Search

Image registration plays a vital role in understanding changes that occur in 2D and 3D scientific imaging datasets. Registration involves finding a spatial transformation that aligns one image to another by optimizing relevant image similarity metrics. In this paper, we introduce itk-elastix, a userfriendly Python wrapping of the mature elastix registration toolbox. The open-source tool supports rigid, affine, and B-spline deformable registration, making it versatile for various imaging datasets. By utilizing the modular design of itk-elastix, users can efficiently configure and compare different registration methods, and embed these in image analysis workflows. The current poster is presented in SciPy 2023 conference. &nbsp

Additional file 3: Table S1. 24-h cough monitoring: Cough count [1/hour]. Table S2. 24-h cough frequency: Subgroup analyses by baseline cough count (Bayesian mixed model). Table S3. 24-h cough frequency: Responder rates for different change thresholds. Table S4. VAS cough severity: Responder rates. Table S5. Treatment-emergent adverse events observed in > 5% of patients. Table S6. Cough frequency: Subgroup analyses by occurrence of taste-related adverse events (Bayesian mixed model). Table S7. Cough severity (VAS): Subgroup analyses by occurrence of taste-related adverse events (Bayesian mixed model). Figure S1. Plasma concentration–time curves for filapixant after multiple administrations.

Additional file 1. Inclusion/exclusion criteria.

To study whether it is possible to differentiate intermediate age-related macular degeneration (AMD) from healthy controls using partial optical coherence tomography (OCT) data, that is, restricting the input B-scans to certain pre-defined regions of interest (ROIs). A total of 15744 B-scans from 269 intermediate AMD patients and 115 normal subjects were used in this study (split on subject level in 80% train, 10% validation and 10% test). From each OCT B-scan, three ROIs were extracted: retina, complex between retinal pigment epithelium (RPE) and Bruch membrane (BM), and choroid (CHO). These ROIs were obtained using two different methods: masking and cropping. In addition to the six ROIs, the whole OCT B-scan and the binary mask corresponding to the segmentation of the RPE-BM complex were used. For each subset, a convolutional neural network (based on VGG16 architecture and pre-trained on ImageNet) was trained and tested. The performance of the models was evaluated using the area under the receiver operating characteristic (AUROC), accuracy, sensitivity, and specificity. All trained models presented an AUROC, accuracy, sensitivity, and specificity equal to or higher than 0.884, 0.816, 0.685, and 0.644, respectively. The model trained on the whole OCT B-scan presented the best performance (AUROC = 0.983, accuracy = 0.927, sensitivity = 0.862, specificity = 0.913). The models trained on the ROIs obtained with the cropping method led to significantly higher outcomes than those obtained with masking, with the exception of the retinal tissue, where no statistically significant difference was observed between cropping and masking (p = 0.47). This study demonstrated that while using the complete OCT B-scan provided the highest accuracy in classifying intermediate AMD, models trained on specific ROIs such as the RPE-BM complex or the choroid can still achieve high performance.

Additional file 2. Analytical methods.

Background: Early screening of the brain is becoming routine clinical practice. Currently, this screening is performed by manual measurements and visual analysis, which is time-consuming and prone to errors. Computational methods may support this screening. Hence, the aim of this systematic review is to gain insight into future research directions needed to bring automated early-pregnancy ultrasound analysis of the human brain to clinical practice. Methods: We searched PubMed (Medline ALL Ovid), EMBASE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar, from inception until June 2022. This study is registered in PROSPERO at CRD42020189888. Studies about computational methods for the analysis of human brain ultrasonography acquired before the 20th week of pregnancy were included. The key reported attributes were: level of automation, learning-based or not, the usage of clinical routine data depicting normal and abnormal brain development, public sharing of program source code and data, and analysis of the confounding factors. Findings: Our search identified 2575 studies, of which 55 were included. 76% used an automatic method, 62% a learning-based method, 45% used clinical routine data and in addition, for 13% the data depicted abnormal development. None of the studies shared publicly the program source code and only two studies shared the data. Finally, 35% did not analyse the influence of confounding factors. Interpretation: Our review showed an interest in automatic, learning-based methods. To bring these methods to clinical practice we recommend that studies: use routine clinical data depicting both normal and abnormal development, make their dataset and program source code publicly available, and be attentive to the influence of confounding factors. Introduction of automated computational methods for early-pregnancy brain ultrasonography will save valuable time during screening, and ultimately lead to better detection, treatment and prevention of neuro-developmental disorders. Funding: The Erasmus MC Medical Research Advisor Committee (grant number: FB 379283).

Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and the types of output they provide. We will focus on specific use cases for diagnosis, i.e. estimating the current 'condition' of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumours, and decision making in stroke. Regarding prediction, i.e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer. Furthermore, based on these use cases, we will assess the current state-of-the-art methodology and highlight current efforts on benchmarking of these methods and the importance of open science therein. Finally, we assess the current clinical impact of computer-aided methods and discuss the required next steps to increase clinical impact.

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning- based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers.

The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods. In this work, we present a framework for the evaluation of growth predictions that focuses on the spatial infiltration patterns, and specifically evaluating a prediction of future growth. We propose to frame the problem as a ranking problem rather than a segmentation problem. Using the average precision as a metric, we can evaluate the results with segmentations while using the full spatiotemporal prediction. Furthermore, by separating the model goodness-of-fit from future predictive performance, we show that in some cases, a better fit of model parameters does not guarantee a better the predictive power.

Supplemental material, sj-pdf-2-ijr-10.1177_0278364920961452 for A hip–knee–ankle exoskeleton emulator for studying gait assistance by Gwendolyn M Bryan, Patrick W Franks, Stefan C Klein, Robert J Peuchen and Steven H Collins in The International Journal of Robotics Research

Supplemental material, sj-pdf-2-ijr-10.1177_0278364920961452 for A hip–knee–ankle exoskeleton emulator for studying gait assistance by Gwendolyn M Bryan, Patrick W Franks, Stefan C Klein, Robert J Peuchen and Steven H Collins in The International Journal of Robotics Research

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers., Comment: SPIE Medical Imaging 2021 (oral)

To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N=8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time., Comment: MICCAI 2019 (oral presentation)

Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM = 0.866; R2DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain., Highlights • Data-driven event sequences describe evolution of relevant biomarkers in AD. • Agreement between event sequences and heuristic AD progression models • Accuracy in classifying subjects from clinical cohorts up to 91% • Staging of subjects and MMSE scores of individuals show linear relation. • Transferability of AD progression models based on research data to clinical cohorts

Missing data is a common problem in machine learning and in retrospective imaging research it is often encountered in the form of missing imaging modalities. We propose to take into account missing modalities in the design and training of neural networks, to ensure that they are capable of providing the best possible prediction even when multiple images are not available. The proposed network combines three modifications to the standard 3D UNet architecture: a training scheme with dropout of modalities, a multi-pathway architecture with fusion layer in the final stage, and the separate pre-training of these pathways. These modifications are evaluated incrementally in terms of performance on full and missing data, using the BraTS multi-modal segmentation challenge. The final model shows significant improvement with respect to the state of the art on missing data and requires less memory during training., Comment: Accepted at MICCAI MIL3ID workshop 2019

When finetuning a convolutional neural network (CNN) on data from a new domain, catastrophic forgetting will reduce performance on the original training data. Elastic Weight Consolidation (EWC) is a recent technique to prevent this, which we evaluated while training and re-training a CNN to segment glioma on two different datasets. The network was trained on the public BraTS dataset and finetuned on an in-house dataset with non-enhancing low-grade glioma. EWC was found to decrease catastrophic forgetting in this case, but was also found to restrict adaptation to the new domain.

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study or any other datasets to which they have access. Participants are then required to make monthly forecasts over a period of 5 years from January 2018, of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These individual forecasts are later compared with the corresponding future measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The first submission phase of TADPOLE was open for prize-eligible submissions between 15 June and 15 November 2017. The submission system remains open via the website: https://tadpole.grand-challenge.org, although since 15 November 2017 submissions are not eligible for the first round of prizes. This paper describes the design of the TADPOLE Challenge., Comment: For more details on TADPOLE Challenge, see https://tadpole.grand-challenge.org/ This paper outlines the design of the TADPOLE Challenge. Paper contains 8 pages, 2 figures, 5 tables

During the 2016 election campaign, President Donald J. Trump championed a policy of intervention and isolation in U.S. foreign policy, which aroused fears among European North Atlantic Treaty Organization (NATO) allies about U.S. commitments to collective defense. This contentious issue points to a preference in certain political circles for an end to U.S. interventionism, which, from a foreign policy perspective, seems tantamount to isolationism. This thesis examines isolationism in the experience of statecraft and considers the potential implications of this nation's deeply rooted isolationist tendencies for continued U.S. security commitments to NATO. To trace the evolution of isolationism, this thesis analyzes case studies of U.S. decision-making prior to entering the First and Second World Wars, U.S. involvement in NATO after the Second World War, and resistance within the Senate to large numbers of U.S. troops in Europe in the 1960s and 1970s. The analysis includes consideration of congressional partisanship, public opinion, and domestic political issues in the shaping of U.S. foreign policy. This thesis concludes that domestic political issues dominate the conduct of U.S. foreign policy. The political agenda of the 2016 Trump campaign, as well as the first days of the administration, mirrored debates of the past, which should not surprise experts of U.S. political history. http://archive.org/details/americfirstisola1094555635 Lieutenant Colonel, German Army Approved for public release; distribution is unlimited.

The core of the CORBEL common IT framework for genomics and imaging studies in biomarker research consists of the tranSMART (data integration and browsing platform), cBioportal (patient-level genomics platform) and XNAT (image archive platform) solutions. Loading existing project data into these platforms requires some level of data curation and transformation. This is particularly true for tranSMART, which is suited to handle a wide range of data types but also requires careful data modeling of those data before loading. The present document describes a detailed set of guidelines how to execute those data curation and modeling steps for each of the supported data types in tranSMART, cBioportal, and XNAT. It is partially based on previous work in the Dutch TraIT platform which was applied to the CORBEL use cases. Some practical lessons learned from those use cases, in particular the IMI APPROACH project, are given as well.

European e-Infrastructure projects are increasingly turning to Semantic Web[1] (SemWeb) technologies to address data integration challenges. This approach is proving to be a solution to some of the emerging challenges in the life sciences. The BioMedBridges semantic web pilot spans deliverables 4.4, 4.6, 4.7, and 4.8; its goal is to test the suitability of a semantic web approach to the task of integrating research data and to report on our experience of running an RDF-based platform integrating multiple data resources. In order to leverage experience where it exists and minimise the risks inherent in novel technology projects, a three-stage delivery was chosen for the pilot. As summarised below, these three stages are reported in separate deliverables. This deliverable D4.8 reports on the strategy, implementation and lessons learned for the semantic web pilots for BioMedBridges. By following this schedule and aligning partner-specific roadmaps to the blueprint delivered in Phase I (D4.4), the pilot projects were developed synchronously, allowing knowledge to be shared efficiently (Phases 2-3: D4.6-4.8). This enabled infrastructures to collaborate effectively and address common issues as they arose. In support of this effort, a knowledge-exchange workshop ran on the 29-30 April 2014 at TMF, Berlin, Germany. The programme and course materials are available at BioMedBridges website[2] and can be found in D4.7 Appendix 2 (‘Resources’), item 3 (‘SWAT4LS SemWeb training materials’). In December 2013 and December 2014, at SWAT4LS[3] and in May 2015 at an industry workshop, tutorials were delivered demonstrating the queries and analyses the RDF platform makes possible. A training course at the School of Computer Science, University of Manchester in December 2014 delivered a summary of the practicalities of working with and running an RDF platform, which summarised the technological approach and technology experience. All of the SemWeb pilot work was informed by the work of WP3, with respect to the choice and use of ontologies, as well as provision and re-use of identifiers, reflecting the application of standards derived from the use case work packages (e.g. WP7 and WP10). BioMedBridges played a major role at the CRI (Clinical Research Informatics) Day organised by ECRIN: "First CRI Solutions Day" (26-27 May 2014 at Heinrich-Heine University, Düsseldorf, Germany). The presentation about BioMedBridges (S. Suhr) was accompanied by presentations of software tools and hands-on sessions with their developers. Tools developed or employed in BioMedBridges (BBMRI Catalogue, CTIM, tranSMART, MOLGENIS / BiobankConnect, XNAT) and the approaches for data sharing of BioMedBridges could be compared with those of many other Research Infrastructures and EU projects, such as EATRIS, BBMRI, ECRIN, BioSHaRE, TRANSFoRm, EHR4CR, and p-medicine. The first impression of participants at the CRI Solutions Day was that EU projects often seem to cope with similar problems and have developed similar solutions. For example, all domains struggle with the same rigid conditions for data protection and the challenge of semantic interoperability. It was suggested that it might be better to bring projects together and to work jointly on software solutions to common problems. Especially in the area of semantic interoperability and legally compliant data sharing, BioMedBridges has developed generic solutions that may be of help for other projects. A workshop on translational research infrastructure including tools like XNAT, tranSMART, MOLGENIS, OpenClinica and Galaxy and the interfaces between them, co-organised by Dutch representatives of BBMRI, EuroBioImaging, and EATRIS, was held at the OpenBridges symposium (see section 3.2.1.2, Pilots 1 through 3). Materials such as presentations, documentation on discussions relating to the OpenClinica-tranSMART and tranSMART-Galaxy connections, as well as XNAT at the Dutch DTL Programmers Meeting are available elsewhere[4]. Various other outreach and dissemination activities have taken place during the course of BioMedBridges WP4, and are detailed within this report, in the relevant sections. While many of the most refined pilots (below) are centred on the use of RDF, it is not the only appropriate solution for data integration; our experience indicated that certain kinds of data are better suited to different distribution and integration mechanisms. Therefore, this report covers an array of solutions that achieve integration, including RDF-based, as well as those that provide integration points for future efforts; D4.6 included both RDF-based and pilots using alternative methodologies, D4.7 was targeted specifically at judging the suitability and scalability of a purely RDF-centric data integration solution, with other deliverables providing various interfaces to data (REST, widgets, GUIs). Here we summarize our processes, products, and lessons learnt during the execution of this work package. We also describe the further work that has been conducted in the period intervening D4.6 and this report (D4.8), as well as detailing the sustainability of these pilot activities, and lessons learnt throughout the entirety of WP4. [1] https://wiki.dtls.nl/index.php/DTL_Programmers_Meeting [2] http://www.biomedbridges.eu/trainings/knowledge-exchange-workshop-resource-description-framework-rdf [3] http://www.swat4ls.org/workshops/berlin2014/ [4] http://www.w3.org/standards/semanticweb/

SPIRIT checklist. (DOC 121Â kb)

European e-Infrastructure projects are increasingly turning to Semantic Web technologies to address data integration challenges. This approach is proving to be a solution to some of the emerging challenges in the life sciences. The BioMedBridges semantic web pilot spans deliverables 4.4, 4.6, 4.7, and 4.8; its goal is to test the suitability of a semantic web approach to the task of integrating research data and to report on our experience of running an RDF-based platform integrating multiple data resources. In order to leverage experience where it exists and minimise the risks inherent in novel technology projects, a three-stage delivery was chosen for the pilot. These stages are reported on in separate deliverables. By following this schedule and aligning partner-specific roadmaps to the blueprint delivered in Phase I (D4.4), the pilot projects can be developed synchronously and knowledge shared efficiently (Phases 2-3: D4.6-4.8). This enables infrastructures to collaborate effectively and address common issues. To support this effort, a knowledge-exchange workshop ran on the 29-30 April 2014 at TMF, Berlin, Germany. In December 2013 and December 2014, at SWAT4LS and May 2015 at an industry workshop, we delivered tutorials during which we demonstrated the queries and analyses the RDF platform makes possible. We provided a training course to Computer Scientists in Manchester on the practicalities of working with, and running the RDF platform and summarised our technological approach and technology experience. All of the SemWeb pilot work is informed by the work of WP3 on choice and use of ontologies as well as provision and re-use of identifiers and reflects the application of standards in the use case work package. However, RDF is not a one-size-fits-all technology; and our experience is that certain kinds of data are better suited to different distribution and integration mechanisms. D4.6 therefore includes some pilots that are not directly related to RDF. Here we summarize our processes, products, and lessons learned and identify future work for D4.8.

This deliverable consists of five pilot studies which integrate and visualise data from across the BioMedBridges domains and the biomedical sciences research infrastructures involved. It builds on the previous WP deliverables and activities (D4.1 to D4.4). The pilots described in this report have varied used cases and cover the following areas: Sharing medical imaging data Visualising and leveraging ontologies in queries Connectivity-based searching for drugs in UniChem Integrating gene and drug information with a clinical trials registry Sharing and visualising sequencing data from environmentally-derived biological samples.

This report on the BioMedBridges security framework covers a complete series of steps from requirements gathering (identified by means of the two surveys and based upon requirement clusters that are explained in the report to deliverable 5.1) to data flow diagrams and threats (constructed from results of the second survey) to risks, further to countermeasures, and finally towards the architectural elements of the proposed security framework. An important characteristic of the security architecture described here is its bottom-up development: process elements and descriptions are based on feedback from the biomedical sciences research infrastructures involved in the respective BioMedBridges use cases. Specifications in the form of activity diagrams (deliverable 5.1) and data flow diagrams (this deliverable report) were developed in direct interaction with representatives of the use case work packages. Domain specific (EGA) and domain independent (EGI) experience was sought for the definition of security specific process elements.

Die derzeit geltende gewerbesteuerliche Behandlung von Leasingverträgen ist im Zuge verschiedener Reformvorhaben angetastet worden. Im Mittelpunkt stand jeweils die Einführung einer so genannten Leasingsteuer bzw. einer modernisierten Gewerbesteuer, die zu einer Besteuerung von Leasingraten auf Ebene des Leasingnehmers führen sollte. Teil I der Untersuchung zeigt die steuerlichen Wirkungszusammenhänge. Ziel der Einführung einer Leasingsteuer sollte es sein, angebliche Steuervergünstigungen von Leasing im Vergleich zum Kreditkauf zu neutralisieren. Bei einem sachgerechten Vergleich von Leasing und Kreditkauf zeigt sich, dass das geltende Steuerrecht Leasing gegenüber Kreditkauf keineswegs bevorzugt. Die Abzugsfähigkeit der Leasingraten von der Bemessungsgrundlage der Gewerbesteuer bewirkt lediglich eine Steuerverlagerung zum Leasinggeber, insgesamt aber keine Steuerersparnis. Ferner werden die steuerlichen Wirkungen des Steuervergünstigungsabbaugesetzes (StVergAbG) und des Vorschlages der Bundesvereinigung der kommunalen Spitzenverbände aufgezeigt sowei anhand von Fallbeispielen die Wirkungen der gegenwärtigen Regelung verglichen. Bei beiden Reformvorschlägen wird Leasing gegenüber dem Kreditkauf steuerlich erheblich benachteiligt. Teil II setzt sich mit den Auswirkungen einer steuerlichen Benachteiligung des Leasing auf die Finanzierung deutscher Unternehmen, insbesondere des Mittelstandes, auseinander. Im Anschluss an eine Analyse derzeitiger Finanzierungsprobleme deutscher Unternehmen wird die Auswirkung einer steuerlich bedingten Verteuerung von Leasing auf die Finanzierungskosten und die Investitionsbereitschaft der Unternehmen erörtert. Die Auswirkungen von Basel II finden hierbei besondere Berücksichtigung. Es ist davon auszugehen, dass ein Rückgang des Leasing nicht durch vermehrte Kreditfinanzierung vollständig ausgeglichen werden kann, so dass ein Rückgang der Investitionstätigkeit unvermeidbar ist. Teil III betrachtet die volkswirtschaftlichen Auswirkungen der Einführung einer Leasingsteuer im Hinblick auf gesamtwirtschaftliche Investitionen und Beschäftigung. Ökonometrische Berechnungen zeigen, dass eine Zunahme der Arbeitslosenzahl in der Größenordnung von 100.000 bis 200.000 möglich ist, wenn Leasing steuerlich stärker belastet wird.

Purpose: To introduce a semiautomatic algorithm to perform the registration of free-hand B-Mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid artery. Methods: The authors' approach combines geometrical features and intensity information. The only user interaction consists of placing three seed points in US and MRI. First, the lumen centerlines are used as landmarks for point based registration. Subsequently, in a joint optimization the distance between centerlines and the dissimilarity of the image intensities is minimized. Evaluation is performed in left and right carotids from six healthy volunteers and five patients with atherosclerosis. For the validation, the authors measure the Dice similarity coefficient (DSC) and the mean surface distance (MSD) between carotid lumen segmentations in US and MRI after registration. The effect of several design parameters on the registration accuracy is investigated by an exhaustive search on a training set of five volunteers and three patients. The optimum configuration is validated on the remaining images of one volunteer and two patients. Results: On the training set, the authors achieve an average DSC of 0.74 and a MSD of 0.66 mm on volunteer data. For the patient data, the authors obtain a DSC of 0.77 and a MSD of 0.69 mm. In the independent set composed of patient and volunteer data, the DSC is 0.69 and the MSD is 0.87 mm. The experiments with different design parameters show that nonrigid registration outperforms rigid registration, and that the combination of intensity and point information is superior to approaches that use intensity or points only. Conclusions: The proposed method achieves an accurate registration of US and MRI, and may thus enable multimodal analysis of the carotid plaque. (C) 2014 American Association of Physicists in

Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86-91%) than all other approaches (AUC = 57-84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself. (C) 2014 Wiley Periodicals, Inc.

A method for registering preoperative 3D + t coronary CTA with intraoperative monoplane 2D + t X-ray angiography images is proposed to improve image guidance during minimally invasive coronary interventions. The method uses a patient-specific dynamic coronary model, which is derived from the CTA scan by centerline extraction and motion estimation. The dynamic coronary model is registered with the 2D + t X-ray sequence, considering multiple X-ray time points concurrently, while taking breathing induced motion into account. Evaluation was performed on 26 datasets of 17 patients by comparing projected model centerlines with manually annotated centerlines in the X-ray images. The proposed 3D + t/2D + t registration method performed better than a 3D/2D registration method with respect to the accuracy and especially the robustness of the registration. Registration with a median error of 1.47 mm was achieved.

Die Keimbildung in tief unterkühlten Schmelzen von reinem Zirkonium und Zr-Legierungen wurde experimentell untersucht. Es wurden Methoden zum behälterfreien Prozessieren (elektromagnetische (EML) und elektrostatische Levitation (ESL)) verwendet und die erzielten Ergebnisse zur Keimbildung verglichen. Die Absenz von heterogenen Keimstellen an Tiegelwänden ermöglicht tiefe Unterkühlungen. Im ESL gelang es erstmals, die kritische Grenze des Hypercoolings zu überschreiten, so dass die gesamte Metallschmelze im Nicht-Gleichgewicht erstarrte. Für reines Zr gelang es, die physikalische Grenze der Unterkühlbarkeit (homogenen Keimbildung) zu erreichen. Dies ermöglichte eine Abschätzung der Grenzflächenenergie, die darauf hinweist, dass Voraussagen von Simulationsrechnungen diese Energie unterschätzt. Die Grenzflächenenergie ist durch Nahordnung der Flüssigkeit sowie Struktur der festen Phase bestimmt. Die Nahordnung einer Zr-Pd Schmelze wurde durch Röntgenstreuung am APS in Chicago untersucht.