[Imaging diagostics of geographic atrophy]

The development of imaging technologies has contributed to the understanding of the genesis and pathophysiological mechanisms of geographic atrophy (GA) secondary to age-related macular degeneration (AMD). Fundus autofluorescence (FAF) imaging allows accurate discrimination of the boundaries of atrophic patches. Furthermore, predictive markers for disease progression can be identified. Non-invasive FAF imaging now represents the gold standard for evaluating progressive enlargement of atrophic areas. By means of high resolution optical coherence tomography (OCT) microstructural retinal changes in GA can be identified. Anatomical endpoints are now being used in interventional GA trials and represent meaningful outcome parameters as surrogate markers in an overall slowly progressive disease which may not affect the fovea until later stages of the disease.

Computed tomography angiography (CTA) to prove circulatory arrest for the diagnosis of brain death in the context of organ transplantation

For the determination of brain death (BD) in potential organ donors, confirmatory tests that show cessation of cerebral circulation are used in many countries. Conventional angiography is considered the golden standard among these ancillary examinations. In recent years other angiographic techniques such as CT angiography (CTA) have been increasingly employed to establish the diagnosis of BD. We report our experience with CTA in this setting.

Sustained impact of electronic alerts on rate of prophylaxis against venous thromboembolism

Advanced electronic alerts (eAlerts) and computerised physician order entry (CPOE) increase adequate thromboprophylaxis orders among hospitalised medical patients. It remains unclear whether eAlerts maintain their efficacy over time, after withdrawal of continuing medical education (CME) on eAlerts and on thromboprophylaxis indications from the study staff. We analysed 5,317 hospital cases from the University Hospital Zurich during 2006-2009: 1,854 cases from a medical ward with eAlerts (interventiongroup) and 3,463 cases from a surgical ward without eAlerts (controlgroup). In the intervention group, an eAlert with hospital-specific venous thromboembolism (VTE) prevention guidelines was issued in the electronic patient chart 6 hours after admission if no pharmacological or mechanical thromboprophylaxis had been ordered. Data were analysed for three phases: pre-implementation (phase 1), eAlert implementation with CME (phase 2), and post-implementation without CME (phase3). The rates of thromboprophylaxis in the intervention group were 43.4% in phase 1 and 66.7% in phase 2 (p<0.001), and increased further to 73.6% in phase3 (p=0.011). Early thromboprophylaxis orders within 12 hours after admission were more often placed in phase 2 and 3 as compared to phase 1 (67.1% vs. 52.1%, p<0.001). In the surgical control group, the thromboprophylaxis rates in the three phases were 88.6%, 90.7%, 90.6% (p=0.16). Advanced eAlerts may provide sustained efficacy over time, with stable rates of thromboprophylaxis orders among hospitalised medical patients.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Multi-Scale 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 multi-scale, multi-physics 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 atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Toward optimal early management after whiplash injury to lessen the rate of transition to chronicity: discussion paper 5

Expert debate and synthesis of research to inform future management approaches for acute whiplash disorders.

Comparison of C-11 methionine and C-11 choline for PET imaging of brain metastases: a prospective pilot study

The aim of the study was the comparison of C-11 methionine (MET) and C-11 choline (CHO) in the positron emission tomography (PET) imaging of brain metastases in correlation to the histopathology findings in stereotactic biopsy.

The Financial Rate of Profit: What Is It, and How Has It Behaved in the United States?

This work contributes to the almost nonexistent literature on the profit rate of the financial sector. It updates the single study to include financial variables to cover the past decade, compares this profit rate to the (almost unpublished) Weisskopf and NIPA financial profit rates, compares the financial and nonfinancial sector rates, and details the procedure to construct the profit rate in the financial sector including relevant financial variables which capitalists consider to make profit-rate decisions. JEL Classification: B50, E11