A new method for automatic identification of coronary arteries in standard biplane angiograms

A new computerized method for analyzing biplane coronary arteriograms was proposed. The method extracted the vessels in an image, detected branching points, and identified vessel segments according to their anatomical names. The method was applied to a pair of orthogonal projection images acquired in the right anterior oblique and left anterior oblique views. It recognized the corresponding vessel segments in the images and labeled them correctly by their anatomical names. For diameter measurement, a combined first and second derivative filter process was applied to the images along the skeleton lines of the extracted vessel segments. We also designed a method of representation in which vessel diameters measured in the two projection images could be displayed in a single image.     

A framework for the study of coping, illness behaviour and outcomes

This paper presents a theoretical framework for the study of coping, illness attribution, health behaviour and outcomes. It is based upon models developed within health psychology and aims to provide a theoretical basis for nurse researchers to utilize psychosocial variables. It is an interactionist model which views outcomes as dependent upon both situation and person variables. The situation is viewed as the health threat or illness symptoms as well as the psychosocial context within which the person is operating. This context includes socio-economic factors, social support, social norms, and external factors such as the mass media. The experience of health threat is dependent upon individual appraisal, and the framework incorporates Folkman and Lazarus' transactional model of stress, as well as Leventhal's illness representation model. Behaviour and the perception of threat are also dependent upon outcome expectancies and the appraisal of one's own coping resources, and so the concepts of locus of control and self-efficacy are also incorporated. This framework allows one to identify determinants of behaviour and outcome, and will aid nurses in identifying areas for psycho-social intervention.     

3D coronary reconstruction from routine single-plane coronary angiograms: Clinical validation and quantitative analysis of the right coronary artery in 100 patients

Background: Current coronary angiographic techniques display complex three-dimensional (3D) coronary structures in two dimensions (2D). We have developed a 3D reconstruction (3DR) algorithm using standard single-plane angiographic images that allows for 3D display of coronary structures. The purpose of this study was to validate our 3DR algorithm and quantify anatomic characteristics of the right coronary artery (RCA) in vivo. Methods: Accuracy and reproducibility studies were performed using 3DRs of a coronary phantom and in vivo following 3DRs in 40 patients. The anatomic features of the RCA were then quantified in 100 patients. Results: Comparison of length and bifurcation angles (BA) from the phantom to the 3DRs revealed good accuracy and correlation for both (r = 0.95 and 0.93 respectively), with diameter error of <7%. In vivo, the average root mean square (RMS) error in the spatial coordinates of the vessel centerlines was 3.12 ± 0.77 and 3.16 ± 0.75 mm in 20 left coronary arteries (LCA) and 20 RCAs respectively. Interobserver average RMS error was 3.47 ± 1.96 mm and intraobserver average RMS error was 3.02 ± 1.07 and 3.44 ± 1.57 mm for two different operators (p = NS). The average RCA length was 10.2 ± 1.7 cm, average radius of curvature (ROC) was 52 ± 9°, and the average 3D bifurcation angle of the posterior descending artery (PDA) from the RCA was 55 ± 22°. Foreshortening (FS) of the segments of the RCA in three 'standard’ projections ranged from 0–60, 0–75, and 0–82% respectively. Conclusions: Using our 3DR algorithm patient-specific anatomic characteristics can be accurately displayed and quantified, expanding the information that can be derived from routine coronary angiography.     

Inter- and intra-observer variability in the qualitative categorization of coronary angiograms

The ABC classification of the American College of Cardiology and the American Heart Association is a commonly used categorization to estimate the risk and success of intracoronary intervention, as well as the probability of restenosis. To evaluate the reliability of qualitative angiogram readings, we randomly selected 200 films from single lesion angioplasty procedures. A repeated visual assessment (≥ 2 months interval) by two independent observers resulted in kappa values of inter and intra-observer variability for the ABC lesion classification and for all separate items that compile it. Variability in assessment is expressed in percentage of total agreement, and in kappa value, which is a parameter of the agreement between two or more observations in excess of the chance agreement. Percentage of total agreement and kappa value was 67.8% and 0.33 respectively for the ABC classification, indicating a poor agreement. Probably this is due to the deficiency of strict definitions. Further investigation has to demonstrate whether improvement can be achieved using complete and detailed definitions without ambiguity, and consensus after panel assessment.     

A dynamic and extensible workflow-oriented software framework for image-guided therapy

Objective The software requirements for computer-aided navigation tools in image-guided therapy become increasingly complex as a result of new imaging and tracking hardware, novel application areas and regulatory restrictions. The objective of this work was to develop a new software framework to meet the needs of emerging image-guided procedures and therapies that require multi-modal imaging and tracking. Materials and methods Building on existing open source tools, a software framework was developed to accommodate dynamic data-structures, dynamic input and output interfaces for interventional devices, and dynamic visualization of data. Arbitrary, yet meaningful, connections can be established between these entities in order to implement customized applications that target specific clinical procedures. This framework is called the SIGN. Results A series of test applications have been developed, showing the versatility and usability of the image-guided navigation framework. Conclusion SIGN addresses new challenges in image-guided therapy related to multi-modal imaging and tracking. The dynamic configurability of SIGN allows for rapid development of new applications.     

A framework for callosal fiber distribution analysis

This paper presents a framework for analyzing the spatial distribution of neural fibers in the brain, with emphasis on interhemispheric fiber bundles crossing through the corpus callosum. The proposed approach combines methodologies for fiber tracking and spatial normalization and is applied on diffusion tensor images and standard magnetic resonance images.     

The concept of archiving unedited and edited digital coronary angiograms

Digital image data acquired during cardiac catheterization will soon be archived on standardized digital storage media. However, with the enormous amount of generated data, considerable time will be wasted at later reviewing or at conferences, or when performing additional quantitative studies. As a result major advantages of a digital acquisition and archiving technique will be lost. The concept of two way archiving includes an unedited (primary) digital archive as well as a (secondary) archive edited by operator guided "intelligent" data reduction (IDR). IDR is based upon the elimination of useless and redundant frame sequences (FS), documentation of coronary interventions on one representative single frame (F) and on the reduction of relevant FS and physiological data to an ECG-controlled representative cardiac cycle (CC). With a heart rate of 72/min and an acquisition rate of 12.5 F/s a documentation of each FS may be obtained with only 10 F. A redundancy-free set of 130 F of a diagnostic study as well as only 41-85 F of an interventional study will be archived on an individual 3.5" MOD or on a CD-R. Two cardiologists and two cardiosurgeons studied independently 24 IDR-edited and the corresponding unedited digital angiograms and found no significant differences in the diagnostically relevant coronary morphology and left ventricular function. IDR provides an edited digital coronary angiogram, e. g. a set of images free of redundance and without loss of relevant information. Uneditable FS can be archived in their unedited (primary) form. IDR is managed on-line by an operator interacting with the angiographer.     

Automatic online layer separation for vessel enhancement in X-ray angiograms for percutaneous coronary interventions

Percutaneous coronary intervention is a minimally invasive procedure that is usually performed under image guidance using X-ray angiograms in which coronary arteries are opacified with contrast agent. In X-ray images, 3D objects are projected on a 2D plane, generating semi-transparent layers that overlap each other. The overlapping of structures makes robust automatic information processing of the X-ray images, such as vessel extraction which is highly relevant to support smart image guidance, challenging. In this paper, we propose an automatic online layer separation approach that robustly separates interventional X-ray angiograms into three layers: a breathing layer, a quasi-static layer and a vessel layer that contains information of coronary arteries and medical instruments. The method uses morphological closing and an online robust PCA algorithm to separate the three layers. The proposed layer separation method ran fast and was demonstrated to significantly improve the vessel visibility in clinical X-ray images and showed better performance than other related online or prospective approaches. The potential of the proposed approach was demonstrated by enhancing contrast of vessels in X-ray images with low vessel contrast, which would facilitate the use of reduced amount of contrast agent to prevent contrast-induced side effects.     

A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum

In this paper, we extend the one-class Support Vector Machine (SVM) and the regularized discriminative direction analysis to the Multiple Kernel (MK) framework, providing an effective analysis pipeline for the detection and characterization of brain malformations, in particular those affecting the corpus callosum.The detection of the brain malformations is currently performed by visual inspection of MRI images, making the diagnostic process sensible to the operator experience and subjectiveness. The method we propose addresses these problems by automatically reproducing the neuroradiologist’s approach. One-class SVMs are appropriate to cope with heterogeneous brain abnormalities that are considered outliers. The MK framework allows to efficiently combine the different geometric features that can be used to describe brain structures. Moreover, the regularized discriminative direction analysis is exploited to highlight the specific malformative patterns for each patient.We performed two different experiments. Firstly, we tested the proposed method to detect the malformations of the corpus callosum on a 104 subject dataset. Results showed that the proposed pipeline can classify the subjects with an accuracy larger than 90% and that the discriminative direction analysis can highlight a wide range of malformative patterns (e.g., local, diffuse, and complex abnormalities). Secondly, we compared the diagnosis of four neuroradiologists on a dataset of 128 subjects. The diagnosis was performed both in blind condition and using the classifier and the discriminative direction outputs. Results showed that the use of the proposed pipeline as an assisted diagnosis tool improves the inter-subject variability of the diagnosis.Finally, a graphical representation of the discriminative direction analysis was proposed to enhance the interpretability of the results and provide the neuroradiologist with a tool to fully and clearly characterize the patient malformations at single-subject level.     

基于GA与模糊连接的冠状动脉造影图像分割

目的 针对模糊连接图像分割方法在实现上的困难,提出了一种基于遗传算法的模糊连接分割方法.方法 首先把路径强度作为遗传算法的寻优条件,从而得到图像中各点到种子点间的最优路径,其强度即该点的模糊连接度.然后,以各点的模糊连接度作为其灰度值,形成目标和背景不交叉的新图,最后用阈值法把目标物体提取出来.结果 实验结果表明,将该方法应用于冠状动脉造影图像的血管分割,能准确快速地将血管从背景中分割出来.结论 该分割方法较好地满足了临床冠心病诊断的要求.     

A computational framework and sensitivity analysis for the hormonal treatment of bone

BackgroundOsteoporosis is a bone disease identified by disordering of bone formation and resorption cells. It increases the risk of bone fragility and fracture. Autocrine and paracrine signalling of osteoclasts and osteoblasts plays an important role in the regulation of bone remodelling. Calcitonin is an approved pharmacologic agent for the treatment of osteoporosis.MethodsA novel mathematical model comprising the interaction among osteoclasts and osteoblasts cells with intermittent administration of calcitonin has been presented to study the dynamics of osteoporotic bone. The stability of model and sensitivity of parameters are also discussed.FindingsThe population of osteoclastic and osteoblastic cells has been predicted via numerical simulation. The results of Monte Carlo sensitivity analysis are shown via tornado diagram.InterpretationIt is concluded that intermittent administration of calcitonin is an effective therapy for the treatment of osteoporosis.