A knowledge-based approach for 3-D reconstruction and labeling of vascular networks from biplane angiographic projections

An approach to the three-dimensional reconstruction of coronary arteries is presented. The principal objective is to show how modeling of a vascular network, together with algorithmic procedures, can lead to accurate 3-D structure and feature labeling. The labeling problem is stated directly within the 3-D reconstruction framework. The reconstruction ambiguities inherent to biplane techniques are solved by means of a knowledge base, modeling of the object, and heuristic rules. Feasibility in near-real situations has been demonstrated. The critical importance of the object 3-D reference to achieving the data and modeling matching is emphasized, and a way to deal with it is pointed out. The overall system implies an incremental development in methodologies and experiments. All of them have been elaborated and tested independently, and the most appropriate ones have been selected for integration into a modular system. All the stages of the process (calibration, segmentation, reconstruction, and display) are discussed, with the main focus on modeling. Examples of automatic reconstruction from a phantom are provided.     

Iterative PET Image Reconstruction Using Translation Invariant Wavelet Transform

The present work describes a Bayesian maximum a posteriori (MAP) method using a statistical multiscale wavelet prior model. Rather than using the orthogonal discrete wavelet transform (DWT), this prior is built on the translation invariant wavelet transform (TIWT). The statistical modeling of wavelet coefficients relies on the generalized Gaussian distribution. Image reconstruction is performed in spatial domain with a fast block sequential iteration algorithm. We study theoretically the TIWT MAP method by analyzing the Hessian of the prior function to provide some insights on noise and resolution properties of image reconstruction. We adapt the key concept of local shift invariance and explore how the TIWT MAP algorithm behaves with different scales. It is also shown that larger support wavelet filters do not offer better performance in contrast recovery studies. These theoretical developments are confirmed through simulation studies. The results show that the proposed method is more attractive than other MAP methods using either the conventional Gibbs prior or the DWT-based wavelet prior.     

Issues in image-guided therapy [A Look at . . .]

M edical robotics, computer- assisted surgery (CAS), image-guided therapy (IGT), and the like emerged more than 20 years ago, and many advances have been made since. Conferences and workshops have been organized; scientific contributions, position papers, and patents have been published; new academic societies have been launched; and companies were created all over the world to propose methods, devices, and systems in the area. Researchers in robotics, computer vision and graphics, electronics, mechanics, biomedical engineers, physicians, and surgeons have been involved, thus demonstrating the enthusiasm for this emerging field. Their commitments emphasize the transdisciplinary nature of the efforts to be made. However, the effective dissemination of CAS-IGT systems in medical disciplines remains limited. There are several reasons that may explain this situation, among which the effective demonstration of patient benefits and cost savings, the reluctance of surgeons or therapists to use them, and, of course, the technological breakthroughs are still expected. This series of articles attempts to point out some of them and will also show that many opportunities in computer-assisted interventions are open in the near future.     

3D navigation in medicine

The field of medical imaging has progressed dramatically. Advances have been both methodological (vision and analysis algorithms) and technological (processing, storage, and communication), despite often troublesome shortcomings. The author discusses the application of virtual reality, virtual sensor modelling and image rendering in the medical field. He shows the initial outline of a navigation into 3D discrete arrays     

Physiologically based modeling of 3-D vascular networks and CT scan angiography

In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices.     

How can deep knowledge be used in CCU monitoring?

The authors discuss the knowledge-based approach to monitoring and how it will contribute to a better assessment of the patient's physiological state. In a CCU (coronary care unit) monitoring context, the full exploitation of biological variables such as the ECG (electrocardiogram) and haemodynamics as well as iatrogenic events and drug-related data may lead to this goal. This integrated view is a means to improve both the robustness of the analysis and the differential diagnosis, thereby leading to a proper hierarchy of alarm-settings and system (re)configuration. Performing fine-grained differential diagnostics is a prerequisite for managing arrhythmias through the most adequate therapeutic actions. Thus, computer-aided therapeutic interventions can be expected to become more appropriate as the knowledge-based systems' internal image of the patient state accords better with reality. Through clinical examples, the authors illustrate basic features of high-level ECG interpretation and show the involvement of shallow and deep knowledge     

Time-frequency scaling transformation of the phonocardiogram basedof the matching pursuit method

A time-frequency scaling transformation based on the matching pursuit (MP) method is developed for the phonocardiogram (PCG). The MP method decomposes a signal into a series of time-frequency atoms by using an iterative process. The modification of the time scale of the PCG can be performed without perceptible change in its spectral characteristics. It is also possible to modify the frequency scale without changing the temporal properties. The technique has been tested on 11 PCGs containing heart sounds and different murmurs. A scaling/inverse-scaling procedure was used for quantitative evaluation of the scaling performance. Both the spectrogram and a MP-based Wigner distribution were used for visual comparison in the time-frequency domain. The results showed that the technique is suitable and effective for the time-frequency scale transformation of both the transient property of the heart sounds and the more complex random property of the murmurs. It is also shown that the effectiveness of the method is strongly related to the optimization of the parameters used for the decomposition of the signals     

Three-dimensional motion and reconstruction of coronary arteries from biplane cineangiography

A new approach is described for reconstructing coronary arteries from two sequences of projection images. The estimation of motion is performed on three-dimensional line segments (or centrelines), and is based on a ‘predictionprojection-optimization’ loop. The method copes with time varying properties, deformations and superpositions of vessels. Experiments using simulated and real data have been carried out. and the results found to be robust over a full cycle of a human heart. Local and global kinetic features can then be derived to obtain a greater insight on the cardiac functional state     

Automatic source scanner identification using 1D convolutional neural network

In this digital world, digitized documents can be considered original or a piece of evidence; checking the authenticity of any suspicious image has become an unavoidable concern to preserve the trust in its legitimacy. However, identifying the source of a digital image without any prior embedded information is a very challenging task. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN) model to solve the source scanner identification (SSI) problem blindly. Unlike traditional methods based on handcrafted features, the proposed framework can dynamically learn and extract scanner device-specific features. This work, comprised of the 1D-CNN and a support vector machine (SVM) as a classifier, was trained on nine scanners of different brands and models. The experimental result shows that our model achieves 98.15% accuracy on full images and overall accuracy of 93.13% on segments from test images, outperforming other state-of-art approaches. Our model also proves to be able to distinguish between scanners of the same model. Furthermore, the SVM classifier improved the 1D-CNN accuracy by approximately 3% compared to its original configuration.