Postprocessing Approaches for the Improvement of Cardiac Ultrasound B-Mode Images: A Review

Open-chest cardiac ultrasound-mediated imaging with a vacuum coupler

BACKGROUND

A fundamental obstacle for the preclinical development of ultrasound-(US) mediated cardiac imaging remains cardiac motion, which limits interframe correlation during extended acquisition periods.

PURPOSE

To address this need, we present the design and implementation of a 3D-printed vacuum coupler that stabilizes a US transducer on the epicardial surface of the heart for feasibility assessment and development of advanced, cardiac, US-mediated imaging approaches.

METHODS

The vacuum coupler was 3D printed with biocompatible resins and secured with a standard intraoperative suction aspirator. US-mediated imaging (i.e., B-mode and photoacoustic [PA] imaging) was performed in an open-chest porcine model with and without the vacuum coupler. Based on inter-frame displacement tracking and cross-correlation (CC) coefficients, changes in frame motion and stability were compared for each imaging mode/configuration through a prolonged (∼1 min) acquisition, while the impact on PA-based SO2 accuracy was assessed.

RESULTS

When compared to conventional handheld imaging, stand-off imaging, and coupler without suction, epicardial imaging with the vacuum coupler and suction applied led to a significantly reduced mean axial displacement of 0.15 mm versus 0.89, 0.49, & 0.49 mm, respectively (p-values ≤ 8.65e-7). Comparing the coupler without suction to that with suction applied, physiologically unrealistic SO2 estimates reduced from 1.72 to 0.81%, respectively, and lateral interframe displacement reduced from 4.58 to 2.01 mm, respectively (p-value = 5.07e-23). Overall, reduced cardiac tissue motion and increased interframe CC coefficient (baseline = 0.43 vs. coupler with suction = 0.80) allow for more accurate PA unmixing.

CONCLUSIONS

Epicardial US-mediated imaging with a vacuum coupler reduces cardiac motion artifact, providing a consistent sampling of an intended region of interest (ROI) over multiple cardiac cycles. This could help facilitate the development of advanced US-mediated imaging, which is often hindered by cardiac motion. Stable implementation of these imaging techniques could allow for intra-operative assessments of local cardiac perfusion as well as tissue characterization.

Multi-View 3D Transesophageal Echocardiography Registration and Volume Compounding for Mitral Valve Procedure Planning

Three-dimensional ultrasound mosaicing can increase image quality and expand the field of view. However, limited work has been done applying these compounded approaches for cardiac procedures focused on the mitral valve. For procedures targeting the mitral valve, transesophageal echocardiography (TEE) is the primary imaging modality used as it provides clear 3D images of the valve and surrounding tissues. However, TEE suffers from image artefacts and signal dropout, particularly for structures lying below the valve, including chordae tendineae, making it necessary to acquire alternative echo views to visualize these structures. Due to the limited field of view obtainable, the entire ventricle cannot be directly visualized in sufficient detail from a single image acquisition in 3D. We propose applying an image compounding technique to TEE volumes acquired from a mid-esophageal position and several transgastric positions in order to reconstruct a high-detail volume of the mitral valve and sub-valvular structures. This compounding technique utilizes both fully and semi-simultaneous group-wise registration to align the multiple 3D volumes, followed by a weighted intensity compounding step based on the monogenic signal. This compounding technique is validated using images acquired from two excised porcine mitral valve units and three patient data sets. We demonstrate that this compounding technique accurately captures the physical structures present, including the mitral valve, chordae tendineae and papillary muscles. The chordae length measurement error between the compounded ultrasound and ground-truth CT for two porcine valves is reported as 0.7 ± 0.6 mm and 0.6 ± 0.6 mm.

Decorrelated compounding improves lesion signal-to-noise ratio of low-contrast lesions in synthetic transmit aperture ultrasound imaging

Speckle variance in ultrasound images limits the detection of low-contrast targets. In conventional compounding, multiple correlated sub-images are generated and then averaged to reduce the speckles at the cost of resolution loss. In this paper, a decorrelation procedure was applied to the correlated sub-images to further reduce speckle variance. Lesion signal-to-noise-ratio (lSNR), which combines the effect of speckle reduction and resolution loss, was used as an indicator of the detectability of lesions. The lSNR of the hyperechoic lesion in the simulated and experimental images using decorrelated compounding was increased by 122% and 89%, respectively, compared to the delay-and-sum method.

Full cardiac cycle asynchronous temporal compounding of 3D echocardiography images

It is important to improve echocardiography image quality, because the accuracy of echocardiographic assessment and diagnosis relies on image quality. Previous work on 2D temporal image compounding for image frames with matching cardiac phases (synchronous), and for temporally neighbouring image frames (asynchronous) over small ranges of time frames showed good improvement to image quality. Here, we extend this by performing asynchronous temporal compounding to echocardiographic images in 3D, involving all frames within a cardiac cycle, via a robust 3D cardiac motion estimation algorithm to describe the large image deformations. After compounding, the images can be reanimated via the motion model. Various methods of fusing image frames together are tested, including mean, max, and wavelet methods, and outlier rejection algorithms. The compounding algorithm is applied on 3D human adult, porcine adolescent, and human fetal echocardiography images. Results show significant improvements to contrast-to-noise ratio (CNR) and boundary clarity, and significantly decreased variability in manual quantification of cardiac chamber volumes after compounding. Interestingly, compounding can extend the field of view of the echo images, by reconstructing cardiac structures that momentarily exceeded the field of view, using the motion estimation algorithm to calculate their locations outside the field of view during these time periods. Although all compounding methods provide general improvements, the mean method led to blurred boundaries, while the max methods led to high variability of CNR. Outlier rejection algorithms were found to be useful in addressing these weaknesses.

Linking statistical shape models and simulated function in the healthy adult human heart

Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.

Resolution and Speckle Reduction in Cardiac Imaging

Cardiac imaging depends on clear visualization of several different structural and functional components to determine left ventricular and overall cardiac health. Ultrasound imaging is confounded by the characteristic speckle texture resulting from subwavelength scatterers in tissues, which is similar to a multiplicative noise on underlying tissue structure. Reduction of this texture can be achieved through physical means, such as spatial or frequency compounding, or through adaptive image processing. Techniques in both categories require a tradeoff of resolution for speckle texture reduction, which together contribute to overall image quality and diagnostic value. We evaluate this tradeoff for cardiac imaging tasks using spatial compounding as an exemplary speckle reduction method. Spatial compounding averages the decorrelated speckle patterns formed by views of a target from multiple subaperture positions to reduce the texture at the expense of active aperture size (and, in turn, lateral resolution). We demonstrate the use of a novel synthetic aperture focusing technique to decompose harmonic backscattered data from focused beams to their aperture-domain spatial frequency components to enable combined transmit and receive compounding. This tool allows the evaluation of matched data sets from a single acquisition over a wide range of spatial compounding conditions. We quantified the tradeoff between resolution and texture reduction in an imaging phantom and demonstrated improved lesion detectability with increasing levels of spatial compounding. We performed a cardiac ultrasound on 25 subjects to evaluate the degree of compounding useful for diagnostic imaging. Of these, 18 subjects were included in both qualitative and quantitative analysis. We found that compounding improved detectability of the endocardial border according to the generalized contrast-to-noise ratio in all cases, and more aggressive compounding made further improvements in ten out of 18 cases. Three expert reviewers evaluated the images for their usefulness in several diagnostic tasks and ranked four compounding conditions ("none," "low," "medium," and "high"). Contrary to the quantitative metrics that suggested the use of high levels of compounding, the reviewers determined that "low" was usually preferred (77.9%), while "none" or "medium" was selected in 21.2% of cases. We conclude with a brief discussion of the generalization of these results to other speckle reduction methods using the imaging phantom data.

Frequency division denoising algorithm based on VIF adaptive 2D-VMD ultrasound image

Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis. To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. First, the ultrasound image was decomposed into a series of sub-modal images using 2D variational mode decomposition (2D-VMD), and adaptively determined 2D-VMD parameter K value based on visual information fidelity (VIF) criterion. Then, an anisotropic diffusion filter was used to denoise low-frequency sub-modal images, and a 3D block matching algorithm (BM3D) was used to reduce noise for high-frequency images with high noise. Finally, each sub-modal image was reconstructed after processing to obtain the denoised ultrasound image. In the comparative experiments of synthetic, simulation, and real images, the performance of this method was quantitatively evaluated. Various results show that the ability of this algorithm in denoising and maintaining structural details is significantly better than that of other algorithms.

Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions

Echocardiography (echo) is a critical tool in diagnosing various cardiovascular diseases. Despite its diagnostic and prognostic value, interpretation and analysis of echo images are still widely performed manually by echocardiographers. A plethora of algorithms has been proposed to analyze medical ultrasound data using signal processing and machine learning techniques. These algorithms provided opportunities for developing automated echo analysis and interpretation systems. The automated approach can significantly assist in decreasing the variability and burden associated with manual image measurements. In this paper, we review the state-of-the-art automatic methods for analyzing echocardiography data. Particularly, we comprehensively and systematically review existing methods of four major tasks: echo quality assessment, view classification, boundary segmentation, and disease diagnosis. Our review covers three echo imaging modes, which are B-mode, M-mode, and Doppler. We also discuss the challenges and limitations of current methods and outline the most pressing directions for future research. In summary, this review presents the current status of automatic echo analysis and discusses the challenges that need to be addressed to obtain robust systems suitable for efficient use in clinical settings or point-of-care testing.

Row-Column-Based Coherence Imaging Using a 2-D Array Transducer: A Row-Based Implementation

Reverberations from tissues around the heart often result in cluttered echocardiograms with reduced diagnostic value. As a consequence, some patients must undergo more expensive and, in some cases, invasive imaging modalities. Coherence-based beamforming can suppress the effect of incoherent reverberations compared with the coherent signal. In some cases, these incoherent reverberations are received by only a part of the aperture. However, the coherence-based techniques, when used on a 1-D array transducer, do not take this into account. We propose an extension of coherence imaging method when using a 2-D array transducer and test a row-based implementation of this extension on two in vitro scenarios and four in vivo cases. The results show that the proposed method improves the lateral resolution compared with the (already improved) resolution with conventional coherence imaging. Furthermore, it gives up to 28% increase in generalized contrast-to-noise ratio (gCNR) (improved detection probability) when incoherent reverberations are partly received by the transducer in the elevation direction.

Studying the Origin of Reverberation Clutter in Echocardiography: In Vitro Experiments and In Vivo Demonstrations

Clutter in echocardiography hinders the visualization of the heart and reduces the diagnostic value of the images. The detailed mechanisms that generate clutter are, however, not well understood. We present five different hypotheses for generation of clutter based on reverberation artifact with a focus on apical four-chamber view echocardiograms. We demonstrate the plausibility of our hypotheses by in vitro experiments and by comparing the results with in vivo recordings from four volunteers. The results show that clutter in echocardiography can be originated both at structures that lie in the ultrasound beam path and at those that are outside the imaging plane. We show that reverberations from echogenic structures outside the imaging plane can make clutter over the image if the ultrasound beam gets deflected out of its intended path by specular reflection at the ribs. Different clutter types in the in vivo examples show that the appearance of clutter varies, depending on the tissue from which it originates. The results of this work can be applied to improve clutter reduction techniques or to design ultrasound transducers that give higher quality cardiac images. The results can also help cardiologists have a better understanding of clutter in echocardiograms and acquire better images based on the type and the source of the clutter.

Robust Optical Flow Estimation in Cardiac Ultrasound Images Using a Sparse Representation

This paper introduces a robust 2-D cardiac motion estimation method. The problem is formulated as an energy minimization with an optical flow-based data fidelity term and two regularization terms imposing spatial smoothness and the sparsity of the motion field in an appropriate cardiac motion dictionary. Robustness to outliers, such as imaging artefacts and anatomical motion boundaries, is introduced using robust weighting functions for the data fidelity term as well as for the spatial and sparse regularizations. The motion fields and the weights are computed jointly using an iteratively re-weighted minimization strategy. The proposed robust approach is evaluated on synthetic data and realistic simulation sequences with available ground-truth by comparing the performance with state-of-the-art algorithms. Finally, the proposed method is validated using two sequences of in vivo images. The obtained results show the interest of the proposed approach for 2-D cardiac ultrasound imaging.

Towards Ultrasound Everywhere: A Portable 3D Digital Back-End Capable of Zone and Compound Imaging

Ultrasound imaging is a ubiquitous diagnostic technique, but does not fit the requirements of the telemedicine approach, because it relies on the real-time manipulation and image recognition skills of a trained expert, called sonographer. Sonographers are only available in hospitals and clinics, negating or at least delaying access to ultrasound scans in many locales-rural areas, developing countries-as well as in medical rescue operations. Telesonography would require an advanced imager that supports three-dimensional (3-D) acquisition; this would allow untrained operators to acquire broad scans and upload them remotely for diagnosis. Such advanced imagers do exist, but do not meet several other requirements for telesonography, such as being portable, inexpensive, and sufficiently low power to enable battery operation. In this work, we present our prototype of the first portable 3-D digital ultrasound back-end system. The prototype is implemented in a single midrange Xilinx field programmable gate array (FPGA), for an estimated power consumption of 5 W. The device supports up to 1024 input channels, which is state of the art and could be scaled further, and supports multiple image reconstruction modes. We evaluate the resource utilization of the FPGA and provide various quality metrics to ascertain the output image quality.

High-Dynamic-Range Ultrasound: Application for Imaging Tendon Pathology

Raw ultrasound (US) signal has a very high dynamic range (HDR) and, as such, is compressed in B-mode US using a logarithmic function to fit within the dynamic range of digital displays. However, in some cases, hyper-echogenic tissue can be overexposed at high gain levels with the loss of hypo-echogenic detail at low gain levels. This can cause the loss of anatomic detail and tissue texture and frequent and inconvenient gain adjustments, potentially affecting the diagnosis. To mitigate these drawbacks, we employed tone mapping operators (TMOs) in HDR photography to create HDR US. We compared HDR US produced from three different popular TMOs (Reinhard, Drago and Durand) against conventional US using a simulated US phantom and in vivo images of patellar tendon pathologies. Based on visual inspection and assessments of structural fidelity, image entropy and contrast-to-noise ratio metrics, Reinhard and Drago TMOs substantially improved image detail and texture.

High dynamic range ultrasound imaging

PURPOSE

High dynamic range (HDR) imaging is a popular computational photography technique that has found its way into every modern smartphone and camera. In HDR imaging, images acquired at different exposures are combined to increase the luminance range of the final image, thereby extending the limited dynamic range of the camera. Ultrasound imaging suffers from limited dynamic range as well; at higher power levels, the hyperechogenic tissue is overexposed, whereas at lower power levels, hypoechogenic tissue details are not visible. In this work, we apply HDR techniques to ultrasound imaging, where we combine ultrasound images acquired at different power levels to improve the level of detail visible in the final image.

METHODS

Ultrasound images of ex vivo and in vivo tissue are acquired at different acoustic power levels and then combined to generate HDR ultrasound (HDR-US) images. The performance of five tone mapping operators is quantitatively evaluated using a similarity metric to determine the most suitable mapping for HDR-US imaging.

RESULTS

The ex vivo and in vivo results demonstrated that HDR-US imaging enables visualizing both hyper- and hypoechogenic tissue at once in a single image. The Durand tone mapping operator preserved the most amount of detail across the dynamic range.

CONCLUSIONS

Our results strongly suggest that HDR-US imaging can improve the utility of ultrasound in image-based diagnosis and procedure guidance.

Enhanced Wiener filter for ultrasound image restoration

BACKGROUND AND OBJECTIVE

Speckle phenomenon strongly affects UltraSound (US) images. In the last years, several efforts have been done in order to provide an effective denoising methodology. Although good results have been achieved in terms of noise reduction effectiveness, most of the proposed approaches are not characterized by low computational burden and require the supervision of an external operator for tuning the input parameters.

METHODS

Within this manuscript, a novel approach is investigated, based on Wiener filter. Working in the frequency domain, it is characterized by high computational efficiency. With respect to classical Wiener filter, the proposed Enhanced Wiener filter is able to locally adapt itself by tuning its kernel in order to combine edges and details preservation with effective noise reduction. This characteristic is achieved by implementing a Local Gaussian Markov Random Field for modeling the image. Due to its intrinsic characteristics, the computational burden of the algorithm is sensibly low compared to other widely adopted filters and the parameter tuning effort is minimal, being well suited for quasi real time applications.

RESULTS

The approach has been tested on both simulated and real datasets, showing interesting performances compared to other state of art methods.

CONCLUSIONS

A novel denoising method for UltraSound images is proposed. The approach is able to combine low computational burden with interesting denoising performances and details preservation.

Dynamic Enhancement of B-Mode Cardiac Ultrasound Image Sequences

Limited contrast, along with speckle and acoustic noise, can reduce the diagnostic value of echocardiographic images. This study introduces dynamic histogram-based intensity mapping (DHBIM), a novel approach employing temporal variations in the cumulative histograms of cardiac ultrasound images to contrast enhance the imaged structures. DHBIM is then combined with spatial compounding to compensate for noise and speckle. The proposed techniques are quantitatively assessed (32 clinical data sets) employing (i) standard image quality measures and (ii) the repeatability of routine clinical measurements, such as chamber diameter and wall thickness. DHBIM introduces a mean increase of 120.9% in tissue/chamber detectability, improving the overall repeatability of clinical measurements by 17%. The integrated approach of DHBIM followed by spatial compounding provides the best overall enhancement of image quality and diagnostic value, consistently outperforming the individual approaches and achieving a 401.4% average increase in tissue/chamber detectability with an associated 24.3% improvement in the overall repeatability of clinical measurements.