Post formation processing of cardiac ultrasound data for enhancing image quality and diagnostic value

Cardiovascular diseases (CVDs) constitute a leading cause of death, including premature death, in the developed world. The early diagnosis and treatment of CVDs is therefore of great importance. Modern imaging modalities enable the quantification and analysis of the cardiovascular system and provide researchers and clinicians with valuable tools for the diagnosis and treatment of CVDs. In particular, echocardiography offers a number of advantages, compared to other imaging modalities, making it a prevalent tool for assessing cardiac morphology and function. However, cardiac ultrasound images can suffer from a range of artifacts reducing their image quality and diagnostic value. As a result, there is great interest in the development of processing techniques that address such limitations. This thesis introduces and quantitatively evaluates four methods that enhance clinical cardiac ultrasound data by utilising information which until now has been predominantly disregarded. All methods introduced in this thesis utilise multiple partially uncorrelated instances of a cardiac cycle in order to acquire the information required to suppress or enhance certain image features. No filtering out of information is performed at any stage throughout the processing. This constitutes the main differentiation to previous data enhancement approaches which tend to filter out information based on some static or adaptive selection criteria. The first two image enhancement methods utilise spatial averaging of partially uncorrelated data acquired through a single acoustic window. More precisely, Temporal Compounding enhances cardiac ultrasound data by averaging partially uncorrelated instances of the imaged structure acquired over a number of consecutive cardiac cycles. An extension to the notion of spatial compounding of cardiac ultrasound data is 3D-to-2D Compounding, which presents a novel image enhancement method by acquiring and compounding spatially adjacent (along the elevation plane), partially uncorrelated, 2D slices of the heart extracted as a thin angular sub-sector of a volumetric pyramid scan. Data enhancement introduced by both approaches includes the substantial suppression of tissue speckle and cavity noise. Furthermore, by averaging decorrelated instances of the same cardiac structure, both compounding methods can enhance tissue structures, which are masked out by high levels of noise and shadowing, increasing their corresponding tissue/cavity detectability. The third novel data enhancement approach, referred as Dynamic Histogram Based Intensity Mapping (DHBIM), investigates the temporal variations within image histograms of consecutive frames in order to (i) identify any unutilised/underutilised intensity levels and (ii) derive the tissue/cavity intensity threshold within the processed frame sequence. Piecewise intensity mapping is then used to enhance cardiac ultrasound data. DHBIM introduces cavity noise suppression, enhancement of tissue speckle information as well as considerable increase in tissue/cavity contrast and detectability. A data acquisition and analysis protocol for integrating the dynamic intensity mapping along with spatial compounding methods is also investigated. The linear integration of DHBIM and Temporal Compounding forms the fourth and final implemented method, which is also quantitatively assessed. By taking advantage of the benefits and compensating for the limitations of each individual method, the integrated method suppresses cavity noise and tissue speckle while enhancing tissue/cavity contrast as well as the delineation of cardiac tissue boundaries even when heavily corrupted by cardiac ultrasound artifacts. Finally, a novel protocol for the quantitative assessment of the effect of each data enhancement method on image quality and diagnostic value is employed. This enables the quantitative evaluation of each method as well as the comparison between individual methods using clinical data from 32 patients. Image quality is assessed using a range of quantitative measures such as signal-to-noise ratio, tissue/cavity contrast and detectability index. Diagnostic value is assessed through variations in the repeatability level of routine clinical measurements performed on patient cardiac ultrasound scans by two experienced echocardiographers. Commonly used clinical measures such as the wall thickness of the Interventricular Septum (IVS) and the Left Ventricle Posterior Wall (LVPW) as well as the cavity diameter of the Left Ventricle (LVID) and Left Atrium (LAD) are employed for assessing diagnostic value

High Frame Rate Ultrasound Velocimetry of Fast Blood Flow Dynamics

In this thesis we develop and validate high frame rate ultrasound sequences for use with echo-particle image velocimetry (in 2D and 3D), with the aim of measuring the high velocity blood flow patterns in the left ventricle and abdominal aorta

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Magnetic resonance coronary vessel wall imaging with highly efficient respiratory motion correction

There is a need for a noninvasive imaging technique for use in longitudinal studies of sub-clinical coronary artery disease. Magnetic resonance (MR) can be used to selectively and non-invasively image the coronary wall without the use of ionising radiation. However, high-resolution 3D studies are often time consuming and unreliable, as data acquisition is generally gated to a small window of diaphragm positions around end-expiration which results in inherently poor and variable respiratory efficiency. This thesis describes the development and application of a novel technique (beat-to-beat respiratory motion correction (B2B-RMC)) for correcting respiratory motion in 3D spiral MR coronary imaging. This technique uses motion of the epicardial fat surrounding the artery as a surrogate for the motion of the artery itself and enables retrospective motion correction with respiratory efficiency close to 100%. This thesis first describes an assessment of the performance of B2B-RMC using a purpose built respiratory motion phantom with realistic coronary artery test objects. Subsequently, MR coronary angiography studies in healthy volunteers show that the respiratory efficiency of B2B-RMC far exceeds that of conventional navigator gating, yet the respiratory motion correction is equally effective. The performance and reproducibility of 3D spiral imaging with B2B-RMC for assessment of the coronary artery vessel wall is subsequently compared to that of commonly used 2D navigator gated techniques. The results demonstrate the high performance, reproducibility and reliability of 3D spiral imaging with B2B-RMC when data acquisition is gated to alternate cardiac cycles. Using this technique, a further in-vivo study demonstrates thickening of the coronary vessel wall with age in healthy subjects and these results are shown to be consistent with outward remodelling of the vessel wall. Finally, the performance of B2B-RMC in a variety of coronary vessel wall applications, including in a small cohort of patients with confirmed coronary artery disease, is presented

Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Performance Analysis and Optimization of 2-D Cardiac Strain Imaging for Clinical Applications

Heart disease has remained the deadliest disease in the United States for the past 100 years. Imaging methods are frequently employed in cardiology in order to help clinicians diagnose the specific type of heart disease and to guide treatment decisions. Ultrasound is the most frequently used imaging modality in cardiology because it is inexpensive, portable, easy to use, and extremely safe for patients. Using a variety of imaging processing techniques, deformations exhibited by the cardiac tissue during contraction can be imaged with ultrasound and used as an indicator of myocardial health. This dissertation will demonstrate the clinical implementation of two ultrasound-based strain estimation techniques developed in the Ultrasound and Elasticity Imaging Laboratory at Columbia University. Each of the two imaging methods will be tailored for clinical applications using techniques for optimal strain estimation derived from ultrasound and imaging processing theory. The motion estimation rate (MER) used for strain estimation is examined in the context of the theoretical Strain Filter and used to increase the precision of axial strain estimation. Diverging beam sequences are used to achieve full-view high MER imaging within a single heartbeat. At approximately 500 Hz, the expected elastographic signal-to-noise ratio (E(SNRe|ε)) of the axial strain becomes single-peaked, indicating an absence of “peak-hopping” errors which can severely corrupt strain estimation. In order to mediate the tradeoff in spatial resolution resulting from the use of diverging beams, coherent spatial compounding is used to increase the accuracy of the lateral strain estimation, resulting in a more physiologic strain profile. A sequence with 5 coherently compounded diverging waves is used at 500 Hz to improve the radial SNRe of the strain estimation compared to a single-source diverging sequence at 500 Hz. The first technique, Myocardial Elastography (ME), is used in conjunction with an intracardiac echocardiography (ICE) system to image the formation of thermal ablation lesions in vivo using a canine model (n=6). By comparing the systolic strain before and after the formation of a lesion, lesion maps are generated which allow for the visualization of the lesion in real-time during the procedure. A good correlation is found between the lesion maps and the actual lesion volume as measured using gross pathology (r2=0.86). The transmurality of the lesions are also shown to be in good agreement with gross pathology. Finally, the feasibility of imaging gaps between neighboring lesions is established. Lesion size and the presence of gaps have been associated with the success rate of cardiac ablation procedures, demonstrating the value of ME as a potentially useful technique for clinicians to help improve patient outcomes following ablation procedures. The second technique, Electromechanical Wave Imaging (EWI), is implemented using a transthoracic echocardiography system in a study of heart failure patients (n=16) and healthy subjects (n=4). EWI uses the transient inter-frame strains to generate maps of electromechanical activation, which are then used to distinguish heart failure patients from healthy controls (p<.05). EWI was also shown to be capable of distinguishing responders from non-responders to cardiac resynchronization therapy (CRT) on the basis of the activation time of the lateral wall. These results indicate that EWI could be used as an adjunct tool to monitor patient response to CRT, in addition to helping guide lead placement prior to device implantation