Towards brain first-aid: a diagnostic device for conscious awareness

When the brain is damaged, evaluating an individual's level of awareness can be a major diagnostic challenge (Is he or she in there?). Existing tests typically rely on behavioral indicators, which are incorrect in as many as one out of every two cases. The current paper presents a diagnostic device that addresses this problem. The technology circumvents behavioral limitations through noninvasive brain wave measurements (electroencephalography, or EEG). Unlike traditional EEG, the device is designed for point-of-care use by incorporating a portable, user-friendly, and stable design. It uses a novel software algorithm that automates subject stimulation, data acquisition/analysis, and the reporting of results. The test provides indicators for five identifiable levels of neural processing: sensation, perception, attention, memory, and language. The results are provided as rapidly obtained diagnostic, reliability, validity, and prognostic scores. The device can be applied to a wide variety of patients across a host of different environments. The technology is designed to be wireless-enabled for remote monitoring and assessment capabilities. In essence, the device is developed to scan for conscious awareness in order to optimize subsequent patient care.     

Volume rendering quantification algorithm for reconstruction of CT volume-rendered structures: Part I. Cerebral arteriovenous malformations

Volume rendering is a visualization technique that has important applications in diagnostic radiology and in radiotherapy but has not achieved widespread use due, in part, to the lack of volumetric analysis tools for comparison of volume rendering to conventional visualization techniques. The volume rendering quantification algorithm (VRQA), a technique for three-dimensional (3-D) reconstruction of a structure identified on six principal volume-rendered views, is introduced and described. VRQA involves three major steps: 1) preprocessing of the partial surfaces constructed from each of six volume-rendered images; 2) merging these processed partial surfaces to define the boundaries of a volume; and 3) computation of the volume of the structure from this boundary information. After testing on phantoms, VRQA was applied to CT data of patients with cerebral arteriovenous malformations (AVM's). Because volumetric visualization of the cerebral AVM is relatively insensitive to operator dependencies, such as the choice of opacity transfer function, and because precise volumetric definition of the AVM is necessary for radiosurgical treatment planning, it is representative of a class of structures that is ideal for testing and calibration of VRQA. AVM volumes obtained using VRQA are intermediate to those obtained using axial contouring and those obtained using CT-correlated biplanar angiography (two routinely used visualization techniques for treatment planning for AVM's). Applications and potential expansions of VRQA are discussed.     

Towards the realistic planning of GMPLS-based optical transport networks

The multi-granular switching concept defined in Generalized Multiprotocol Label Switching (GMPLS) is expected to be a future-proof solution for mitigating the Optical Crossconnet scalability problems associated with the skyrocketing growth of traffic in optical transport networks. In this paper, we address the problem of planning the GMPLS-based (or multi-granular) transport network with color (or label) conversion and signal regeneration capabilities. The objective of the problem is to minimize the total weighted port count in the transport network. The novelty of this problem lies in the incorporation of the following for the first time: (1) considering all traffic granularities defined in GMPLS; (2) allowing wavelength, waveband, and fiber conversions; (3) considering the optical-reach limitation of optical signals; and (4) customizing the optical reach of all-optical paths. Due to the computational complexity of the problem, we propose various efficient heuristics that are capable of solving large-sized problems in a reasonable amount of time. In order to achieve the best possible solution to the planning problem, a comprehensive evaluation of different heuristic variations through illustrative examples and simulations is conducted. The results also provide valuable insights into many issues that can contribute to further research and development in this area.     

Towards model-based engineering of optoelectronic packaging materials: dielectric constant modeling

Increases in data transmission speeds of optoelectronic devices have consequently increased high-frequency requirements for optoelectronic packaging materials including substrate, EMC/EMI shielding, adhesive and encapsulant (molding and underfill) materials. Most of those materials are polymer/filler composites, and critical materials properties for the device design and packaging include the effective dielectric constant, dielectric loss and their frequency and filler concentration dependence. This work presents a systematic theoretical investigation of the effective dielectric constant of polymer/filler composite materials, and its dependence on the filler concentration, the filler/polymer interaction, and the size of fillers. Our results demonstrate that, in contrary to the prevailing views, the filler concentration dependence of the effective dielectric constant is non-monotonic. Depending on the dielectric constant ratio between filler and polymer matrix, and the degree of interaction between filler and matrix, the effective dielectric constant exhibits an extreme as a function of filler concentration. In addition, our model is demonstrated to contain the Maxwell–Wagner formulation as an asymptotic limit. The present results have significant implications to the targeted formulation of optoelectronic packaging materials.     

Experimental characterization and numerical modeling of tissue electrical conductivity during pulsed electric fields for irreversible electroporation treatment planning

Irreversible electroporation is a new technique to kill cells in targeted tissue, such as tumors, through a nonthermal mechanism using electric pulses to irrecoverably disrupt the cell membrane. Treatment effects relate to the tissue electric field distribution, which can be predicted with numerical modeling for therapy planning. Pulse effects will change the cell and tissue properties through thermal and electroporation (EP)-based processes. This investigation characterizes these changes by measuring the electrical conductivity and temperature of ex vivo renal porcine tissue within a single pulse and for a 200 pulse protocol. These changes are incorporated into an equivalent circuit model for cells and tissue with a variable EP-based resistance, providing a potential method to estimate conductivity as a function of electric field and pulse length for other tissues. Finally, a numerical model using a human kidney volumetric mesh evaluated how treatment predictions vary when EP- and temperature-based electrical conductivity changes are incorporated. We conclude that significant changes in predicted outcomes will occur when the experimental results are applied to the numerical model, where the direction and degree of change varies with the electric field considered.     

Multiscale modeling for image analysis of brain tumor studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multiscale, multiphysics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlas-based segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.     

Rigorous mathematical modeling techniques for optimal delivery of macromolecules to the brain

Several treatment modalities for neurodegenerative diseases or tumors of the central nervous system involve invasive delivery of large molecular weight drugs to the brain. Despite the ample record of experimental studies, accurate drug targeting for the human brain remains a challenge. This paper proposes a systematic design method of administering drugs to specific locations in the human brain based on first principles transport in porous media. The proposed mathematical framework predicts achievable treatment volumes in target regions as a function of brain anatomy and infusion catheter position. A systematic procedure to determine the optimal infusion and catheter design parameters that maximize the penetration depth and volumes of distribution will be discussed. The computer simulations are validated with agarose gel phantom experiments and rat data. The rigorous computational approach will allow physicians and scientists to better plan the administration of therapeutic drugs to the central nervous system.      

Direct use of CT scans for hyperthermia treatment planning

In the field of deep regional hyperthermia cancer therapy, the BSD-2000 Hyperthermia System is one of the most widely used devices. Because of the complexity of the treatment process, computer modeling has long been viewed as a desirable means of planning patient treatments. Patient-specific, three-dimensional computer modeling for treatment planning in the BSD-2000 has been in clinical use at this institution for two years. Two of the persistent problems have been the large amount of time needed to create the patient model from a computed tomography (CT) scan (one and a half days), and the lack of a way to view the large amounts of data that comprise the output of a treatment plan, i.e., the specific absorption rate (SAR) at 20,000 to 30,000 cells. Here we present a method that obtains the dielectric properties needed for hyperthermia treatment planning directly from the CT image with minimum operator interaction, a process which takes about a half day and is more accurate. Comparison is made with the previous method of drawing contours around the different tissue types. We further describe a method which displays the output as iso-SAR contours directly over the CT scan of the patient.     

Computer-aided placement of deep brain stimulators: from planning to intraoperative guidance

In current practice, optimal placement of deep-brain stimulators (DBSs) used to treat movement disorders in patients with Parkinson's disease and essential tremor is an iterative procedure. A target is chosen preoperatively based on anatomical landmarks identified on magnetic resonance images. This point is used as an initial position that is refined intraoperatively using both microelectrode recordings and macrostimulation. In this paper, we report on our current progress toward developing a system for the computer-assisted preoperative selection of target points and for the intraoperative adjustment of these points. The system consists of a deformable atlas of optimal target points that can be used to select automatically the preoperative target, of an electrophysiological atlas, and of an intraoperative interface. Results we have obtained show that automatic prediction of target points is an achievable goal. Our results also indicate that electrophysiological information could be used to resolve structures not visible in anatomic images, thus improving both preoperative and intraoperative guidance. Our intraoperative system has reached the stage of a working prototype and we compare targeting accuracy as well as the number of paths needed to reach the targets with our system and with the method in current clinical use.     

Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly.     

An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures

This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warranty that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.     

颅内血管内栓塞治疗的超微结构电镜观察

应用扫描电镜及透射电镜对三种不同颅内血管栓塞材料进行颅内动静脉畸形（AVM）和动脉瘤治疗后的手术标术进行了超微结构的观察和比较。研究结果显示：可控性钨制电解式微弹簧圈栓塞效果理想，血栓形成完全；丝线栓因栓形成较差，而氰基丙烯酸正丁酯（NBCA）则对血管组织可引起慢性炎症及异物吞噬反应。本文对颅内AVM和动脉瘤等血管内栓塞治疗选择栓塞材料提供一定参考价值。     

A new algorithm for autoreconstruction of catheters in computed tomography-based brachytherapy treatment planning

This paper describes innovative software for automatic reconstruction, which we term autoreconstruction, of plastic and metallic brachytherapy catheters using computed tomography (CT) data. No such automatic facility has previously existed in any treatment planning software. The patient data consists of a set of post-implantation CT images with the catheters in situ in their final positions. This new software solves those difficulties which arise when the catheters are intersecting or when loop techniques are used. With the software algorithms, catheter reconstruction time is significantly reduced and accuracy is also improved when compared with that achieved using the classical manual method of CT-slice-by-CT-slice reconstruction.     

Data-guided brain deformation modeling: evaluation of a 3-D adjoint inversion method in porcine studies

Biomechanical models of brain deformation are useful tools for estimating parenchymal shift that results during open cranial procedures. Intraoperative data is likely to improve model estimates, but incorporation of such data into the model is not trivial. This study tests the adjoint equations method (AEM) for data assimilation as a viable approach for integrating displacement data into a brain deformation model. AEM was applied to two porcine experiments. AEM-based estimates were compared both to measured displacement data [from computed tomography (CT) scans] and to model solutions obtained without the guidance of sparse data, which we term the best prior estimate (BPE). Additionally, the sensitivity of the AEM solution to inverse parameter selection was investigated. The results suggest that it is most important to estimate the size of the variance in the measurement error correctly, make the correlation length long and estimate displacement (over stress) boundary conditions. Application of AEM shows an average 33% improvement over BPE. This paper represents the first evidence of successful use of the AEM technique in three dimensions with experimental data validation. The guidelines established for selection of model parameters are starting points for further optimization of the method under clinical conditions.     

Towards the standardization of performance measures for projectscheduling heuristics

Performance measures that can be used to compare project-scheduling heuristics are presented. Quantitative measures relative to the project duration are proposed for the diagnostic analysis of activity-sequencing heuristics in PERT networks with precedence and resource constraints. The aim is to establish a basis for the standardization of performance measures for the heuristics. A project analyst can use the evaluation techniques presented in deciding which of the many available scheduling heuristics is most appropriate for a given project scenario. A comparative experiment indicates that heuristics perform almost equally well for small projects, while their performances vary considerably for large projects. Thus, small projects with mediocre complexity are not accurate performance-measurement agents for scheduling heuristics     

Increasing the Prediction Accuracy for Thyroid Disease: A Step Towards Better Health for Society

Wireless Personal Communications - A healthy life is essential for a happy society, however it is a fact that seemingly invisible diseases plague our families and people suffer. The thyroid disease...