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OBJECTIVE: A practical method for patient-specific modeling of the aortic arch and the entire carotid vasculature from computed tomography angiography (CTA) scans for morphologic analysis and for interventional procedure simulation. MATERIALS AND METHODS: The method starts with the automatic watershed-based segmentation of the aorta and the construction of an a-priori intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weighting function that adaptively couples voxel intensity, intensity prior, and local vesselness shape prior. Finally, the same graph-cut optimization framework is used to interactively remove a few unwanted veins segments and to fill in minor vessel discontinuities caused by intensity variations. RESULTS: We validate our modeling method with two experimental studies on 71 multicenter clinical CTA datasets, including carotid bifurcation lumen segmentation on 56 CTAs from the MICCAI'2009 3D Segmentation Challenge. Segmentation results show that our method is comparable to the best existing methods and was successful in modeling the entire carotid vasculature with a Dice similarity measure of 84.5% (SD = 3.3%) and MSSD 0.48 mm (SD = 0.12 mm.) Simulation study shows that patient-specific simulations with four patient-specific models generated by our segmentation method on the ANGIO Mentor™ simulator platform are robust, realistic, and greatly improve the simulation. CONCLUSION: This constitutes a proof-of-concept that patient-specific CTA-based modeling and simulation of carotid interventional procedures are practical in a clinical environment.

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Diffusion-weighted MRI of the body has the potential to provide important new insights into physiological and microstructural properties. The intra-voxel incoherent motion (IVIM) model relates the observed DW-MRI signal decay to parameters that reflect perfusivity (D*) and its volume fraction (f), and diffusivity (D). However, the commonly used voxel-wise fitting of the IVIM model leads to parameter estimates with poor precision, which has hampered their practical usage. In this work, we increase the estimates' precision by introducing a model of spatial homogeneity, through which we obtain estimates of model parameters for all of the voxels at once, instead of solving for each voxel independently. Furthermore, we introduce an efficient iterative solver which utilizes a model-based bootstrap estimate of the distribution of residuals and a binary graph cut to generate optimal model parameter updates. Simulation experiments show that our approach reduces the relative root mean square error of the estimated parameters by 80% for the D* parameter and by 50% for the f and D parameters. We demonstrated the clinical impact of our model in distinguishing between enhancing and nonenhancing ileum segments in 24 Crohn's disease patients. Our model detected the enhanced segments with 91%/92% sensitivity/specificity which is better than the 81%/85% obtained by the voxel-independent approach.

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We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of -36% in the uncertainty values.

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This paper describes an evaluation framework that allows a standardized and objective quantitative comparison of carotid artery lumen segmentation and stenosis grading algorithms. We describe the data repository comprising 56 multi-center, multi-vendor CTA datasets, their acquisition, the creation of the reference standard and the evaluation measures. This framework has been introduced at the MICCAI 2009 workshop 3D Segmentation in the Clinic: A Grand Challenge III, and we compare the results of eight teams that participated. These results show that automated segmentation of the vessel lumen is possible with a precision that is comparable to manual annotation. The framework is open for new submissions through the website http://cls2009.bigr.nl.

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We present a new non-uniform sampling method for the accurate estimation of mutual information in multi-modal brain image rigid registration. Most existing density estimators used for mutual information computation incorrectly assume that the intensity of each voxel is independent from its neighborhood. Our method uses the 3D Fast Discrete Curvelet Transform to reduce the sampled voxels' interdependency by sampling voxels that are less dependent on their neighborhood, and thus provide a more accurate estimation of the mutual information and a more accurate registration. The main advantages of our method over other non-uniform sampling schemes are that: (1) it provides more accurate estimation of the image statistics with fewer samples; (2) it is less sensitive to the variability of anatomical structures shapes, orientations, and sizes, and; (3) it yields more accurate registration results. Extensive evaluation on 1000 synthetic registrations between T1 and T2-weighted clinical MRI images and 20 real clinical registrations of brain CT images to Proton Density (PD) and T1 and T2-weighted MRI images from the public RIRE database show the effectiveness of our method. Our method has the lowest mean registration errors recorded to date for CT-MR image registration in the RIRE website for methods tested on more than five datasets. These results indicate that our sampling scheme can be used to achieve more accurate multi-modal registration required for image guided therapy and surgery.

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We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.

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We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and metastatic lesions from abdominal CTA scans. The method repeatedly applies multi-resolution, multi-class smoothed Bayesian classification followed by morphological adjustment and active contours refinement. It uses multi-class and voxel neighborhood information to compute an accurate intensity distribution function for each class. The method requires only one or two user-defined voxel seeds, with no manual adjustment of internal parameters. A retrospective study on two validated clinical datasets totaling 56 CTAs was performed. We obtained correlations of 0.98 and 0.99 with a manual ground truth liver volume estimation for the first and second databases, and a total score of 67.87 for the second database. These results suggest that our method is accurate, efficient, and robust to seed selection compared to manually generated ground truth segmentation and to other semi-automatic segmentation methods.

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This paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fMRI images. The method uses fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from T2*-W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model is build to differentiate between tumors and healthy liver tissues. To validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. Our experimental results on mice yielded an accuracy of 78% with high precision (88%). This suggests that the method can provide a useful aid for early detection of liver metastases.

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This paper describes a novel image-guided system for precise automatic targeting in minimally invasive keyhole neurosurgery. The system consists of the MARS miniature robot fitted with a mechanical guide for needle, probe or catheter insertion. Intraoperatively, the robot is directly affixed to a head clamp or to the patient's skull. It automatically positions itself with respect to predefined targets in a preoperative CT/MRI image following an anatomical registration with an intraoperative 3D surface scan of the patient's facial features and registration jig. We present the system architecture, surgical protocol, custom hardware (targeting and registration jig), and software modules (preoperative planning, intraoperative execution, 3D surface scan processing, and three-way registration). We also describe a prototype implementation of the system and in vitro registration experiments. Our results indicate a system-wide target registration error of 1.7 mm (standard deviation = 0.7 mm), which is close to the required 1.0-1.5 mm clinical accuracy in many keyhole neurosurgical procedures.

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This paper present a novel image-guided system for precise automatic targeting in keyhole minimally invasive neurosurgery. The system consists of a miniature robot fitted with a mechanical guide for needle/probe insertion. Intraoperatively, the robot is directly affixed to a head clamp or to the patient skull. It automatically positions itself with respect to predefined targets in a preoperative CT/MRI image following an anatomical registration with a intraoperative 3D surface scan of the patient facial features. We describe the preoperative planning and registration modules, and an in-vitro registration experiment of the entire system which yields a target registration error of 1.7 mm (std = 0.7 mm).

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A child who aspirates a foreign body may present in many ways. We present the case of an 8-year-old boy whose initial presentation consisted only of his preoccupation with his lost toy and his gesturing toward his oral cavity. There was no witnessed coughing or choking episode, and there were no initial physical examination findings of note, and no abnormalities on appropriate radiographic studies. The patient was transferred to our institution for further evaluation and management. Our careful examination revealed a slight wheeze at the left upper lobe. Endoscopy was immediately performed, and bronchoscopy revealed a yellow, tubular, plastic foreign body in the left main bronchus through which the patient was breathing freely. This was removed without incident by means of optical graspers. The physical characteristics of this foreign body, including its small size, relatively inert material, and large lumen, allowed this patient to present relatively asymptomatically. This case demonstrates the need for a high index of suspicion in the evaluation and management of foreign bodies in the aerodigestive tract.

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We estimate a reduced-form bivariate probit model to analyse jointly the choice of ambulatory treatment from the specialty mental health sector and/or the use of psychotropic drugs for a nationally representative sample of US household residents. We find significant differences in treatment choice by education, gender, race and ethnicity, while controlling for several aspects of self-reported mental health and treatment attitudes. For example, while women are more likely than men to use the specialty mental health sector and more likely to take psychotropic medications, this difference between men and women is much greater for psychotropic medications. The estimated differences may reflect patient preferences in a manner traditionally assumed when interpreting these coefficients in such equations, but we discuss how they may also reflect biases and misperceptions on the parts of patients and providers. We also discuss how our results relate to some findings and policies in the general health care sector.

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Ovarian function in the cycling female is intimately related to and dependent upon significant microvascular regulation and restructuring. To enable investigation of the microvascular determinants of ovarian function, we present an in vivo preparation of the golden hamster ovary. The preparation does not compromise the ovarian vascular supply. The viability and responsiveness of the preparation were confirmed by quantifying arteriolar responses to vasoactive agents in 17 hamsters. Small surface arterioles (mean diameter 15-16 microns) responded with statistically significant changes in diameter to adenosine and oxygen and showed significant, dose-dependent constriction in response to norepinephrine and the NO synthase inhibitor L-NAME. Other key findings included extremely high microvascular permeability that varied with the day of the estrous cycle and functionally significant architectural features of the utero-ovarian vascular network. Potential applications of the preparation include elucidation of the role of the microvasculature in follicular development and luteal regression, investigation of utero-ovarian crossregulation, and development of a model for the study of ovarian angiogenesis and vascular regression.

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OBJECTIVE: To analyze differences in the determination of healthcare expenditures among racial/ethnic groups. DATA SOURCE: The 1987 National Medical Expenditure Survey, a nationally representative sample of the United States population. (Nomenclature reflects racial/ethnic categories as used in the Survey.) STUDY DESIGN: The design was to estimate completely separate demand systems for blacks, Hispanics, and whites, perform statistical tests for the appropriateness of such separation, and carry out various simulations of healthcare expenditures. DATA COLLECTION/EXTRACTION METHODS: All black, white, and Hispanic persons in the 1987 NMES Household Survey were used in this analysis. PRINCIPAL FINDINGS: Several of the differences among the equations for the three racial/ethnic groups appear to be related to access to care, particularly between Hispanics and whites, and to a lesser degree between blacks and whites. Simulations indicated that most of the differences in healthcare spending were due on net to differences in characteristics of the sampled persons and their environments. However, for Hispanics relative to both blacks and whites, some of the differences in total expenditures were also due to differences in the behavior embodied in the equations. CONCLUSIONS: It would be inadequate, and possibly misleading, to allow for differences in health expenditures by simply including dummy variables for blacks, Hispanics, and/or whites in pooled equations estimated for the entire sample. Studies that allow one to analyze the institutional and behavioral aspects of healthcare spending in greater detail are needed for a better understanding of these racial/ethnic differences.

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The use of home care by cancer patients over the course of a year was analyzed using a two-part model that estimated: (1) the probability of any use, and (2) the quantity of visits given some use. The findings support the use of a two-stage model for estimating home care over single equation approaches. We found that while HMO membership increased the probability of some home care for cancer, it resulted in a smaller number of visits given some use. Health care coverage was also found to have different effects on these two components of total use. Some implications of some of these findings for future policies are discussed.

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OBJECTIVE: To analyze a comprehensive multivariate model of the use of mental health-related ambulatory care services by children ages 6-17. STUDY SETTING: The 1987 National Medical Expenditure Survey, a national probability sample of the U.S. civilian noninstitutionalized population. STUDY DESIGN: A cross-sectional survey of a national probability sample of the U.S. population. Key independent variables include person-level mental health status, health care coverage, family income, and use of mental health services by other family members. DATA COLLECTION: Four in-person interviews were conducted during 1987 using structured questionnaires. A designated family respondent was used to answer questions for other family members, including children. PRINCIPAL FINDINGS: Children with poor mental health in high-income families were more than three times as likely to have a mental health-related visit than children with poor mental health in low-income families. The number of mental health-related visits and the likelihood of seeing a mental health specialist also increased along with family income. Mental health use by other family members was strongly associated with use. CONCLUSIONS: The results from this study provide strong evidence that the socioeconomic status of children is an important factor in explaining unmet need for mental health services.

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National estimates are provided, for the first time, of the number of hospitalizations in a year for elderly persons who also experience some nursing home use, and patterns for this interaction are described. In 1987, 816,000 persons were transferred from nursing homes to hospitals, constituting 8.5 percent of all Medicare hospital admissions for persons ages 65 and older. Another 347,000 hospital stays involved people admitted from the community and discharged to a nursing home. The reporting of discharge destination on Medicare hospital bill data in 1987 also is analyzed. It was found that these data may have underreported a nursing home as the destination by between 15 and 20 percent. The magnitude of hospitalizations of nursing home residents suggests that programs aimed at improving nursing home care might have an important impact on total days of hospital care, and that it is important to learn more about the optimal use of expensive hospital care.

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Little national data have been available to guide the design of programs aimed at reducing the hospitalization of nursing home residents. This article uses the 1987 National Medical Expenditure Survey to identify elderly nursing home residents with an elevated risk of hospitalization and the reasons for and outcomes of residents' hospital stays. Study findings include an elevated risk of hospitalization for residents with one of several different primary diagnoses and a rise in risk as ADL dependence increases. An infection was the main medical reason for roughly 27% of hospital stays. The results suggest possible target groups for two types of programs aimed at reducing hospitalization.

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