Fast block flow tracking of atrial septal defects in 4D echocardiography

Hierarchical Template Matching for 3D Myocardial Tracking and Cardiac Strain Estimation

Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain estimation. We have extended a Local Weighted Mean (LWM) transformation function for 3D, and incorporated in a Hierarchical Template Matching model to solve 3D myocardial tracking and strain estimation problem. The LWM does not need to solve a large system of equations, provides smooth displacement of myocardial points, and adapt local geometric differences in images. Hence, 3D myocardial tracking can be performed with 1.49 mm median error, and without large error outliers. The maximum error of tracking is up to 24% reduced compared to benchmark methods. Moreover, the estimated strain can be insightful to improve 3D imaging protocols, and the computer code of LWM could also be useful for geo-spatial and manufacturing image analysis researchers.

Estimation of Large-Scale Organ Motion in B-Mode Ultrasound Image Sequences: A Survey

Reviewed here are methods developed for following (i.e., tracking) structures in medical B-mode ultrasound time sequences during large-scale motion. The resulting motion estimation problem and its key components are defined. The main tracking approaches are described, and their strengths and weaknesses are discussed. Existing motion estimation methods, tested on multiple in vivo sequences, are categorized with respect to their clinical applications, namely, cardiac, respiratory and muscular motion. A large number of works in this field had to be discarded as thorough validation of the results was missing. The remaining relevant works identified indicate the possibility of reaching an average tracking accuracy up to 1-2 mm. Real-time performance can be achieved using several methods. Yet only very few of these have progressed to clinical practice. The latest trends include incorporation of complementary and prior information. Advances are expected from common evaluation databases and 4-D ultrasound scanning technologies.

A new technique for the estimation of cardiac motion in echocardiography based on transverse oscillations: a preliminary evaluation in silico and a feasibility demonstration in vivo

Quantification of regional myocardial motion and deformation from cardiac ultrasound is fostering considerable research efforts. Despite the tremendous improvements done in the field, all existing approaches still face a common limitation which is intrinsically connected with the formation of the ultrasound images. Specifically, the reduced lateral resolution and the absence of phase information in the lateral direction highly limit the accuracy in the computation of lateral displacements. In this context, this paper introduces a novel setup for the estimation of cardiac motion with ultrasound. The framework includes an unconventional beamforming technique and a dedicated motion estimation algorithm. The beamformer aims at introducing phase information in the lateral direction by producing transverse oscillations. The estimator directly exploits the phase information in the two directions by decomposing the image into two 2-D single-orthant analytic signals. An in silico evaluation of the proposed framework is presented on five ultra-realistic simulated echocardiographic sequences, where the proposed motion estimator is contrasted against other two phase-based solutions exploiting the presence of transverse oscillations and against block-matching on standard images. An implementation of the new beamforming strategy on a research ultrasound platform is also shown along with a preliminary in vivo evaluation on one healthy subject.

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Accurate automated segmentation and detection of ovarian cancer metastases may improve the diagnosis and prognosis of women with ovarian cancer. In this paper, we focus on an important subset of ovarian cancer metastases that spread to the surface of the liver and spleen. Automated ovarian cancer metastasis detection and segmentation are very challenging problems to solve. These metastases have a wide variety of shapes and intensity values similar to that of the liver, spleen and adjacent soft tissues. To address these challenges, this paper presents a variational approach, called tumor sensitive matching flow (TSMF), to detect and segment perihepatic and perisplenic ovarian cancer metastases. TSMF is an image motion field that only highlights metastasis-caused deformation on the surface of liver and spleen while dampening all other image motion between the patient image and the atlas image. It provides several benefits: (1) juxtaposing the roles of image matching and metastasis classification within a variational framework; (2) only requiring a small set of features from a few patient images to train a metastasis-likelihood function for classification; and (3) dynamically creating shape priors for geodesic active contour (GAC) to prevent inaccurate metastasis segmentation. We compared the TSMF to an organ surface partition (OSP) baseline approach. At a false positive rate of 2 per patient, the sensitivities of TSMF and OSP were 87% and 17% (p<0.001), respectively. In a comparison of the segmentations conducted using TSMF-constrained GAC and conventional GAC, the volume overlap rates were 73 ± 9% and 46 ± 26% (p<0.001) and average surface distances were 2.4 ± 1.2 mm and 7.0 ± 6.0 mm (p<0.001), respectively. These encouraging results demonstrate that TSMF could accurately detect and segment ovarian cancer metastases.

Robust myocardial motion tracking for echocardiography: variational framework integrating local-to-global deformation

This paper proposes a robust real-time myocardial border tracking algorithm for echocardiography. Commonly, after an initial contour of LV border is traced at one or two frames from the entire cardiac cycle, LV contour tracking is performed over the remaining frames. Among a variety of tracking techniques, optical flow method is the most widely used for motion estimation of moving objects. However, when echocardiography data is heavily corrupted in some local regions, the errors bring the tracking point out of the endocardial border, resulting in distorted LV contours. This shape distortion often occurs in practice since the data acquisition is affected by ultrasound artifacts, dropouts, or shadowing phenomena of cardiac walls. The proposed method is designed to deal with this shape distortion problem by integrating local optical flow motion and global deformation into a variational framework. The proposed descent method controls the individual tracking points to follow the local motions of a specific speckle pattern, while their overall motions are confined to the global motion constraint being approximately an affine transform of the initial tracking points. Many real experiments show that the proposed method achieves better overall performance than conventional methods.

Medical image diagnostics based on computer-aided flow analysis using magnetic resonance images

Most of the cardiac abnormalities have an implication on hemodynamics and affect cardiovascular health. Diagnostic imaging modalities such as computed tomography and magnetic resonance imaging provide excellent anatomical information on myocardial structures, but fail to show the cardiac flow and detect heart defects in vivo condition. The computerized technique for fluid motion estimation by pixel intensity tracking based on magnetic resonance signals represents a promising technique for functional assessment of cardiovascular disease, as it can provide functional information of the heart in addition to analysis of its anatomy. Cardiovascular flow characteristics can be measured in both normal controls and patients with cardiac abnormalities such as atrial septal defect, thus, enabling identification of the underlying causes of these flow phenomena. This review paper focuses on an overview of a flow analysis scheme based on computer-aided evaluation of magnetic resonance intensity images, in comparison with other commonly used medical imaging modalities. Details of the proposed technique are provided with validations being conducted at selected abnormal cardiovascular patients. It is expected that this new technique can potentially extend applications for characterizing cardiovascular defects and their hemodynamic behavior.

Left ventricular border tracking using cardiac motion models and optical flow

The use of automated methods is becoming increasingly important for assessing cardiac function quantitatively and objectively. In this study, we propose a method for tracking three-dimensional (3-D) left ventricular contours. The method consists of a local optical flow tracker and a global tracker, which uses a statistical model of cardiac motion in an optical-flow formulation. We propose a combination of local and global trackers using gradient-based weights. The algorithm was tested on 35 echocardiographic sequences, with good results (surface error: 1.35 ± 0.46 mm, absolute volume error: 5.4 ± 4.8 mL). This demonstrates the method's potential in automated tracking in clinical quality echocardiograms, facilitating the quantitative and objective assessment of cardiac function.

3-D phantom and in vivo cardiac speckle tracking using a matrix array and raw echo data

Cardiac motion has been tracked using various methods, which vary in their invasiveness and dimensionality. One such noninvasive modality for cardiac motion tracking is ultrasound. Three-dimensional ultrasound motion tracking has been demonstrated using detected data at low volume rates. However, the effects of volume rate, kernel size, and data type (raw and detected) have not been sufficiently explored. First comparisons are made within the stated variables for 3-D speckle tracking. Volumetric data were obtained in a raw, baseband format using a matrix array attached to a high parallel receive beam count scanner. The scanner was used to acquire phantom and human in vivo cardiac volumetric data at 1000-Hz volume rates. Motion was tracked using phase-sensitive normalized cross-correlation. Subsample estimation in the lateral and elevational dimensions used the grid-slopes algorithm. The effects of frame rate, kernel size, and data type on 3-D tracking are shown. In general, the results show improvement of motion estimates at volume rates up to 200 Hz, above which they become stable. However, peak and pixel hopping continue to decrease at volume rates higher than 200 Hz. The tracking method and data show, qualitatively, good temporal and spatial stability (for independent kernels) at high volume rates.

Analysis of motion tracking in echocardiographic image sequences: influence of system geometry and point-spread function

This paper focuses on motion tracking in echocardiographic ultrasound images. The difficulty of this task is related to the fact that echographic image formation induces decorrelation between the underlying motion of tissue and the observed speckle motion. Since Meunier's seminal work, this phenomenon has been investigated in many simulation studies as part of speckle tracking or optical flow-based motion estimation techniques. Most of these studies modeled image formation using a linear convolution approach, where the system point-spread function (PSF) was spatially invariant and the probe geometry was linear. While these assumptions are valid over a small spatial area, they constitute an oversimplification when a complete image is considered. Indeed, echocardiographic acquisition geometry relies on sectorial probes and the system PSF is not perfectly invariant, even if dynamic focusing is performed. This study investigated the influence of sectorial geometry and spatially varying PSF on speckle tracking. This was done by simulating a typical 64 elements, cardiac probe operating at 3.5 MHz frequency, using the simulation software Field II. This simulation first allowed quantification of the decorrelation induced by the system between two images when simple motion such as translation or incompressible deformation was applied. We then quantified the influence of decorrelation on speckle tracking accuracy using a conventional block matching (BM) algorithm and a bilinear deformable block matching (BDBM) algorithm. In echocardiography, motion estimation is usually performed on reconstructed images where the initial sectorial (i.e., polar) data are interpolated on a cartesian grid. We therefore studied the influence of sectorial acquisition geometry, by performing block matching on cartesian and polar data. Simulation results show that decorrelation is spatially variant and depends on the position of the region where motion takes place relative to the probe. Previous studies did not consider translation in their experiments, since their simulation model (spatially invariant PSF and linear probe) yields by definition no decorrelation. On the opposite, our realistic simulation settings (i.e., sectorial probe and realistic beamforming) show that translation yields decorrelation, particularly when translation is large (above 6 mm) and when the moving regions is located close to the probe (distance to probe less than 50 mm). The tracking accuracy study shows that tracking errors are larger for the usual cartesian data, whatever the estimation algorithm, indicating that speckle tracking is more reliable when based on the unconverted polar data: for axial translations in the range 0-10 mm, the maximum error associated to conventional block matching (BM) is 4.2 mm when using cartesian data and 1.8 mm for polar data. The corresponding errors are 1.8 mm (cartesian data) and 0.4 mm (polar data) for an applied deformation in the range 0-10%. We also show that accuracy is improved by using the bilinear deformable block matching (BDBM) algorithm. For translation, the maximum error associated to the bilinear deformable block matching is indeed 3.6mm (cartesian data) and 1.2 mm (polar data). Regarding deformation, the error is 0.7 mm (cartesian data) and 0.3 mm (polar data). These figures also indicates that the larger improvement brought by the bilinear deformable block matching over standard block matching logically takes place when deformation on cartesian data is considered (the error drops from 1.8 to 0.7 mm is this case). We give a preliminary evaluation of this framework on a cardiac sequence acquired with a Toshiba Powervision 6000 imaging system using a probe operating at 3.25 MHz. As ground truth reference motion is not available in this case, motion estimation performance was evaluated by comparing a reference image (i.e., the first image of the sequence) and the subsequent images after motion compensation has been applied. The comparison was quantified by computing the normalized correlation between the reference and the motion-compensated images. The obtained results are consistent with the simulation data: correlation is smaller for cartesian data, whatever the estimation algorithm. The correlation associated to the conventional block matching (BM) is in the range 0.45-0.02 when using cartesian data and in the range 0.65-0.2 for polar data. The corresponding correlation ranges for the bilinear deformable block matching are 0.98-0.2 and 0.98-0.55. In the same way these figures indicate that the bilinear deformable block matching yield a larger improvement when cartesian data are considered (correlation range increases from 0.45-0.02 to 0.98-0.2 in this case).

Automated border detection in three-dimensional echocardiography: principles and promises

Several automated border detection approaches for three-dimensional echocardiography have been developed in recent years, allowing quantification of a range of clinically important parameters. In this review, the background and principles of these approaches and the different classes of methods are described from a practical perspective, as well as the research trends to achieve a robust method.

Ethical Analysis of Non-Medical Fetal Ultrasound

Obstetric ultrasound is the well-recognized prenatal test used to visualize and determine the condition of a pregnant woman and her fetus. Apart from the clinical application, some businesses have started promoting the use of fetal ultrasound machines for nonmedical reasons. Non-medical fetal ultrasound (also known as ‘keepsake’ ultrasound) is defined as using ultrasound to view, take a picture, or determine the sex of a fetus without a medical indication. Notwithstanding the guidelines and warnings regarding ultrasound safety issued by governments and professional bodies, the absence of scientifically proven physical harm to fetuses from this procedure seems to provide these businesses with grounds for rapid expansion. However, this argument is too simplistic because current epidemiological evidence is not synchronous with advancing ultrasound technology. As non-medical fetal ultrasound has aroused very significant public attention, a thorough ethical analysis of this topic is essential. Using a multifaceted approach, we analyse the ethical perspective of non-medical fetal ultrasound in terms of the expectant mother, the fetus and health professionals. After applying four major theories of ethics and principles (the precautionary principle; theories of consequentialism and impartiality; duty-based theory; and rights-based theories), we conclude that obstetric ultrasound practice is ethically justifiable only if the indication for its use is based on medical evidence. Non-medical fetal ultrasound can be considered ethically unjustifiable. Nevertheless, the ethical analysis of this issue is time dependent owing to rapid advancements in ultrasound technology and the safety issue. The role of health professionals in ensuring that obstetric ultrasound is an ethically justifiable practice is also discussed.