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For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.

Non-rigid registration is essential for a wide range of clinical applications, such as intraoperative image-guidance and postoperative follow-up assessment, and longitudinal image analysis for disease diagnosis and monitoring. Vascular structures are a rich descriptor of the organ deformation, since it permeates through all organs within body. As vasculature differs in size, shape and topology, following surgical intervention/treatment or due to disease progression, non-rigid vessel matching remains a challenging task. Recently, hybrid mixture models (HdMM) have been applied to tackle this challenge, and demonstrate significant improvements in terms of accuracy and robustness relative to the state-of-the-art. However, the smoothness constraint enforced on the deformation field with this approach only accounts for the global topology of the vasculature, resulting in a reduced capacity to accurately match localized changes to vascular structures, and preserve local topology. In this work, we proposed a modified version of HdMM by formulating an adaptive kernel, to enforce a local smoothness constraint on the deformation field, henceforth referred to as HdMMad. The proposed HdMMad framework is evaluated with cerebral and pulmonary vasculature, acquired retrospectively. The registration results for both data sets demonstrate that the proposed approach outperforms registration algorithms also designed to preserve local topology. Using HdMMad, around 80% of the initial registration error was reduced, for both data sets.

In the runup to the ILO's 100th anniversary in 2019, the ILO asked the Nordic countries to contribute to the debate about how the future of work can be shaped. the Stockholm conference gathered mor ...

The prominent soft tissue deformation during a neurosurgical procedure, the so called brain shift phenomenon, affects the accuracy of the surgery greatly. Although the feasibility of numerous intraoperative modalities is investigated for the brain shift compensation, another state-of-the-art interventional imaging modality C-Arm CT is rarely studied in this context. Due to the lack of clinical data, a suitable anthropomorphic deformable phantom is indispensable. The purpose of this study is to describe and determine the characteristics of a multi-modal deformable brain phantom made out of polyurethane and ceramic composite bone material. The phantom is made of six parts: skin, skull, brain, blood vessels, ventricles and an inflatable tumor. Fluid can be filled into the blood vessels, the ventricles and the tumor in order to simulate the brain shift phenomenon. Such an anthropomorphic phantom is both visible with MR and C-Arm CT scanner. In order to evaluate the clinical relevance of our phantom, we induce deformations with different degrees by in- and deflated the embedded tumor in 5 steps. Both MR and C-Arm CT (cone beam CT) images are acquired in each step. The induced deformation is estimated by performing mono-modal deformable registration with symmetric normalization method (SyN) both on MR and cone beam CT (CBCT) acquisitions. The maximum displacement estimated with MR and CBCT pair is 29.6mm and 28.1mm. This result correlates the clinical findings.

During a neurosurgical procedure, the exposed brain undergoes an elastic deformation caused by numerous factors. This deformation, also known as brain shift, greatly affects the accuracy of neuronavigation systems. Non-rigid registration methods based on point matching algorithms are frequently used to compensate for intraoperative brain shift, especially when anatomical structures such as cerebral vascular tree are available. In this work, we introduce a pipeline to compensate for the volumetric brain deformation with Cone Beam CT (CBCT) image data. Point matching algorithms are combined with Spline-based transforms for this purpose. The initial result of different combination is evaluated with synthetical image data.

Brain deformation (or brain shift) during neurosurgical procedures such as tumor resection has a significant impact on the accuracy of neuronavigation systems. Compensating for this deformation during surgery is essential for effective guidance. In this paper, we propose a method for brain shift compensation based on registration of vessel centerlines derived from preoperative C-Arm cone beam CT (CBCT) images, to intraoperative ones. A hybrid mixture model (HdMM)-based non-rigid registration approach was formulated wherein, Student’s t and Watson distributions were combined to model positions and centerline orientations of cerebral vasculature, respectively. Following registration of the preoperative vessel centerlines to its intraoperative counterparts, B-spline interpolation was used to generate a dense deformation field and warp the preoperative image to each intraoperative image acquired. Registration accuracy was evaluated using both synthetic and clinical data. The former comprised CBCT images, acquired using a deformable anthropomorphic brain phantom. The latter meanwhile, consisted of four 3D digital subtraction angiography (DSA) images of one patient, acquired before, during and after surgical tumor resection. HdMM consistently outperformed a state-of-the-art point matching method, coherent point drift (CPD), resulting in significantly lower registration errors. For clinical data, the registration error was reduced from 3.73 mm using CPD to 1.55 mm using the proposed method.

Resume Les indicateurs associes aux objectifs du Millenaire pour le developpement presentent plusieurs lacunes: des problemes de mesure et d'interpretation, l'utilisation de donnees agregees et des hypotheses inadaptees aux pays en developpement. Les auteurs proposent par consequent quatre nouveaux indicateurs de l'emploi productif et du travail decent: l'augmentation de la part du travail dans la valeur ajoutee par těte, le taux de travailleurs pauvres et la proportion de travailleurs qui percoivent un revenu inferieur a un minimum donne, fixe en valeur absolue d'abord puis a 60 pour cent du revenu du travail median. Deux cas d'espece – Ouganda et Perou – illustrent l'application pratique de ces indicateurs.

The aim of this paper is to contribute to the ongoing discussion about the design of a post‐ 2015 development framework by proposing indicators to monitor employment outcomes. Our analysis of the current MDG employment indicators shows that measurement prob‐ lems, the inappropriate use of aggregate statistics, ambiguous interpretability, and as‐ sumptions that often do not hold true in the context of developing countries are major shortcomings of the current indicators. Based on this critique, we develop a new set of in‐ dicators for productive employment and decent work. We propose four indicators: (i) the growth of labor value added per worker, (ii) the working poverty rate, (iii) (a) the share of workers receiving less than an absolute labor income and (b) the share of workers receiv‐ ing less than 60 percent of the median labor income. We demonstrate the empirical appli‐ cation of these indicators using the country cases of Uganda and Peru.

Los indicadores actuales de la ODM sobre empleo presentan deficiencias importantes, como problemas de medicion, uso inadecuado de estadisticas agregadas, ambiguedad interpretativa y supuestos de base no aplicables a los paises en desarrollo. Tratando de superar estos problemas, los autores proponen cuatro nuevos indicadores de empleo productivo y trabajo decente: el crecimiento de la contribucion del trabajo al valor anadido por trabajador, la tasa de trabajadores pobres y los porcentajes de trabajadores que ganan menos de un ingreso laboral minimo absoluto y relativo (el 60 por ciento de la mediana del ingreso laboral). Aplican empiricamente estos indicadores a los casos de Uganda y Peru.

The aim of this paper is to contribute to the ongoing discussion about the design of a post-2015 development framework by proposing indicators to monitor employment outcomes. Our analysis of the current MDG employment indicators shows that measurement problems, the inappropriate use of aggregate statistics, ambiguous interpretability, and assumptions that often do not hold true in the context of developing countries are major shortcomings of the current indicators. Based on this critique, we develop a new set of indicators for productive employment and decent work. We propose four indicators: (i) the growth of labor value added per worker, (ii) the working poverty rate, (iii) (a) the share of workers receiving less than an absolute labor income and (b) the share of workers receiving less than 60 percent of the median labor income. We demonstrate the empirical application of these indicators using the country cases of Uganda and Peru.

The aim of this paper is to contribute to the ongoing discussion about the design of a post-2015 development framework by proposing indicators to monitor employment outcomes. Our analysis of the current MDG employment indicators shows that measurement problems, the inappropriate use of aggregate statistics, ambiguous interpretability, and assumptions that often do not hold true in the context of developing countries are major shortcomings of the current indicators. Based on this critique, we develop a new set of indicators for productive employment and decent work. We propose four indicators: (i) the growth of labor value added per worker, (ii) the working poverty rate, (iii) (a) the share of workers receiving less than an absolute labor income and (b) the share of workers receiving less than 60 percent of the median labor income. We demonstrate the empirical application of these indicators using the country cases of Uganda and Peru.

A recently introduced rate responsive cardiac pacing system is based on information derived from the intracardiac impedance signal containing information on the inotropic state of the ventricle. This study compared the inotropic state index (ISI) with muscle sympathetic activity (MSA), both being modulated by the autonomic nervous system. Nine patients (66 +/- 3 years, mean +/- SEM) with Inos2DR pacemakers were included. Each patient was studied at rest and during cold pressor test (CPT). Microneurography of the peroneal nerve was performed to measure MSA continuously, which was digitally stored along with continuous surface ECG and blood pressure. The intracardiac impedance signal was transmitted by the pacemaker and stored simultaneously. Linear correlation between ISI and MSA was calculated for the period of the CPT. During CPT, mean systolic blood pressure increased from 122 +/- 4 to 149 +/- 6 mmHg (P < 0.0001), diastolic blood pressure increased from 74 +/- 8 to 86 +/- 4 mmHg (P = 0.02), and intrinsic heart rate increased from 69 +/- 7 to 75 +/- 7 beats/mill (P = 0.019). ISI increased by 21 +/- 7% (P = 0.018), MSA by 26 +/- 6% (P = 0.004). ISI and MSA were positively correlated during the CPT in eight of nine patients (R2 = 0.86-0.99, P < 0.0001). Negative correlation was found in one patient (R2 = 0.94). This study demonstrates parallel increases of the ISI and MSA during CPT. ISI and MSA showed a close linear relationship during provoked changes of sympathetic activity. These results provide further evidence that the sympathetic nervous system is responsible for the observed ISI changes.

In image-guided cardiac interventions, X-ray imaging and intravascular ultrasound (IVUS) imaging are two often used modalities. Interventional X-ray images, including angiography and fluoroscopy, are used to assess the lumen of the coronary arteries and to monitor devices in real time. IVUS provides rich intravascular information, such as vessel wall composition, plaque, and stent expansions, but lacks spatial orientations. Since the two imaging modalities are complementary to each other, it is highly desirable to co-register the two modalities to provide a comprehensive picture of the coronaries for interventional cardiologists. In this paper, we present a solution for co-registering 2-D angiography and IVUS through image-based device tracking. The presented framework includes learning-based vessel detection and device detections, model-based tracking, and geodesic distance-based registration. The system first interactively detects the coronary branch under investigation in a reference angiography image. During the pullback of the IVUS transducers, the system acquires both ECG-triggered fluoroscopy and IVUS images, and automatically tracks the position of the medical devices in fluoroscopy. The localization of tracked IVUS transducers and guiding catheter tips is used to associate an IVUS imaging plane to a corresponding location on the vessel branch under investigation. The presented image-based solution can be conveniently integrated into existing cardiology workflow. The system is validated with a set of clinical cases, and achieves good accuracy and robustness.

The accurate and robust tracking of catheters and transducers employed during image-guided coronary intervention is critical to improve the clinical workflow and procedure outcome. Image-based device detection and tracking methods are preferred due to the straightforward integration into existing medical equipments. In this paper, we present a novel computational framework for image-based device detection and tracking applied to the co-registration of angiography and intravascular ultrasound (IVUS), two modalities commonly used in interventional cardiology. The proposed system includes learning-based detections, model-based tracking, and registration using the geodesic distance. The system receives as input the selection of the coronary branch under investigation in a reference angiography image. During the subsequent pullback of the IVUS transducers, the system automatically tracks the position of the medical devices, including the IVUS transducers and guiding catheter tips, under fluoroscopy imaging. The localization of IVUS transducers and guiding catheter tips is used to continuously associate an IVUS imaging plane to the vessel branch under investigation. We validated the system on a set of 65 clinical cases, with high accuracy (mean errors less than 1.5mm) and robustness (98.46% success rate). To our knowledge, this is the first reported system able to automatically establish a robust correspondence between the angiography and IVUS images, thus providing clinicians with a comprehensive view of the coronaries.

The accurate and robust tracking of catheters and transducers employed during image-guided coronary intervention is critical to improve the clinical workflow and procedure outcome. Image-based device detection and tracking methods are preferred due to the straightforward integration into existing medical equipments. In this paper, we present a novel computational framework for image-based device detection and tracking applied to the co-registration of angiography and intravascular ultrasound (IVUS), two modalities commonly used in interventional cardiology. The proposed system includes learning-based detections, modelbased tracking, and registration using the geodesic distance. The system receives as input the selection of the coronary branch under investigation in a reference angiography image. During the subsequent pullback of the IVUS transducers, the system automatically tracks the position of the medical devices, including the IVUS transducers and guiding catheter tips, under fluoroscopy imaging. The localization of IVUS transducers and guiding catheter tips is used to continuously associate an IVUS imaging plane to the vessel branch under investigation. We validated the system on a set of 65 clinical cases, with high accuracy (mean errors less than 1.5mm) and robustness (98.46% success rate). To our knowledge, this is the first reported system able to automatically establish a robust correspondence between the angiography and IVUS images, thus providing clinicians with a comprehensive view of the coronaries.

Fluoroscopic images contain useful information that is difficult to comprehend due to the collapse of the 3D information into 2D space. Extracting the informative layers and analyzing them separately could significantly improve the task of understanding the image content. Traditional Digital Subtraction Angiography (DSA) is not applicable for coronary angiography because of heart beat and breathing motion. In this work, we propose a layer extraction method for separating transparent motion layers in fluoroscopic image sequences, so that coronary tree can be better visualized.. The method is based on the fact that different anatomical structures possess different motion patterns, e.g., heart is beating fast, while lung is breathing slower. A multiscale implementation is used to further improve the efficiency and accuracy. The proposed approach helps to enhance the visibility of the vessel tree, both visually and quantitatively.

This paper presents a new technique of coronary digital subtraction angiography which separates layers of moving background structures from dynamic fluoroscopic sequences of the heart and obtains moving layers of coronary arteries. A Bayeisan framework combines dense motion estimation, uncertainty propagation and statistical fusion to achieve reliable background layer estimation and motion compensation for coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing breathing and cardiac movements. Perceptibility improvement is significant especially for very thin vessels. Clinical benefit is expected in the context of obese patients and deep angulation, as well as in the reduction of contrast dose in normal size patients.

Digital subtraction angiography (DSA) is a well-known technique for improving the visibility and perceptibility of blood vessels in the human body. Coronary DSA extends conventional DSA to dynamic 2D fluoroscopic sequences of coronary arteries which are subject to respiratory and cardiac motion. Effective motion compensation is the main challenge for coronary DSA. Without a proper treatment, both breathing and heart motion can cause unpleasant artifacts in coronary subtraction images, jeopardizing the clinical value of coronary DSA. In this paper, we present an effective method to separate the dynamic layer of background structures from a fluoroscopic sequence of the heart, leaving a clean layer of moving coronary arteries. Our method combines the techniques of learning-based vessel detection and robust motion estimation to achieve reliable motion compensation for coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing breathing and cardiac movement. Perceptibility improvement is significant especially for very thin vessels. The potential clinical benefit is expected in the context of obese patients and deep angulation, as well as in the reduction of contrast dose in normal size patients.

This paper presents a new technique of coronary digital subtraction angiography which separates layers of moving background structures from dynamic fluoroscopic sequences of the heart and obtains moving layers of coronary arteries. A Bayeisan framework combines dense motion estimation, uncertainty propagation and statistical fusion to achieve reliable background layer estimation and motion compensation for coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing breathing and cardiac movements. Perceptibility improvement is significant especially for very thin vessels. Clinical benefit is expected in the context of obese patients and deep angulation, as well as in the reduction of contrast dose in normal size patients.

Fluoroscopic images contain useful information that is difficult to comprehend due to the collapse of the 3D information into 2D space. Extracting the informative layers and analyzing them separately could significantly improve the task of understanding the image content. Traditional Digital Subtraction Angiography (DSA) is not applicable for coronary angiography because of heart beat and breathing motion. In this work, we propose a layer extraction method for separating transparent motion layers in fluoroscopic image sequences, so that coronary tree can be better visualized.. The method is based on the fact that different anatomical structures possess different motion patterns, e.g. , heart is beating fast, while lung is breathing slower. A multiscale implementation is used to further improve the efficiency and accuracy. The proposed approach helps to enhance the visibility of the vessel tree, both visually and quantitatively.

This paper discusses the application of Time-domain ElectroMagnetics (TEM) in a borehole-conveyed logging tool. Defining concepts like apparent conductivity, apparent dip and apparent azimuth, we show that such a tool may be used to image the conductivity distribution around and ahead of the drill bit at comparatively large distances from the borehole. As such, a TEM tool would be instrumental in optimal placement of a well in a hydrocarbon reservoir.

Summary Several recently published studies discuss the concept of inductive resistivity-logging devices with oblique transmitting and/or receiving coils. Both wireline induction and logging-while-drilling (LWD) propagation resistivity-tool concepts have been considered. Directional resistivity measurements and improved anisotropy measurements are among the benefits promised by this type of device. Analyses based on point-magnetic dipole antennas were used to illustrate these potential benefits. The effects of a metallic mandrel, borehole, and invasion were not considered because of the absence of a suitable forward model. This paper characterizes mandrel, borehole, and invasion effects for a variety of candidate tilt-coil devices with antenna array parameters similar to those of the previous studies. The characterization is based on calculations from a new forward model that includes tilted transmitting and receiving coils of finite diameter embedded in a concentric cylindrical structure. Important details of the forward model used in the calculations are also provided.