Utility of Angle Correction for Hemodynamic Measurements with Doppler Echocardiography.

OBJECTIVES: The routine application angle correction (AnC) in hemodynamic measurements with transesophageal echocardiography currently is not recommended but potentially could be beneficial. The authors hypothesized that AnC can be applied reliably and may change grading of aortic stenosis (AS). DESIGN: Retrospective analysis. SETTING: Single institution, university hospital. PARTICIPANTS: During phase I, use of AnC was assessed in 60 consecutive patients with intraoperative transesophageal echocardiography. During phase II, 129 images from a retrospective cohort of 117 cases were used to quantify AS by mean pressure gradient. INTERVENTIONS: A panel of observers used custom-written software in Java to measure intra-individual and inter-individual correlation in AnC application, correlation with preoperative transthoracic echocardiography gradients, and regrading of AS after AnC. MEASUREMENTS AND MAIN RESULTS: For phase I, the median AnC was 21 (16-35) degrees, and 17% of patients required no AnC. For phase II, the median AnC was 7 (0-15) degrees, and 37% of assessed images required no AnC. The mean inter-individual and intra-individual correlation for AnC was 0.50 (95% confidence interval [CI] 0.49-0.52) and 0.87 (95% CI 0.82-0.92), respectively. AnC did not improve agreement with the transthoracic echocardiography mean pressure gradient. The mean inter-rater and intra-rater agreement for grading AS severity was 0.82 (95% CI 0.81-0.83) and 0.95 (95% CI 0.91-0.95), respectively. A total of 241 (7%) AS gradings were reclassified after AnC was applied, mostly when the uncorrected mean gradient was within 5 mmHg of the severity classification cutoff. CONCLUSIONS: AnC can be performed with a modest inter-rater and intra-rater correlation and high degree of inter-rater and intra-rater agreement for AS severity grading.

Comparison of Newer Hand-Held Ultrasound Devices for Post-Dive Venous gas Emboli Quantification to Standard Echocardiography.

Decompression sickness (DCS) can result from the growth of bubbles in tissues and blood during or after a reduction in ambient pressure, for example in scuba divers, compressed air workers or astronauts. In scuba diving research, post-dive bubbles are detectable in the venous circulation using ultrasound. These venous gas emboli (VGE) are a marker of decompression stress, and larger amounts of VGE are associated with an increased probability of DCS. VGE are often observed for hours post-dive and differences in their evolution over time have been reported between individuals, but also for the same individual, undergoing a same controlled exposure. Thus, there is a need for small, portable devices with long battery lives to obtain more ultrasonic data in the field to better assess this inter- and intra-subject variability. We compared two new handheld ultrasound devices against a standard device that is currently used to monitor post-dive VGE in the field. We conclude that neither device is currently an adequate replacement for research studies where precise VGE grading is necessary.

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.

Improved Visualization in Difficult-to-Image Stress Echocardiography Patients Using Real-Time Harmonic Spatial Coherence Imaging.

Stress echocardiography is used to detect myocardial ischemia by evaluating cardiovascular function both at rest and at elevated heart rates. Stress echocardiography requires excellent visualization of the left ventricle (LV) throughout the cardiac cycle. However, LV endocardial border visualization is often negatively impacted by high levels of clutter associated with patient obesity, which has risen dramatically worldwide in recent decades. Short-lag spatial coherence (SLSC) imaging has demonstrated reduced clutter in several applications. In this work, a computationally efficient formulation of SLSC was implemented into an object-oriented graphics processing unit-based software beamformer, enabling real-time (>30 frames per second) SLSC echocardiography on a research ultrasound scanner. The system was then used to image 15 difficult-to-image stress echocardiography patients in a comparison study of tissue harmonic imaging (THI) and harmonic spatial coherence imaging (HSCI). Video clips of four standard stress echocardiography views acquired with either THI or HSCI were provided in random shuffled order to three experienced readers. Each reader rated the visibility of 17 LV segments as "invisible," "suboptimally visualized," or "well visualized," with the first two categories indicating a need for contrast agent. In a symmetry test unadjusted for patientwise clustering, HSCI demonstrated a clear superiority over THI ( ). When measured on a per-patient basis, the median total score significantly favored HSCI with . When collapsing the ratings to a two-level scale ("needs contrast" versus "well visualized"), HSCI once again showed an overall superiority over THI, with by McNemar test adjusted for clustering.

Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could circumvent border detection and instead would estimate the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images. METHODS: Machine learning algorithm was developed and trained to automatically estimate LVEF on a database of >50 000 echocardiographic studies, including multiple apical 2- and 4-chamber views (AutoEF, BayLabs). Testing was performed on an independent group of 99 patients, whose automated EF values were compared with reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Consistency was assessed by mean absolute deviation among automated estimates from different combinations of apical views. Finally, sensitivity and specificity of detecting of EF ≤35% were calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers. RESULTS: Automated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (mean absolute deviation =2.9%) and excellent agreement with the reference values: r=0.95, bias=1.0%, limits of agreement =±11.8%, with sensitivity 0.90 and specificity 0.92 for detection of EF ≤35%. This was similar to clinicians' measurements: r=0.94, bias=1.4%, limits of agreement =±13.4%, sensitivity 0.93, specificity 0.87. CONCLUSIONS: Machine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared with reference values provided by an expert panel.