Cardiac Valve Event Timing in Echocardiography Using Deep Learning and Triplane Recordings.

Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES). In this work, we propose a deep learning approach that leverages triplane recordings to enhance detection of valve events in echocardiography. Our method demonstrates improved performance detecting six different events, including valve events conventionally associated with ED and ES. Of all events, we achieve an average absolute frame difference (aFD) of maximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms) for mitral valve opening when performing a ten-fold cross-validation with test splits on triplane data from 240 patients. On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1.8 (30 ms). The proposed approach has the potential to significantly impact clinical practice by enabling more accurate, rapid and comprehensive event detection, leading to improved clinical measurements.

Two methods for modifed Doo-Sabin modeling of nonsmooth surfaces-applied to right ventricle modeling.

Purpose: In recent years, there has been increased clinical interest in the right ventricle (RV) of the heart. RV dysfunction is an important prognostic marker for several cardiac diseases. Accurate modeling of the RV shape is important for estimating the performance. We have created computationally effective models that allow for accurate estimation of the RV shape. Approach: Previous approaches to cardiac shape modeling, including modeling the RV geometry, has used Doo-Sabin surfaces. Doo-Sabin surfaces allow effective computation and adapt to smooth, organic surfaces. However, they struggle with modeling sharp corners or ridges without many control nodes. We modified the Doo-Sabin surface to allow for sharpness using weighting of vertices and edges instead. This was done in two different ways. For validation, we compared the standard Doo-Sabin versus the sharp Doo-Sabin models in modeling the RV shape of 16 cardiac ultrasound images, against a ground truth manually drawn by a cardiologist. A Kalman filter fitted the models to the ultrasound images, and the difference between the volume of the model and the ground truth was measured. Results: The two modified Doo-Sabin models both outperformed the standard Doo-Sabin model in modeling the RV. On average, the regular Doo-Sabin had an 8-ml error in volume, whereas the sharp models had 7- and 6-ml error, respectively. Conclusions: Compared with the standard Doo-Sabin, the modified Doo-Sabin models can adapt to a larger variety of surfaces while still being compact models. They were more accurate on modeling the RV shape and could have uses elsewhere.

Real-Time Nonlocal Means-Based Despeckling.

In this paper, we propose a multiscale nonlocal means-based despeckling method for medical ultrasound. The multiscale approach leads to large computational savings and improves despeckling results over single-scale iterative approaches. We present two variants of the method. The first, denoted multiscale nonlocal means (MNLM), yields uniform robust filtering of speckle both in structured and homogeneous regions. The second, denoted unnormalized MNLM (UMNLM), is more conservative in regions of structure assuring minimal disruption of salient image details. Due to the popularity of anisotropic diffusion-based methods in the despeckling literature, we review the connection between anisotropic diffusion and iterative variants of NLM. These iterative variants in turn relate to our multiscale variant. As part of our evaluation, we conduct a simulation study making use of ground truth phantoms generated from clinical B-mode ultrasound images. We evaluate our method against a set of popular methods from the despeckling literature on both fine and coarse speckle noise. In terms of computational efficiency, our method outperforms the other considered methods. Quantitatively on simulations and on a tissue-mimicking phantom, our method is found to be competitive with the state-of-the-art. On clinical B-mode images, our method is found to effectively smooth speckle while preserving low-contrast and highly localized salient image detail.

Real-time scan assistant for echocardiography.

A real-time scan assistant (SA) for use with echocardiography is presented. The motivation is to aid nonexpert users in capturing apical 4-chamber views (A4CH) during echocardiography. The algorithm is based on a parametric multi-chamber model of the A4CH view, updated in an extended Kalman filter framework. The regional model goodness-of-fit is used to calculate a score, which is provided to the user during acquisition, together with an icon (emoticon) indicating whether the current view is acceptable or not. The SA was implemented on a commercially available scanner. A feasibility test was performed using two healthy volunteers as models and 10 medical students acting as nonexpert users. The students examined the models on two occasions, separated more than four days in time. Half of the students used the SA during the first exam and no SA at the second exam. The other half used the opposite order. The recordings were later rated by a cardiologist. A Wilcoxon signed pair rank test revealed a statistically significant improvement when using SA. Nine cases were rated as poor without using the SA. In eight (89%) of these cases, view quality improved to acceptable when the SA was used.

Assessment of left ventricular function by GPs using pocket-sized ultrasound.

BACKGROUND: Assessment of left ventricular (LV) function with echocardiography is mandatory in patients with suspected heart failure (HF). OBJECTIVES: To investigate if GPs were able to evaluate the LV function in patients at risk of developing or with established HF by using pocket-sized ultrasound (pUS). METHODS: Feasibility study in general practice, seven GPs in three different Norwegian primary care centres participated. Ninety-two patients with reduced or at risk of developing reduced LV function were examined by their own GP using pUS. The scan (<5 minute) was done as part of a routine appointment. A cardiologist examined the patients <30 minutes afterwards with both a laptop scanner and pUS. Measurements of the septal mitral annular excursion (sMAE) were compared. RESULTS: In 87% of the patients, the GPs were able to obtain a standard view and measure the sMAE. There was a non-significant mean difference in sMAE between GP pUS and cardiologist laptop scanner of -0.15 mm 95% confidence interval (-0.60 to 0.30) mm. A comparison of the pUS recordings and measurements of sMAE made by GP versus cardiologist revealed a non-significant mean difference with acceptable 95% limits of agreement (-0.26 ± 3.02 mm). CONCLUSIONS: With tailored training, GPs were able to assess LV function with sMAE and pUS. pUS, as a supplement to the physical examination, may become an important tool in general practice.

Automated septum thickness measurement--a Kalman filter approach.

Interventricular septum thickness in end-diastole (IVSd) is one of the key parameters in cardiology. This paper presents a fast algorithm, suitable for pocket-sized ultrasound devices, for measurement of IVSd using 2D B-mode parasternal long axis images. The algorithm is based on a deformable model of the septum and the mitral valve. The model shape is estimated using an extended Kalman filter. A feasibility study using 32 unselected recordings is presented. The recordings originate from a database consisting of subjects from a normal healthy population. Five patients with suspected hypertrophy were included in the study. Reference B-mode measurements were made by two cardiologists. A paired t-test revealed a non-significant mean difference, compared to the B-mode reference, of (mean±SD) 0.14±1.36 mm (p=0.532). Pearson's correlation coefficient was 0.79 (p<0.001). The results are comparable to the variability between the two cardiologists, which was found to be 1.29±1.23 mm (p<0.001). The results indicate that the method has potential as a tool for rapid assessment of IVSd.

Fast automatic measurement of mitral annulus excursion using a pocket-sized ultrasound system.

We present a fast, automatic method for mitral annulus excursion measurement using pocket-sized ultrasound (PSU). The motivation is to provide PSU users with a rapid measurement of cardiac systolic function. The algorithm combines low-frame-rate tolerance, computational efficiency and automation in a novel way. The method uses a speckle-tracking scheme, initialized and constrained by a deformable model. A feasibility study using 30 apical four-chamber PSU recordings from an unselected patient population revealed an error of (mean ± SD) -1.80 ± 1.96 mm, p < 0.001, when compared with manual anatomic m-mode analysis using laptop scanner data. When only septal side excursion was measured, the mean error was -0.27 ± 1.89 mm, p < 0.001. The accuracy is comparable with previously reported results using semiautomatic methods and full-size scanners. The computation time of 3.7 ms/frame on a laptop computer suggests that a real-time implementation on a PSU device is feasible.

Echocardiography without electrocardiogram.

AIMS: automatic detection of the QRS complex on electrocardiogram (ECG) is used on cardiac ultrasound scanners to separate ultrasound image series into cardiac cycles for playback and storage. On small hand-held scanners it is unpractical to connect ECG cables. We therefore aim to do automatic cardiac cycle separation using apical B-mode ultrasound images. METHODS AND RESULTS: cardiac cycle length is estimated by cyclicity analysis of B-mode intensities. To determine a cycle start estimate near QRS, a deformable model is fitted to the left ventricle in real-time. The model is used to initialize and constrain a speckle tracker positioned near the mitral annulus. In the displacement curve generated by the speckle tracker, a time point near maximum distance from the probe is detected as a cardiac cycle start estimate. Validation against ECG was done on 233 recordings from normal subjects and 46 recordings from subjects with coronary pathology. Several test cases were run for each recording to emulate B-mode series starting at all time points in the cardiac cycle. Totally, 11 886 test cases were run. Cycle length estimation was feasible in 98% of normal subject cases and 91% of pathology cases. Median difference in cycle length by ECG was 0 and -3 ms, respectively. Cycle start estimation was feasible in 90% of normal subject cases and 77% of pathology cases. Median difference to cycle start by ECG was 62 and 76 ms, respectively. CONCLUSION: apical B-mode series can automatically be separated into cardiac cycles without using ECG.