Quantitative cardiac output assessment using 4D ultrafast Doppler imaging: an in vitro study

International audienceBackground, Motivation and Objective Echocardiography is routinely used in the clinic to evaluate the cardiac function. Anatomical indexes such as ventricular volume measurements or functional indexes such Cardiac Output are performed using standard echocardiography. However, 2D dimensional measurements induce inter-operator variability and standard 3D measurements do not have the sufficient volume rate to evaluate functional indexes. Moreover, the accuracy of flow velocity estimates is strongly reduced by the angular dependence of Doppler measurements. In this study, we propose to use 4D ultrafast Doppler to evaluate flow rates in a pipe to demonstrate the potentiality of performing Cardiac Output measurements without assumptions on the valve geometry and without angular dependence. Statement of Contribution/Methods An ultrasonic matrix array probe (central frequency 2.5MHz, 1024 elements, pitch 0.3 mm, bandwith 60%, Vermon, France) connected to a 1024 channels ultrasound scanner prototype was used to image the pipe output in three dimensions. 500 diverging waves (angular aperture 80°) were emitted at a volume rate of 2000 volumes/s during 250 ms. Color Doppler volumes (quantitative flow speed volumes) were computed by calculating the first moment of the Doppler spectrums in each voxel. The pipe flow rates (N=7) were calculated by integrating directly the flow speed over the cross section of the pipe. Results/Discussion The measured flow rates were found to be in a good agreement with the flowmeter values used as a gold standard (= 0.96). The four dimensional nature of the acquisition has the potential to enable the calculation of the Cardiac Output in vivo in patients without the need of making any assumption on the valve geometry or the direction of the ultrasonic beam usually responsible for errors

Ultrasound Elastography: Deep Learning Approach

Ultrasound elastography images the elasticity of a biological tissue. Conventional algorithms for ultrasound elastography suffer from different noises severely compromising the quality of time-delay estimation. Calculation of time-delay estimation is a key component of strain estimation. However, time-delay estimation is analogous to optical flow estimation, a classical computer vision problem. Deep learning networks have reported recent success in optical flow estimation compared to the conventional techniques. Classical ultrasound elastography algorithms have been unable to provide a single solution to both commonly known issues of noise and computation time. Deep learning techniques have a bright prospect in addressing both issues. The goal of this thesis is to investigate whether optical flow estimation is translatable to ultrasound elastography as the core nature of both of these problems are analogous. In this thesis we aim to develop and train a robust deep neural network for ultrasound elastography. First, an efficient deep learning network trained for optical flow estimation is used for time-delay estimation. The initial time-delay estimation is further fine-tuned by optimizing a global cost function for generating high quality strain images. Simulation, phantom and clinical experiments show the robustness of the deep learning approach both quantitatively and qualitatively. Next, the weights of the deep learning network are fine-tuned using transfer learning technique for transferring the efficacy of optical flow estimation to time-delay estimation. The objective is to retain the robustness introduced by the deep learning network while enhancing the overall performance of the time-delay estimation in ultrasound elastography. Simulation and experimental phantom results show that the time-delay estimation has improved slightly after fine-tuning the weights using transfer learning

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

2-D and 3-D high frame-rate Pulse Wave Imaging for the characterization of focal vascular disease

Cardiovascular diseases are major causes of morbidity and mortality in Western-style populations. Atherosclerosis and Abdominal Aortic Aneurysms (AAAs) are two prevalent vascular diseases that may progress without symptoms and contribute to acute cardiovascular events such as stroke and AAA rupture, which are consistently among the leading causes of death worldwide. The imaging methods used in the diagnosis of these diseases, have been reported to present several limitations. Given that both are associated with mechanical changes in the arterial wall, imaging of the arterial mechanical properties may improve early disease detection and patient care. Pulse wave velocity (PWV) refers to the velocity at which arterial waves generated by ventricular ejection travel along the arterial tree. PWV is a surrogate marker of arterial stiffness linked to cardiovascular mortality. The foot-to-foot method that is typically used to calculate PWV suffers from errors of distance measurements and time-delay measurements. Additionally, a single PWV estimate is provided over a relatively long distance, thus inherently lacking the capability to provide regional arterial stiffness measurements. Pulse Wave Imaging (PWI) is a noninvasive, ultrasound-based technique for imaging the propagation of pulse waves along the wall of major arteries and providing a regional PWV value for the imaged artery. The aim of this work was to enable PWI to provide more localized PWV and stiffness measurements within the imaged arterial segment and to further extend it into a 2-D and 3-D technique for the detection and monitoring of focal vascular disease at high temporal and spatial resolution. The improved modality was integrated with blood flow imaging modalities aiming to render PWI a comprehensive methodology for the study of arterial biomechanics in vivo. Spatial information was increased with the introduction of piecewise PWI. This novel technique was used to measure PWV within small sub-regions of the imaged vessel in murine aneurysmal (n = 8) and atherosclerotic aortas (n = 11) in vivo. It provided PWV and stiffness maps while capturing the progressive arterial stiffening caused by atherosclerosis. PWI was further augmented with a sophisticated adaptive algorithm, enabling it to optimally partition the imaged artery into relatively homogeneous segments, automatically isolating arterial stiffness inhomogeneities. Adaptive PWI was validated in silicone phantoms consisting of segments of varying stiffness and then tested in murine aortas in vivo. Subsequently, the conventional tradeoff between spatial and temporal resolution was addressed with a plane wave compounding implementation of PWI, allowing the acquisition of full field of view frames at over 2000 Hz. A GPU-accelerated PWI post-processing framework was developed for the processing of the big bulk of generated data. The parameters of coherent compounding were optimized in vivo. The optimized sequences were then used in the clinic to assess the mechanical properties of atherosclerotic carotids (n=10) and carotids of patients after endarterectomy (n=7), a procedure to remove the plaque and restore blood flow to the brain. In the case of atherosclerotic patients undergoing carotid endarterectomy, the results were compared against the histology of the excised plaques. Investigation of the mechanical properties of plaques was also conducted for the first time with a high-frequency transducer (18.5 MHz). Additionally, 4-D PWI was introduced, utilizing high frame rate 3-D plane wave acquisitions with a 2-D matrix array transducer (16x16 elements, 2.5 MHz). A novel methodology for PWV estimation along the direction of pulse wave propagation was implemented and validated in silicone phantoms. 4-D PWI provided comprehensive views of the pulse wave propagation in a plaque phantom and the results were compared against conventional PWI. Finally, its feasibility was tested in the carotid arteries of healthy human subjects (n=6). PWVs derived in 3-D were within the physiological range and showed good agreement with the results of conventional PWI. Finally, PWI was integrated with flow imaging modalities (Color and Vector Doppler). Thus, full field-of-view, high frame-rate, simultaneous and co-localized imaging of the arterial wall dynamics and color flow as well as 2-D vector flow was implemented. The feasibility of both techniques was tested in healthy subjects (n=6) in vivo. The relationship between the timings of the flow and wall velocities was investigated at multiple locations of the imaged artery. Vector flow velocities were found to be aligned with the vessel’s centerline during peak systole in the common carotid artery and interesting flow patterns were revealed in the case of the carotid bifurcation Consequently, with the aforementioned improvements and the inclusion of 3-D imaging, PWI is expected to provide comprehensive information on the mechanical properties of pathological arteries, providing clinicians with a powerful tool for the early detection of vascular abnormalities undetectable on the B-mode, while also enabling the monitoring of fully developed vascular pathology and of the recovery of post-operated vessels