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Discusses biomedical signal processing schemes. Time-series processing methods; Time and frequency transforms; Blind signal processing; Empirical mode decomposition; Kernel methods; Data mining.

Computational modeling and imaging in biology and medicine are gaining more and more interest with the discovery of in-depth structural and functional knowledge at all space and time scales (molecule to proteins, cells to organs and organizms). The recursion between description levels for highly dynamical, interacting and complex systems, i.e the integrative approach, is a very challenging topic where formal models, observational tools and experimental investigations have to be closely designed, coupled and confronted together. Imaging techniques play a major role in this interdisciplinary attempt to elucidate this biocomplexity: they convey relevant information about the underlying mechanisms, depict the conformations and anatomical topologies and draw the biophysical laws they may follow. Furthermore, the basic image analysis tools (from calibration to segmentation, motion estimation and registration up to pattern recognition) are generic enough to be of value whatever the objects under consideration. The same comments apply when Computer Graphics or Virtual Reality techniques are concerned. This paper will survey the recent contributions dealing with both models, imaging data and processing frames. Examples ranging over different scales, from macro to nano, will be given in order to enhance the mutual benefits and perspectives that can be expected from this coupling. [ABSTRACT FROM AUTHOR]

In this second of two articles on signal processing, we explore the coupling between modeling and signal processing and the critical importance of well-posed clinical questions or hypotheses, as well as a deep knowledge of the underlying mechanisms of such coupling. Our approach consists of building models either as open-loop simulators allowing us to analyze the influence of one or several internal variables on the observations, or as dynamic systems that can enhance our understanding of these underlying mechanisms to be identified in order to lead to an explanative interpretation. These explorations are illustrated by epileptic network and cardiac models, both derived at the macroscopic level. [ABSTRACT FROM AUTHOR]

Signal processing offers a wide spectrum of theories, methods, and algorithms for addressing a variety of problems ranging from noise reduction, restoration, detection (of events or changes), spatiotemporal dynamics estimation, source localization, and pattern recognition. However, the classical assumptions (stationarity, linearity, etc.) usually do not apply in real situations. Recent advances, such as time-scale and time-frequency transforms, data fusion, long-range dependence, and higher order moments, do not always provide sufficiently robust solutions. In this article, the basic properties and generic features of biomedical signals are examined using a wide range of examples. Algorithmic results are presented to show not only the potential performance but also the limitations of the processing resources at our disposal. The last section describes and discusses signal matching, scenario recognition, and data fusion. [ABSTRACT FROM AUTHOR]

Editorial. Introduces a series of articles on the information technology applications and the infrastructure innovations that would harness biomedical and health care programs in the 21st century. Development and applications of information technology; Overview of the concept of a national information infrastructure; High-performance computing and communication; Next-generation Internet.

The article focuses on moment-based techniques in medical imaging. Edge detection is accomplished by derivative operators while integrative schemes may offer an alternative like in 2-D with geometric moments and Zernike moments. Geometric and Legendre moments have been proposed for 3-D surface detection that assume local planar patch or parabolic model. These techniques should be combined with other competitive methods to take advantage of their features such as invariance properties and computational complexity.

The article focuses on the computation of moment functions. It mentions that a significant amount of computation is required to generate moment values from images. To accelerate the computation of moments and to improve its accuracy and efficiency, the author suggests developing new theoretical formulations, reducing the complexity of the equations and designing innovative implementations. He concludes that faster algorithms are still needed to address more demanding applications in real-time environment.

The article examines moment-based approaches in imaging. Important properties for computer vision applications such as invariance and robustness to noise were sketched. Moments have been found to have a sound theoretical framework for solving the generic problems encountered in many imaging applications. The diverse families of orthogonal moments provide the flexibility that may be required to face a particular target. Such moments have to satisfy the time computation constraints that are inherent in many applications.

The article presents the third installment in a series of reports about biomedical imaging. It describes the sample preparation and conditioning in biology. Several cellular and subcellular sensing and tagging techniques to obtain insights on biological objects include atomic force microscopy and electron microscopy. It describes challenges in segmentation and tracking operations.

This paper describes an extended ray casting scheme for three-dimensional (3-D) navigation into the heart cavities for preoperative planning using multislice X-ray computed tomography data. The key benefit is that artifacts due to contrast inhomogeneities can be eliminated during volume traversal, thus improving the visual perception of the endocardial wall. [ABSTRACT FROM AUTHOR]

Discusses problems with regards to digital imaging. Motion estimation; Motion-based segmentation; Tracking over time or the process of following objects in their movement; Representation issue; Equal to shape-from-shading, structure-from-motion; High-level description of motion.

Highlights the IEEE EMBS International Summer School on Biomedical Imaging on June 19-27, 2004 in Berde Island, Brittany, France. Sponsor of the Summer School; Technical program; Lecturers.

Highlights the Engineering in Medicine and Biology Society seminar in Beijing, China on June 14-30, 2004. Success of the seminar; Hosts of the seminar; Effort to develop biomedical engineering in China.

Examines some of the issues that need to be addressed regarding physiological modeling and information processing to make them central in the future. Knowledge integration through models; Deep models for evaluating the performance of processing algorithms; Combination of multiscale models; Coupling multimodal data and models; Model evaluation.

Examines the role of computational modeling and information technology in biology and medicine on the interdisciplinary attempt to elucidate structures and functions of living systems. Evolution of imaging technology; Concentration on a particular biomedical target; Balance in the understanding of the complexity of living systems and the immediate clinical needs.

Reports on the Fifth IEEE Engineering in Medicine and Biology Society's International Summer School on Biomedical Imaging held at Berder's Island, Brittany, France on June 15-23, 2002. Organizers and supporters of the event; Forum for interdisciplinary exchange; Social activities.

Presents a study which described an algorithm dealing with the extraction of vessels in three-dimensional imaging based on geometrical moments and local cylindrical approximation. Method used; Results and discussion; Conclusion.

Highlights the 6th Summer School. Discussion on image reconstruction and processing; Key factor for the future of the next generation of researchers attending the school; Opportunity to bring emerging technologies; Assessment of the impact of the explosion of imaging techniques.

Focuses on the impact of the active contributions of the participants on the success of the school. Presentation of research work; Social activities; Materials related to the 6th Summer School.

Discusses issues on biomedical engineering. Views on the production of knowledge; Way to avoid hurried scientific works; Challenges that education and training will face.

Although the nonlocal means (NLM) algorithm takes a significant step forward in image filtering field, it suffers from a high computational complexity. To deal with this drawback, this paper proposes an acceleration strategy based on a correlation operation. Instead of per-pixel processing, this approach performs a simultaneous calculation of all the image pixels with the help of correlation operators. Complexity analysis and experimental results are reported and show the advantage of the proposed algorithm in terms of computation and time cost. [ABSTRACT FROM AUTHOR]

Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is crucial, which supports tissue plasminogen activator (tPA) thrombolysis decision-making. Deep learning methods demonstrate superiority in TSS classification. However, they often overfit task-irrelevant features due to insufficient paired labeled data, resulting in poor generalization. We observed that unpaired data are readily available and inherently carry task-relevant cues, but are less often considered and explored. Based on this, in this paper, we propose to fully excavate the potential of unpaired unlabeled data and use them to facilitate the downstream AIS analysis task. We first analyze the utility of features at the varied grain and propose a multi-grained contrastive learning (MGCL) framework to learn task-related prior representations from both coarse-grained and fine-grained levels. The former can learn global prior representations to enhance the location ability for the ischemic lesions and perceive the healthy surroundings, while the latter can learn local prior representations to enhance the perception ability for semantic relation between the ischemic lesion and other health regions. To better transfer and utilize the learned task-related representation, we designed a novel multi-task framework to simultaneously achieve ischemic lesion segmentation and TSS classification with limited labeled data. In addition, a multi-modal region-related feature fusion module is proposed to enable the feature correlation and synergy between multi-modal deep image features for more accurate TSS decision-making. Extensive experiments on the large-scale multi-center MRI dataset demonstrate the superiority of the proposed framework. Therefore, it is promising that it helps better stroke evaluation and treatment decision-making. • We propose a multi-grained contrastive learning framework for the AIS analysis task. • We design two task-specific contrastive feature enhancement strategies. • We devise a feature fusion strategy to capture the multi-modal feature relationship. • Extensive results show the superiority of our method on the large-scale MRI dataset. [ABSTRACT FROM AUTHOR]

Accurate segmentation of uterus, uterine fibroids, and spine from MR images is crucial for high intensity focused ultrasound (HIFU) therapy but remains still difficult to achieve because of 1) the large shape and size variations among individuals, 2) the low contrast between adjacent organs and tissues, and 3) the unknown number of uterine fibroids. To tackle this problem, in this paper, we propose a large kernel Encoder-Decoder Network based on a 2D segmentation model. The use of this large kernel can capture multi-scale contexts by enlarging the valid receptive field. In addition, a deep multiple atrous convolution block is also employed to enlarge the receptive field and extract denser feature maps. Our approach is compared to both conventional and other deep learning methods and the experimental results conducted on a large dataset show its effectiveness. [ABSTRACT FROM AUTHOR]

Compressed sensing MRI (CS-MRI) is considered as a powerful technique for decreasing the scan time of MRI while ensuring the image quality. However, state of the art reconstruction algorithms are still subjected to two challenges including terrible parameters tuning and image details loss resulted from over-smoothing. In this paper, we propose a deep frequency-division network (DFDN) to face these two image reconstruction issues. The proposed DFDN approach applies a deep iterative reconstruction network (DIRN) to replace the regularization terms and the corresponding parameters by a stacked convolution neural network (CNN). And then multiple DIRN blocks are cascaded continuously as one deeper neural network. Data consistency (DC) layer is incorporated after each DIRN block to correct the k -space data of intermediate results. Image content loss is computed after each DC layer and frequency-division loss is gained by weighting the high frequency loss and low frequency loss after each DIRN block. The combination of image content loss and frequency-division loss is considered as the total loss for constraining the network training procedure. Validations of the proposed method have been performed on two brain datasets. Visual results and quantitative evaluations show that the proposed DFDN algorithm has better performance in sparse MRI reconstruction than other comparative methods. [ABSTRACT FROM AUTHOR]

Highlights • Supervoxel technique has been increasingly employed for processing and analyzing the brain magnetic resonance images. • A novel supervoxel segmentation method is first proposed specially for the brain magnetic resonance images. • The proposed iterative spatial fuzzy clustering method can efficiently generate reliable 3D supervoxels for brain magnetic resonance volume with well fitness of boundaries among tissues. Abstract Background Although supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory. New methods To address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume. Results The performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods. Conclusions The proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration. [ABSTRACT FROM AUTHOR]

This work presents a novel method for multimodal medical registration based on histogram estimation of continuous image representation. The proposed method, regarded as “fast continuous histogram estimation,” employs continuous image representation to estimate the joint histogram of two images to be registered. The Jensen–Arimoto (JA) divergence is a similarity measure to measure the statistical dependence between medical images from different modalities. The estimated joint histogram is exploited to calculate the JA divergence in multimodal medical image registration. In addition, to reduce the grid effect caused by the grid-aligning transformations between two images and improve the implementation speed of the registration method, random samples instead of all pixels are extracted from the images to be registered. The goal of the registration is to optimize the JA divergence, which would be maximal when two misregistered images are perfectly aligned using the downhill simplex method, and thus to get the optimal geometric transformation. Experiments are conducted on an affine registration of 2D and 3D medical images. Results demonstrate the superior performance of the proposed method compared to standard histogram, Parzen window estimations, particle filter, and histogram estimation based on continuous image representation without random sampling. [ABSTRACT FROM AUTHOR]

Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization. [ABSTRACT FROM AUTHOR]

By providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging. Recent contributions have demonstrated that the total variation (TV) constraint can lead to improved solution by regularizing the underdetermined ill-posed problem of sparse-view reconstruction. However, when the projection views are reduced below certain numbers, the performance of TV regularization tends to deteriorate with severe artifacts. In this paper, we explore the applicability of Gamma regularization for the sparse-view CT reconstruction. Experiments on simulated data and clinical data demonstrate that the Gamma regularization can lead to good performance in sparse-view reconstruction. [ABSTRACT FROM AUTHOR]

One-to-multiple medical image segmentation aims to directly test a segmentation model trained with the medical images of a one-domain site on those of a multiple-domain site, suffering from segmentation performance degradation on multiple domains. This process avoids additional annotations and helps improve the application value of the model. However, no successful one-to-multiple unsupervised domain adaptation (O2M-UDA) work has been reported in one-to-multiple medical image segmentation due to its inherent challenges: distribution differences among multiple target domains (among-target differences) caused by different scanning equipment and distribution differences between one source domain and multiple target domains (source–target differences). In this paper, we propose an O2M-UDA framework called dynamic domain adaptation (DyDA), for one-to-multiple medical image segmentation, which has two innovations: (1) dynamic credible sample strategy (DCSS) dynamically extracts credible samples from the target site and iteratively updates their number, thus iteratively expanding the generalization boundary of the model and minimizing the among-target differences; (2) hybrid uncertainty learning (HUL) reduces the voxel-level and domain-level uncertainty simultaneously, thus minimizing the source–target differences from the detail and entire perspective concurrently. Experiments on two one-to-multiple medical image segmentation tasks have been conducted to demonstrate the performance of the proposed DyDA. The proposed DyDA achieved competitive segmentation results and high adaptation with an average of 83.8% and 48.1% dice for the two tasks, respectively, which has improved by 21.7% and 9.2% compared with no adaptation, respectively. The code developed in this study code can be downloaded at https://github.com/ZoeyJiang/DyDA. [ABSTRACT FROM AUTHOR]

Purpose: The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. Methods: Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. Results: Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. Conclusions: A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination. [ABSTRACT FROM AUTHOR]

Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CT image is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries is difficult to be observed in the noncontrast CT images, the contrast CT image of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CT image according to the segmentation results in the contrast CT image. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible image noise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1-100, 101-300, >300) by comparing with the ground truth. Conclusions: The calcified lesions in the noncontrast CT images can be detected automatically by using the segmentation results of the aorta, heart, and coronary arteries obtained in the contrast CT images with a very high accuracy. [ABSTRACT FROM AUTHOR]

Computational load remains a major concern when processing signals by means of sliding transforms. In this paper, we present an efficient algorithm for the fast computation of one-dimensional and two-dimensional sliding discrete Tchebichef moments. To do so, we first establish the relationships that exist between the Tchebichef moments of two neighboring windows taking advantage of Tchebichef polynomials’ properties. We then propose an original way to fast compute the moments of one window by utilizing the moment values of its previous window. We further theoretically establish the complexity of our fast algorithm and illustrate its interest within the framework of digital forensics and more precisely the detection of duplicated regions in an audio signal or an image. Our algorithm is used to extract local features of such a signal tampering. Experimental results show that its complexity is independent of the window size, validating the theory. They also exhibit that our algorithm is suitable to digital forensics and beyond to any applications based on sliding Tchebichef moments. [ABSTRACT FROM PUBLISHER]

Renal tumor diagnosis on abdominal enhanced CT volumes is one of the most significant tasks in kidney disease diagnosis. It helps clinicians decide whether to perform the surgery (subtype classification), perform radical operations or minimally invasive treatment (grade classification). However, inherent challenges greatly limit the performance of the model: 1) Tumor appearance differences caused by non-tumor factors. 2) Small inter-class differences and large intra-class variations. In this paper, we propose a novel diagnosis framework for renal tumors, Bi-knowledge Contrastive Network (BKC-Net), which has two innovations: (1) Focus-perceive learning segments the tumors while perceiving the surrounding healthy tissues, thus adjusting the model's representation of tumor appearance, helping the BKC-Net represent the inherent features of tumors. (2) Bi-knowledge contrastive learning introduces prior radiomics features, makes the prior radiomics knowledge and latent deep knowledge complementary to each other from the intra-case level, and forces the high cohesion and low coupling embedding feature space from the inter-case levels, helping to discover subtle but essential differences among classes. Experiments demonstrate that our BKC-Net has the best performance in renal tumor diagnosis. Results reveal that our framework has great potential for renal tumor diagnosis in clinical use. Source codes will be released at https://github.com/DD0922/BKC-Net-pytorch. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the quaternion Bessel–Fourier moments are introduced. The significance of phase information in quaternion Bessel–Fourier moments is investigated and an accurate estimation method for rotation angle is described. Furthermore, a new set of invariant descriptors based on the magnitude and the phase information of quaternion Bessel–Fourier moments is derived. Experimental results show that quaternion Bessel–Fourier moments lead to better performance for color image reconstruction than the other quaternion orthogonal moments such as quaternion Zernike moments, quaternion pseudo-Zernike moments and quaternion orthogonal Fourier–Mellin moments. In addition, the angles estimated by the proposed moments are more accurate than those obtained by using other quaternion orthogonal moments. The proposed invariant descriptors show also better robustness to geometric and photometric transformations. [Copyright &y& Elsevier]

Abstract: Discrete orthogonal moments are powerful tools for characterizing image shape features for applications in pattern recognition and image analysis. In this paper, a new set of discrete orthogonal moments is proposed, based on the discrete Racah polynomials. In order to ensure numerical stability, the Racah polynomials are normalized, thus creating a set of weighted orthonormal Racah polynomials, to define the so-called Racah moments. This new type of discrete orthogonal moments eliminates the need for numerical approximations. The paper also discusses the properties of Racah polynomials such as recurrence relations and permutability property that can be used to reduce the computational complexity in the calculation of Racah polynomials. Finally, we demonstrate Racah moments’ feature representation capability by means of image reconstruction and compression. Comparison with other discrete orthogonal transforms is performed, and the results show that the Racah moments are potentially useful in the field of image analysis. [Copyright &y& Elsevier]

Background: The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://www.symbiomatics.org). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. Results: This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005 ("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. Conclusion: We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science. [ABSTRACT FROM AUTHOR]

This paper reports on a method for left ventricle three-dimensional (3-D) reconstruction from two orthogonal ventriculograms. The proposed algorithm is voxel-based and takes into account the conical projection geometry associated with the biplane image acquisition equipment. The reconstruction process starts with an initial ellipsoidal approximation derived from the input ventriculograms. This model is subsequently deformed in such a way as to match the input projections. To this end, the object is modeled as a 3-D Markov-Gibbs random field, and an energy function is defined so that it includes one term that models the projections compatibility and another one that includes the space-time regularity constraints. The performance of this reconstruction method is evaluated by considering the reconstruction of mathematically synthesized phantoms and two 3-D binary databases from two orthogonal synthesized projections. The method is also tested using real biplane ventriculograms. In this case, the performance of the reconstruction is expressed in terms of the projection error, which attains values between 9.50% and 11.78% for two biplane sequences including a total of 55 images. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is twofold: (i) to show how multislice computed tomography (MSCT) data sets bring the information required for cardiac resynchronisation therapy (CRT) planning; (ii) to demonstrate the feasibility of 3D navigation into the veins where left ventricular leads have to be placed. The former has been achieved by exploring and labelling the cardiac structures of concern, the latter has been performed by using the concept of virtual navigation with high resolution surface detection and estimation algorithms. [Copyright &y& Elsevier]

Clinical interventional hemodynamic studies quantify the ventricular function from two-dimensional (2-D) X-ray projection images without having enough information of the actual three-dimensional (3-D) shape of this cardiac cavity. This paper reports a left ventricle 3-D reconstruction method from two orthogonal angiographic projections. This investigation is motivated by the lack of information about the actual 3-D shape of the cardiac cavity. The proposed algorithm works in 3-D space and considers the oblique projection geometry associated with the biplane image acquisition equipment. The reconstruction process starts by performing an approximate reconstruction based on the Cylindrical Closure Operation and the Dempster--Shafer theory. This approximate reconstruction is appropriately deformed in order to match the given projections. The deformation procedure is carried out by an iterative process that, by means of the Dempster--Shafer and the fuzzy integral theory, combines the information provided by the projection error and the connectivity between voxels. The performance of the proposed reconstruction method is evaluated by considering first the reconstruction of two 3-D binary databases from two orthogonal synthetized projections, obtaining errors as low as 6.48%. The method is then tested on real data, where two orthogonal preprocessed angiographic images are used for reconstruction. The performance of the technique, in this case, is assessed by means of the projection error, whose average for both views is 7.5%. The reconstruction method is also tested by performing the 3-D reconstruction of a ventriculographic sequence throughout an entire cardiac cycle. [ABSTRACT FROM AUTHOR]

In this short paper, accelerated three-dimensional computer simulations of vascular trees development, preserving physiological and haemodynamic features, are reported. The new computation schemes deal: (i) with the geometrical optimization of each newly created bifurcation; and (ii) with the recalculation of blood pressures and radii of vessels in the whole tree. A significant decrease of the computation time is obtained by replacing the global optimization by the fast updating algorithm allowing more complex structure to be simulated. A comparison between the new algorithms and the previous one is illustrated through the hepatic arterial tree. [Copyright &y& Elsevier]