Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI.

BACKGROUND: Phase contrast magnetic resonance imaging (PC-MRI) is used clinically for quantitative assessment of cardiovascular flow and function, as it is capable of providing directly-measured 3D velocity maps. Alternatively, vascular flow can be estimated from model-based computation fluid dynamics (CFD) calculations. CFD provides arbitrarily high resolution, but its accuracy hinges on model assumptions, while velocity fields measured with PC-MRI generally do not satisfy the equations of fluid dynamics, provide limited resolution, and suffer from partial volume effects. The purpose of this study is to develop a proof-of-concept numerical procedure for constructing a simulated flow field that is influenced by both direct PC-MRI measurements and a fluid physics model, thereby taking advantage of both the accuracy of PC-MRI and the high spatial resolution of CFD. The use of the proposed approach in regularizing 3D flow fields is evaluated. METHODS: The proposed algorithm incorporates both a Newtonian fluid physics model and a linear PC-MRI signal model. The model equations are solved numerically using a modified CFD algorithm. The numerical solution corresponds to the optimal solution of a generalized Tikhonov regularization, which provides a flow field that satisfies the flow physics equations, while being close enough to the measured PC-MRI velocity profile. The feasibility of the proposed approach is demonstrated on data from the carotid bifurcation of one healthy volunteer, and also from a pulsatile carotid flow phantom. RESULTS: The proposed solver produces flow fields that are in better agreement with direct PC-MRI measurements than CFD alone, and converges faster, while closely satisfying the fluid dynamics equations. For the implementation that provided the best results, the signal-to-error ratio (with respect to the PC-MRI measurements) in the phantom experiment was 6.56 dB higher than that of conventional CFD; in the in vivo experiment, it was 2.15 dB higher. CONCLUSIONS: The proposed approach allows partial or complete measurements to be incorporated into a modified CFD solver, for improving the accuracy of the resulting flow fields estimates. This can be used for reducing scan time, increasing the spatial resolution, and/or denoising the PC-MRI measurements.

A sliding window approach to detrended fluctuation analysis of heart rate variability.

The analysis of heart rate variability (HRV) aids in the diagnosis of various diseases related to the malfunction of the autonomic nervous system. Traditional approaches for analysis of HRV require the signal to be reasonably stationary during the period of observation. This is not possible when analyzing long duration signals. Detrended fluctuation analysis (DFA) is robust to this issue, as it removes external interferences ("trends") and considers only intrinsic characteristics which are present throughout the signal. DFA is typically performed by segmenting the signal into shorter windows. This has two undesirable effects: (i) if the signal length is not a multiple of the window length, then at least one window will have fewer samples than the others; and (ii) discontinuities are observed on the detrended signal at the edges of each window. Both issues may be addressed using a sliding window. We propose and evaluate this idea, comparing its results with those obtained using the traditional approach. Experiments using different kinds of random and real HRV signals are presented. Statistically significant differences were observed with the proposed approach, especially with respect to α2 values. The proposed method also presented a great reduction in α1 error for white noise, which is a good model for congestive heart failure, with respect to α1 correlations.

Segmentation of aortic flow in real time magnetic resonance images.

Real-time spiral phase contrast magnetic resonance imaging (MRI) is capable of non-invasively measuring the stroke volume associated with each individual heartbeat. The quality of these measurements depends on how good the segmentation of the interface between aortic wall and lumen is. Such process is hampered by the low-resolution and low-contrast nature of real-time images. Image segmentation using traditional techniques has proven not robust. This paper presents a novel model-based approach, which is capable of very accurately segmenting aortic flow. Instead of attempting to achieve a millimetrically-accurate segmentation of the wall-lumen interface, the proposed algorithm focuses on separating the aortic flow from neighboring flows. This provides robustness, even when this interface is not visually distinguishable. The proposed segmentation takes real-time MRI one step further towards becoming the non-invasive gold standard for assessment of stroke volume variability.

Parallel imaging acceleration of spiral Fourier velocity encoded MRI using SPIRiT.

This paper demonstrates parallel imaging acceleration of spiral Fourier velocity encoded MRI using the iterative self-consistent parallel imaging reconstruction (SPIRiT) technique. Magnitude images and time-velocity distributions obtained with image domain SPIRiT and sum-of-squares reconstruction are compared, for 2-fold and 4-fold undersampling. We show that SPIRiT is able to reduce spatial aliasing from undersampled time-velocity distributions, with good results for 2-fold undersampling, and moderately good results for 4-fold undersampling.

Insole with pressure control and tissue neoformation induction systems for diabetic foot.

This article presents the development of a prototype insole derived from natural rubber from Hevea brasiliensis, equipped with pressure control and capable of neoformation of tissue for people who have diabetic foot. The active element of this insole is the electronic circuit that monitors the plantar pressure. In addition, on the present stage of the research, a signal irradiating cell is used based on the principle of tissue regeneration using laser. This project proposes a "smart" insole prototype with a pressure monitoring system and an electronic system for tissue regeneration, which will open a new approach in an attempt to solve the problem of diabetic foot.

Feasibility of in vivo measurement of carotid wall shear rate using spiral Fourier velocity encoded MRI.

Arterial wall shear stress is widely believed to influence the formation and growth of atherosclerotic plaque; however, there is currently no gold standard for its in vivo measurement. The use of phase contrast MRI has proved to be challenging due to partial-volume effects and inadequate signal-to-noise ratio at the high spatial resolutions that are required. This work evaluates the use of spiral Fourier velocity encoded MRI as a rapid method for assessing wall shear rate in the carotid arteries. Wall shear rate is calculated from velocity histograms in voxels spanning the blood/vessel wall interface, using a method developed by Frayne and Rutt (Magn Reson Med 1995;34:378-387). This study (i) demonstrates the accuracy of the velocity histograms measured by spiral Fourier velocity encoding in a pulsatile carotid flow phantom compared with high-resolution two-dimensional Fourier transform phase contrast, (ii) demonstrates the accuracy of Fourier velocity encoding-based shear rate measurements in a numerical phantom designed using a computational fluid dynamics simulation of carotid flow, and (iii) demonstrates in vivo measurement of regional wall shear rate and oscillatory shear index in the carotid arteries of healthy volunteers at 3 T.

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle.

BACKGROUND: Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve. METHODS: This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles. RESULTS: The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated. CONCLUSIONS: The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.

On the use of motion-based frame rejection in temporal averaging denoising for segmentation of echocardiographic image sequences.

We have recently introduced an algorithm for semi-automatic segmentation of the left ventricular wall in short-axis echocardiographic images (EMBC 30:218-221). In its preprocessing stage, the algorithm uses temporal averaging for image denoising. Motion estimation is used to detect and reject frames that do not correlate well with the set of images being averaged. However, the process of estimating motion vectors is computationally intense, which increases the algorithm's computation time. In this work, we evaluate the viability of replacing the motion estimation stage with less computationally intense approaches. Two alternative techniques are evaluated. The ventricular contours obtained from each of the three algorithm variants were quantitatively and qualitatively compared with contours manually-segmented by a specialist. We show that it is possible to reduce the algorithm's computational load without significantly reducing the segmentation quality. The proposed algorithms are also compared with three other techniques from the literature.

Two-dimensional compression of surface electromyographic signals using column-correlation sorting and image encoders.

We present a new preprocessing technique for two-dimensional compression of surface electromyographic (S-EMG) signals, based on correlation sorting. We show that the JPEG2000 coding system (originally designed for compression of still images) and the H.264/AVC encoder (video compression algorithm operating in intraframe mode) can be used for compression of S-EMG signals. We compare the performance of these two off-the-shelf image compression algorithms for S-EMG compression, with and without the proposed preprocessing step. Compression of both isotonic and isometric contraction S-EMG signals is evaluated. The proposed methods were compared with other S-EMG compression algorithms from the literature.

Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses.

The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the user's intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg-Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865-72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

Semi-automatic detection of the left ventricular border.

Two semi-automatic methods for the detection of the left ventricular border in two-dimensional short axis echocardiographic images are presented and compared. In these methods, the left ventricular area variation curve is calculated during a complete cardiac cycle after the segmentation of several frames. This allows the evaluation of the cardiovascular dynamics and the identification of important clinical parameters. The algorithms are proposed as several independent modules. The results are validated through the comparison between the semi-automatic continuous boundaries and manuals boundaries sketched by a medical specialist.

Left ventricle segmentation in echocardiography using a radial-search-based image processing algorithm.

A new left ventricle segmentation method in two-dimensional echocardiography images is proposed. Image processing techniques combined with radial search and temporal information are used to extract the left ventricle boundary. Borders from sequential images are extracted using the proposed method, and a curve illustrating the area variation within a cardiac cycle is presented. Performance evaluation is performed by comparing the borders obtained from the presented method to those manually prescribed by a medical specialist. The new sequential radial search algorithm improved the border extraction from long-axis ultrasound images, specially the ones where the mitral valve was open. Segmentation errors due to low contrast were corrected.

A new model for programming software in body sensor networks.

A Body Sensor Network (BSN) must be designed to work autonomously. On the other hand, BSNs need mechanisms that allow changes in their behavior in order to become a clinically useful tool. The purpose of this paper is to present a new programming model that will be useful for programming BSN sensor nodes. This model is based on an intelligent intermediate-level compiler. The main purpose of the proposed compiler is to increase the efficiency in system use, and to increase the lifetime of the application, considering its requirements, hardware possibilities and specialist knowledge. With this model, it is possible to maintain the autonomous operation capability of the BSN and still offer tools that allow users with little grasp on programming techniques to program these systems.

A new wavelet-based algorithm for compression of EMG signals.

Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, only a few studies dealt with the compression of these signals. In this article we propose a novel algorithm for EMG signal compression using the wavelet transform. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 50 to 90%, with an average PRD ranging from 1.4 to 7.5%. The proposed method uses a new scheme for normalizing the wavelet coefficients. The wavelet coefficients are quantized using dynamic bit allocation, which is carried out by a Kohonen Neural Network. After the quantization, these coefficients are encoded using an arithmetic encoder. The compression results using the proposed algorithm were compared to other algorithms based on the wavelet transform. The proposed algorithm had a better performance in compression ratio and fidelity of the reconstructed signal.

Rapid quantitation of cardiovascular flow using slice-selective fourier velocity encoding with spiral readouts.

Accurate flow visualization and quantitation is important for the assessment of many cardiovascular conditions such as valvular stenosis and regurgitation. Phase contrast based methods experience partial volume artifacts when flow is highly localized, complex and/or turbulent. Fourier velocity encoding (FVE) avoids such problems by resolving the full velocity distribution within each voxel. This work proposes the use of slice selective FVE with spiral readouts to acquire fully localized velocity distributions in a short breath-hold. Scan-plane prescription is performed using classic protocols, and an automatic algorithm is used for in-plane localization of the flow. Time and spatially-resolved aortic valve velocity distributions with 26-msec temporal resolution and 25 cm/sec velocity resolution over a 600 cm/sec field-of-view were acquired in a 12-heartbeat breath-hold. In carotid studies, scan time was extended to achieve higher spatial resolution. The method was demonstrated in healthy volunteers and patients, and the results compared qualitatively well with Doppler ultrasound. Acquisition time could be reduced to 7 heartbeats (a 42% reduction) using partial Fourier reconstruction along the velocity dimension.