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I. INTRODUCTION

ECHOCARDIOGRAPHY provides a versatile, real-time diagnostic tool with no adverse secondary effects, capable of acquiring images of high spatial and temporal resolution at relatively low operational cost [1]. The wide range of available imaging techniques makes cardiac ultrasound a prevalent tool for the qualitative and quantitative assessment of cardiac morphology and function in both 2-D and 3-D. Cardiac ultrasound images can be acquired 1) through the thorax of the patient, also known as transthoracic echocardiography (TTE), or 2) from inside the esophagus of the patient (by utilizing specialized acquisition probes), also known as transesophageal echocardiography (TEE) [2]. TEE can generate high-quality images. However, the extended acquisition time and personnel requirements along with patient discomfort currently limit its clinical use, making TTE the common approach in clinical examinations. However, transthoracic cardiac ultrasound images are often incomplete (partial heart coverage) and suffer from a range of artifacts as a consequence of the interaction of the transmitted ultrasound signals with anatomic structures of the examined body. Structures such as bone, lungs (air), and fat have a direct limiting effect on the quality and diagnostic value of the acquired cardiac images. Furthermore, transthoracic cardiac ultrasound images a constantly and rapidly moving structure through the patient’s rib cage. The nature of such a challenging acquisition enhances the manifestation of common medical ultrasound artifacts (Fig. 1). Cardiac ultrasound images suffer from acoustic noise due to a range of acoustical phenomena (artifacts) such as reverberations, side-lobes, and grating-lobes [1], [2]. The extent of each artifact on the imaged cardiac structures depends on both the acquisition technology utilized as well as the echogenicity of the patient. For example, modern phased-array transducers minimize the effect of grating-lobes by using an adequately small pitch (less than half the wavelength of the transmitted signal) between the elements of the array. On the other hand, the effect of side-lobes, especially when transmitted in out-of-scan-plane directions, is mostly related to the proximity of extra-cardiac structures such as the lung and rib-cage bones. Furthermore, many instruments, especially phased array transducers, suffer from near-field clutter or ring-down effect [2]. Near-field clutter manifests itself at the top part of the scan as a zone with a high level of stationary noise that gradually declines to zero for increasing scanning depth [2]. Finally, oblique incidence angles of the transmitted ultrasound beam with respect to an imaged structure may result in low contrast between the cardiac tissue and chamber. A high-gain setting, possibly in an attempt to compensate for the low tissue signal, may result in additional amplifier noise mostly present in cardiac chambers. While not an exhaustive list, the aforementioned artifacts corrupt the imaged cardiac structures and from an imaging perspective can be considered as noise. Imaging of relatively small and rapidly moving structures such as the cardiac valves introduces additional challenges. Besides the limited delineation as a result of noise, the structure may move into and out of the scan plane due to the cardiac and respiratory motion. Furthermore, reverberations and shadowing appear due to the interaction of the transmitted ultrasound with high reflective and attenuating structures, such as the patient’s rib cage and lungs that lie in the path of the ultrasound beam. Such artifacts may appear momentarily or alter their position and orientation throughout a scan due to small movements of the transducer combined with the patient’s respiration motion, obscuring the imaging of portions of the examined cardiac structure [1], [2]. Speckle is a type of acoustic phenomenon responsible for the granular appearance of ultrasound images. Speckle is a result of constructive and destructive interference of echoes produced by scattering of ultrasound at random, small-scale, tissue inhomogeneities. Speckle is a direct consequence of 1) the stochastic nature of the reflectivity of scattering media, and 2) the coherent nature of the piezoelectric transducer. Several studies provide detailed information on the origin of speckle and its statistical properties [3]–[۵]. The granular pattern of speckle can sometimes be considered as an undesirable property since it may obscure fine anatomic detail. In cardiac ultrasound images, tissue speckle combined with high levels of chamber noise can limit the delineation of cardiac structures. Furthermore, the granular appearance of the images limits the application of postformation processing techniques such as image registration and segmentation. Therefore, means for suppressing noise and speckle can possibly improve the image quality and diagnostic value of a cardiac ultrasound dataset. On the other hand, speckle motion may be utilized in tissue velocity and strain estimation methods such as speckle tracking echocardiography (STE) [6] and radio frequency (RF)-based strain imaging [7]. Both techniques assess global and regional cardiac function by tracking the movement of speckle patterns over time. They provide a promising alternative to 1) tagged cardiac MRI for assessing left ventricular deformation and torsion [8], and 2) color Doppler for strain imaging, addressing problems associated with angle dependence [7]. Detailed descriptions on the principles of STE as well as current and future clinical applications are provided in [6], [9]–[۱۲]. Similarly, more information on RF-based strain imaging is provided in [7], [13]–[۱۷]. Image processing methods that enhance the intensity dynamic range (contrast) within speckle may improve the accuracy and robustness of such existing techniques that tackle speckle motion. Over the last three decades, a number of advances in data acquisition have substantially improved cardiac ultrasound image quality. Nevertheless, a considerable portion of current cardiac ultrasound images demonstrate low image quality and limited diagnostic value. In 2008, a systematic study was performed on routine patients going through the echocardiography department of the Western General Hospital (Edinburgh). The results of the study have been used for educational purposes in the department and have not been published yet. The study, performed using both older and the state-of-theart cardiac ultrasound systems, demonstrated that about 33% of the datasets are of high (clear cardiac structures, enabling reliable clinical measurements), 33% are of average (partially corrupted cardiac structures, limiting the accuracy and precision of clinical measurements), and 33% are of low (highly corrupted cardiac structures, limiting and many times prohibiting clinical measurements) image quality and diagnostic value. While the state-of-the art ultrasound system improved the quality of the acquired data, the findings were heavily dependent on the echogenicity of the patients. Furthermore, a number of postformation image processing techniques such as image registration, image segmentation, data classification, and texture analysis have been introduced for cardiac data acquired using modalities such as CT and MRI [18]–[۲۰]. These techniques enable the development of tools and protocols that enhance the accuracy, robustness, and repeatability of the diagnostic process. Over the last few years, similar postprocessing techniques have been attempted on cardiac ultrasound images [21]–[۲۴]. Recent advances in real-time 3-D echocardiography (RT3DE) extend the potential application of such techniques [25], [26]. However, while postformation image processing techniques may work on high quality images, high levels of noise, low contrast, speckle, and shadowing limit their effectiveness in a considerable proportion of clinical cardiac ultrasound datasets. The development of effective postprocessing methods that enhance the quality and diagnostic value of cardiac ultrasound images is, therefore, desirable. Postprocessing techniques do not require hardware modifications and can be applied to both existing and new data. This study attempts to provide a thorough review of such image-enhancement postprocessing techniques for cardiac ultrasound images.

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