible fiberoptic Doppler probes attached to the skull over the MCA territory. Animal MR scans were performed at 1.5 T using a knee coil. Three experts performed manual tracing of the stroke regions for each rat, using the histologic-stained slices to guide delineation of stroke regions. A strict tracing protocol was followed that included multiple (three) tracings of each stroke region. The volumetric MR image data were processed for each rat by computing the axis of symmetry and extracting statistical dissimilarities. A nonparametric Wilcoxon rank sum test operating on paired windows in opposing hemispheres identified seeds in the pixels exhibiting statistically significant bi-fold mirror asymmetry. Two brain reference maps were used for analysis: an absolute difference map (ADM) and a statistical difference map (SDM). Although an ADM simply displays the absolute difference by subtracting one brain hemisphere from its reflection, SDM highlights regions by labeling pixels exhibiting statistically significant asymmetry. RESULTS: To assess the accuracy of the proposed segmentation method, the surrogate ground truth (the stroke tracing data) was compared to the results of our proposed automated segmentation algorithm. Three accuracy segmentation metrics were utilized: true-positive volume fraction (TPVF), false-positive volume fraction (FPVF), and false-negative volume fraction (FNVF). The mean value of the TPVF for our segmentation method was 0.8877; 95% CI 0.7254 to 1.0500; the mean FPVF was 0.3370, 95% CI -0.0893 to 0.7633; the mean FNVF was 0.1122, 95% CI -0.0502 to 0.2747. CONCLUSIONS: Unlike most segmentation methods that require some degree of manual intervention, our segmentation algorithm is fully automated and highly accurate in identifying regions of brain asymmetry. This approach is attractive for numerous neurologic applications where the operator's intervention should be minimal or null.

Modeling real-time 3-d lung deformations for medical visualization.

In this paper, we propose a physics-based and physiology-based approach for modeling real-time deformations of 3-D high-resolution polygonal lung models obtained from high-resolution computed tomography (HRCT) images of normal human subjects. The physics-based deformation operator is nonsymmetric, which accounts for the heterogeneous elastic properties of the lung tissue and spatial-dynamic flow properties of the air. An iterative approach is used to estimate the deformation with the deformation operator initialized based on the regional alveolar expandability, a key physiology-based parameter. The force applied on each surface node is based on the airflow pattern inside the lungs, which is known to be based on the orientation of the human subject. The validation of lung dynamics is done by resimulating the lung deformation and comparing it with HRCT data and computing force applied on each node derived from a 4-D HRCT dataset of a normal human subject using the proposed deformation operator and verifying its gradient with the orientation.

Distributed augmented reality with 3-D lung dynamics--a planning tool concept.

Augmented reality (AR) systems add visual information to the world by using advanced display techniques. The advances in miniaturization and reduced hardware costs make some of these systems feasible for applications in a wide set of fields. We present a potential component of the cyber infrastructure for the operating room of the future: a distributed AR-based software-hardware system that allows real-time visualization of three-dimensional (3-D) lung dynamics superimposed directly on the patient's body. Several emergency events (e.g., closed and tension pneumothorax) and surgical procedures related to lung (e.g., lung transplantation, lung volume reduction surgery, surgical treatment of lung infections, lung cancer surgery) could benefit from the proposed prototype.

A framework for evaluating image segmentation algorithms.

The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors-precision (reliability), accuracy (validity), and efficiency (viability)-need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit, repeat segmentation considering all sources of variation, and determine variations in figure of merit via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. In determining accuracy, it may be important to consider different 'landmark' areas of the structure to be segmented depending on the application. To assess efficiency, both the computational and the user time required for algorithm training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency factors have an influence on one another. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors, as illustrated in an example wherein two methods are compared in a particular application domain. The weight given to each factor depends on application.

Toward objective quantification of perfusion-weighted computed tomography in subarachnoid hemorrhage: quantification of symmetry and automated delineation of vascular territories.

RATIONALE AND OBJECTIVES: Perfusion-weighted computed tomography (CTP) is a relatively recent innovation that estimates a value for cerebral blood flow (CBF) using a series of axial head CT images tracking the time course of a signal from an intravenous contrast bolus. MATERIALS AND METHODS: CTP images were obtained using a standard imaging protocol and were analyzed using commercially available software. A novel computer-based method was used for objective quantification of side-to-side asymmetries of CBF values calculated from CTP images. RESULTS: Our method corrects for the inherent variability of the CTP methodology seen in the subarachnoid hemorrhage (SAH) patient population to potentially aid in the diagnosis of cerebral vasospasm (CVS). This method analyzes and quantifies side-to-side asymmetry of CBF and presents relative differences in a construct termed a Relative Difference Map (RDM). To further automate this process, we have developed a unique methodology that enables a computer to delineate vascular territories within a brain image, regardless of the size and shape of the brain. CONCLUSIONS: While both the quantification of image symmetry using RDMs and the automated assignment of vascular territories were initially designed for the analysis of CTP images, it is likely that they will be useful in a variety of applications.

A novel drill set for the enhancement and assessment of robotic surgical performance.

BACKGROUND: There currently exist several training modules to improve performance during video-assisted surgery. The unique characteristics of robotic surgery make these platforms an inadequate environment for the development and assessment of robotic surgical performance. METHODS: Expert surgeons (n=4) (>50 clinical robotic procedures and >2 years of clinical robotic experience) were compared to novice surgeons (n=17) (<5 clinical cases and limited laboratory experience) using the da Vinci Surgical System. Seven drills were designed to simulate clinical robotic surgical tasks. Performance score was calculated by the equation Time to Completion + (minor error) x 5 + (major error) x 10. The Robotic Learning Curve (RLC) was expressed as a trend line of the performance scores corresponding to each repeated drill. RESULTS: Performance scores for experts were better than novices in all 7 drills (p<0.05). The RLC for novices reflected an improvement in scores (p<0.05). In contrast, experts demonstrated a flat RLC for 6 drills and an improvement in one drill (p=0.027). CONCLUSION: This new drill set provides a framework for performance assessment during robotic surgery. The inclusion of particular drills and their role in training robotic surgeons of the future awaits larger validation studies.

Development of a training tool for endotracheal intubation: distributed augmented reality.

The authors introduce a tool referred to as the Ultimate Intubation Head (UIH) to train medical practitioners' hand-eye coordination in performing endotracheal intubation with the help of augmented reality methods. In this paper we describe the integration of a deployable UIH and present methods for augmented reality registration of real and virtual anatomical models. The assessment of the 52 degrees field of view optics of the custom-designed and built head-mounted display is less than 1.5 arc minutes in the amount of blur and astigmatism, the two limiting optical aberrations. Distortion is less than 2.5%. Preliminary results of the registration of a physical phantom mandible on its virtual counterpart yields less than 3mm rms. in registration. Finally we describe an approach to distributed visualization where a given training procedure may be visualized and shared at various remote locations. Basic assessments of delays within two scenarios of data distribution were conducted and reported.

Employing symmetry features for automatic misalignment correction in neuroimages.

A novel method to automatically compute the symmetry plane and to correct the 3D orientation of neuro-images is presented. In acquisition of neuroimaging scans, the lack of perfect alignment of a patient's head makes it challenging to evaluate brain images. By deploying a shape-based criterion, the symmetry plane is defined as a plane that best matches external surface points on one side of the head, with their counterparts on the other side. In our method, the head volume is represented as a re-parameterized surface point cloud, where each location is parameterized by its elevation (latitude), azimuth (longitude), and radius. The search for the best matching surfaces is implemented in a multi-resolution paradigm, and the computation time is significantly decreased. The algorithm was quantitatively evaluated using in both simulated data and in real T1, T2, Flair magnetic resonance patient images. This algorithm is found to be fast (<10s per MR volume), robust and accurate (<.6 degree of Mean Angular Error), invariant to the acquisition noise, slice thickness, bias field, and pathological asymmetries.

Automatic misalignment correction in neuroimages using surface symmetry priors.

Many brain imaging procedures require the careful alignment of different sets of images obtained in the same individual. The available automatic methods for brain alignment are susceptible to improvement. This paper discusses briefly a new automatic method to reinstall the tilted orientation of head images, using surface symmetry as a prior.

Generating classes of 3D virtual mandibles for AR-based medical simulation.

INTRODUCTION: Simulation and modeling represent promising tools for several application domains from engineering to forensic science and medicine. Advances in 3D imaging technology convey paradigms such as augmented reality (AR) and mixed reality inside promising simulation tools for the training industry. METHODS: Motivated by the requirement for superimposing anatomically correct 3D models on a human patient simulator (HPS) and visualizing them in an AR environment, the purpose of this research effort was to develop and validate a method for scaling a source human mandible to a target human mandible within a 2 mm root mean square (RMS) error. RESULTS: Results show that, given a distance between 2 same landmarks on 2 different mandibles, a relative scaling factor may be computed. Using this scaling factor, results show that a 3D virtual mandible model can be made morphometrically equivalent to a real target-specific mandible within a 1.30 mm RMS error. CONCLUSION: The virtual mandible may be further used as a reference target for registering other anatomic models, such as the lungs, on the HPS. Such registration will be made possible by physical constraints among the mandible and the spinal column in the horizontal normal rest position.

Symmetry identification using partial surface matching and tilt correction in 3D brain images.

We propose a novel method to automatically compute the symmetry plane and correct the 3D orientation of patient brain images. Many images of the brain are clinically unreadable because of the misalignment of the patient's head in the scanner. We proposed an algorithm that represents the brain volume as a re-parameterized surface point cloud where each location has been parameterized by its elevation (latitude), azimuth (longitude) and radius. The removal of the interior contents of the brain makes this approach perform robustly in the presence of the brain pathologies, e.g. tumor, stroke and bleed. Thus, we decompose the symmetry plane computation problem into a surface matching routine. The search for the best matching surface is implemented in a multi-resolution paradigm so as to decrease computational time considerably. Spatial affine transform then is performed to rotate the 3D brain images and align them within the coordinate system of the scanner. The corrected brain volume is re-sliced such that each planar image represents the brain at the same axial level.

Statistical bilateral asymmetry measurement in brain images.

We present an improvement of an automated generic methodology for symmetry identification, asymmetry quantification, and segmentation of brain pathologies, utilizing the inherent bi-fold mirror symmetry in brain imagery. In the pipeline of operations starting with detection of the symmetry axis, hemisphere-wise cross registration, statistical correlation and quantification of asymmetries, we segment a target brain pathology. The detection of pathological difference left to right in brain imagery is complicated by normal variations as well as geometric misalignment in anatomical structures between two hemispheres. Introducing hemisphere-wise registration and spatial correlation makes our approach perform robustly in the presence of normal asymmetries and systematic artifacts such as bias field and acquisition noise.

New views of male pelvic anatomy: role of computer-generated 3D images.

There is considerable controversy concerning the role of cadaveric dissection in teaching gross anatomy and the potential of using 3D computer-generated images to substitute for actual laboratory dissections. There are currently few high-quality 3D virtual models of anatomy available to evaluate the utility of computer-generated images. Existing 3D models are frequently of structures that are easily examined in three dimensions by removal from the cadaver, i.e., the heart, skull, and brain. We have focused on developing a 3D model of the pelvis, a region that is conceptually difficult and relatively inaccessible for student dissection. We feel students will benefit tremendously from 3D views of the pelvic anatomy. We generated 3D models of the male pelvic anatomy from hand-segmented color Visible Human Male cryosection data, reconstructed and visualized by Columbia University's in-house 3D Vesalius trade mark Visualizer.(1) These 3D models depict the anatomy of the region in a realistic true-to-life color and texture. They can be used to create 3D anatomical scenes, with arbitrary complexity, where the component anatomical structures are displayed in correct 3D anatomical relationships. Moreover, a sequence of 3D scenes can be defined to simulate actual dissection. Structures can be added in a layered sequence from the bony framework to build from the "inside-out" or disassembled much like a true laboratory dissection from the "outside-in." These 3D reconstructed anatomical models can provide views of the structures from new perspectives and have the potential to improve understanding of the anatomical relationships of the pelvic region (http://www.cellbiology.lsuhsc.edu/People/Faculty/Venuti_Figures/movie_index.html).

Augmented Reality for teaching endotracheal intubation: MR imaging to create anatomically correct models.

Clinical procedures have traditionally been taught at the bedside, in the morgue and in the animal lab. Augmented Reality (AR) technology (the merging of virtual reality and real objects or patients) provides a new method for teaching clinical and surgical procedures. Improved patient safety is a major advantage. We describe a system which employs AR technology to teach endotracheal intubation, using the Visible Human datasets, as well as MR images from live patient volunteers.

Adipose tissue quantification by imaging methods: a proposed classification.

Recent advances in imaging techniques and understanding of differences in the molecular biology of adipose tissue has rendered classical anatomy obsolete, requiring a new classification of the topography of adipose tissue. Adipose tissue is one of the largest body compartments, yet a classification that defines specific adipose tissue depots based on their anatomic location and related functions is lacking. The absence of an accepted taxonomy poses problems for investigators studying adipose tissue topography and its functional correlates. The aim of this review was to critically examine the literature on imaging of whole body and regional adipose tissue and to create the first systematic classification of adipose tissue topography. Adipose tissue terminology was examined in over 100 original publications. Our analysis revealed inconsistencies in the use of specific definitions, especially for the compartment termed "visceral" adipose tissue. This analysis leads us to propose an updated classification of total body and regional adipose tissue, providing a well-defined basis for correlating imaging studies of specific adipose tissue depots with molecular processes.