Device innovation in cardiovascular medicine: a report from the European Society of Cardiology Cardiovascular Round Table

Research performed in Europe has driven cardiovascular device innovation. This includes, but is not limited to, percutaneous coronary intervention, cardiac imaging, transcatheter heart valve implantation, and device therapy of cardiac arrhythmias and heart failure. An important part of future medical progress involves the evolution of medical technology and the ongoing development of artificial intelligence and machine learning. There is a need to foster an environment conducive to medical technology development and validation so that Europe can continue to play a major role in device innovation while providing high standards of safety. This paper summarizes viewpoints on the topic of device innovation in cardiovascular medicine at the European Society of Cardiology Cardiovascular Round Table, a strategic forum for high-level dialogue to discuss issues related to the future of cardiovascular health in Europe. Devices are developed and improved through an iterative process throughout their lifecycle. Early feasibility studies demonstrate proof of concept and help to optimize the design of a device. If successful, this should ideally be followed by randomized clinical trials comparing novel devices vs. accepted standards of care when available and the collection of post-market real-world evidence through registries. Unfortunately, standardized procedures for feasibility studies across various device categories have not yet been implemented in Europe. Cardiovascular imaging can be used to diagnose and characterize patients for interventions to improve procedural results and to monitor devices long term after implantation. Randomized clinical trials often use cardiac imaging-based inclusion criteria, while less frequently trials randomize patients to compare the diagnostic or prognostic value of different modalities. Applications using machine learning are increasingly important, but specific regulatory standards and pathways remain in development in both Europe and the USA. Standards are also needed for smart devices and digital technologies that support device-driven biomonitoring. Changes in device regulation introduced by the European Union aim to improve clinical evidence, transparency, and safety, but they may impact the speed of innovation, access, and availability. Device development programmes including dialogue on unmet needs and advice on study designs must be driven by a community of physicians, trialists, patients, regulators, payers, and industry to ensure that patients have access to innovative care.

Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography

BACKGROUND

Measurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can perform automatic LVOTd measurements on par with expert cardiologists.

METHODS

Data consisted of 649 consecutive transthoracic echocardiographic examinations of patients with coronary artery disease admitted to a university hospital. 1304 LVOTd measurements in the parasternal long axis (PLAX) and zoomed parasternal long axis views (ZPLAX) were collected, with each patient having 1-6 measurements per examination. Data quality control was performed by an expert cardiologist, and spatial geometry data was preserved for each LVOTd measurement to convert DL predictions into metric units. A convolutional neural network based on the U-Net was used as the DL model.

RESULTS

The mean absolute LVOTd error was 1.04 (95% confidence interval [CI] 0.90-1.19) mm for DL predictions on the test set. The mean relative LVOTd errors across all data subgroups ranged from 3.8 to 5.1% for the test set. Generally, the DL model had superior performance on the ZPLAX view compared to the PLAX view. DL model precision for patients with repeated LVOTd measurements had a mean coefficient of variation of 2.2 (95% CI 1.6-2.7) %, which was comparable to the clinicians for the test set.

CONCLUSION

DL for automatic LVOTd measurements in PLAX and ZPLAX views is feasible when trained on a limited clinical dataset. While the DL predicted LVOTd measurements were within the expected range of clinical inter-observer variability, the robustness of the DL model requires validation on independent datasets. Future experiments using temporal information and anatomical constraints could improve valvular identification and reduce outliers, which are challenges that must be addressed before clinical utilization.

A deep learning approach for automatic echocardiographic right ventricular strain measurements using a limited dataset

Background

Speckle tracking echocardiography provides quantification of myocardial deformation and is useful in the assessment of myocardial function. Right ventricular (RV) strain has been suggested as a sensitive tool for diagnosing cardiomyopathies and assessing long term patient outcomes for patients with pulmonary hypertension, severe tricuspid regurgitation and COVID-19 infection. Recent advances in deep learning (DL) have made promising advances in automating the labour-intensive delineation of regions of interest (ROIs). However, compared to echocardiograms with left ventricular (LV) strain, RV strain data is scarce, making DL models difficult to train.

Purpose

To investigate whether annotated LV strain data could be beneficial in training a DL model for automatic RV strain when using a limited RV dataset.

Methods

The dataset consisted of anonymized still frames from 141 echocardiograms of the RV in the RV-focused 4 chamber view with corresponding cardiologist-defined ROI. Exams included healthy subjects and patients with heart failure, valvular disease, and conduction abnormalities. ROIs and still images were extracted at the mid-systole, and then quality assessed by an experienced cardiologist as high, medium, or low. The dataset was randomly split into 68%/17%/15% sets for training, validation, and testing. A convolutional neural network for image segmentation (U-Net) with a residual neural network (ResNet50) encoder was used, with a combination of binary cross entropy and Dice loss functions. Augmentation, predefined ImageNet weights and pre-training were also employed. For pre-training, 715 still images in the apical 4 chamber view with LV defined ROIs were used, both in their original and horizontally flipped view. Predicted ROIs were reintroduced into commercially available echocardiogram analysis software to automatically calculate longitudinal strain (LS) values.

Results

The model pre-trained with the flipped LV images achieved the highest performance with a mean absolute difference of 1.26 percentage points (95% confidence interval (CI): 0.62–1.89 percentage points) between manually measured and DL-assisted LS. Median absolute LS difference was 0.85 (95% CI: 0.28–1.57) percentage points. A Bland-Altman plot revealed two outliers and no obvious trends. In comparison, the mean and median absolute LS differences for the model without pre-training were 1.87 (95% CI: 0.73–3.00) and 1.09 (95% CI: 0.56–1.63) percentage points, respectively.

Conclusions

The current study demonstrates that DL-assisted, automated RV strain measurement is feasible even with a small dataset, and that performance can be increased by using images annotated for LV strain. While the majority of the predicted RV strain results were within the typical range of intra- and interobserver variability, a few outliers were observed. These outliers could possibly be avoided with the use of larger datasets.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research council of Norway

Automatic alignment of standard views for transesophageal echocardiographic images

Purpose

3D transesophageal echocardiography (TEE) has become an important modality for pre- and peri-operative imaging of valvular heart disease. TEE can give excellent visualization of valve morphology in 3D rendering. As a convention, 3D TEE images are reformatted in three standard views. We describe a method for automatic calculation of parameters needed to define the standard views from 3D TEE images using no manual input.

Approach

An algorithm was designed to find the center of the mitral valve and the left ventricular outflow tract (OT). These parameters defined the three-chamber view. The problem was modeled as a state estimation problem in which a 3D model was deformed based on shape priors and edge detection using a Kalman filter. This algorithm is capable of running in real time after initialization.

Results

The algorithm was validated by comparing the automatic alignments of 106 TEE images against manually placed landmarks. The median error for determining the mitral valve center was 7.1 mm, and the median error for determining the left ventricular OT orientation was 13.5 deg.

Conclusion

The algorithm is an accurate tool for automating the process of finding standard views for TEE images of the mitral valve.

Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology: Cardiovascular Round Table

Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography

Background

Left ventricular outflow tract diameter (LVOTd) is routinely measured to calculate stroke volume and estimate aortic valve area by the continuity equation. Despite LVOTd being regularly measured clinically, significant inter- and intraobserver variability is evident. This variability is highly impactful on both aortic stenosis evaluation and cardiac output calculation due to the squaring of the LVOT radius.

Purpose

We aimed to investigate if LVOTd measurements from clinical echocardiographic examinations could be used in a deep learning (DL) model to automatically perform LVOTd measurements with equivalent accuracy and improved consistency compared to current practice.

Methods

Data was collected from clinical echocardiographic examinations performed on 656 consecutive patients admitted to the cardiac catheterization laboratory at a university hospital in January – December 2018. Parasternal views with cardiologist annotated LVOTd coordinates were assessed for 1314 echocardiographic still images. The quality of the still image and annotated LVOT ground truth were individually graded as high, medium and low by experienced cardiologists to establish a rigorous training basis. Spatial geometry data was preserved for each still image in order to distinguish between different degrees of image zoom. Data was randomly split into training, validation and testing sets (68%, 17%, 15%). A fully convolutional network based on the Resnet50 architecture was used with a custom loss function with heatmap regression. Image augmentations were added to extend the dataset.

Results

When including echocardiographic images of any quality (n=1314) in the model training and inference, the median absolute difference between cardiologist LVOTd and DL LVOTd was 0.97 mm (95% Confidence interval (CI) 0.79–1.14). Using only high and medium quality still images and ground truth (n=869) in the training and inference, median absolute difference decreased to 0.81 mm (95% CI 0.60–0.96). Adding image augmentations to this dataset further improved the model, resulting in a median LVOTd absolute difference of 0.66 mm (95% CI 0.51–0.78). The LVOTd error in inference increased with decreasing image quality, as shown in Figure 1, with two predictions (0.9%) failing completely.

Conclusion

Deep learning models are capable of measuring LVOTd with comparable accuracy to cardiologists when trained on clinical data. Data quality affects both training and inference. Even with a slightly lower accuracy when used on lower quality echocardiographic images, DL-assisted LVOT measurement has a clear potential to increase repeatability and consistency of LVOTd measurements.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Research Council of Norway (Norges forskningsråd) Figure 1

Generating Synthetic Labeled Data From Existing Anatomical Models: An Example With Echocardiography Segmentation

Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and are transformed into realistic synthetic ultrasound images with paired labels using a CycleGAN. We demonstrate the pipeline by generating synthetic 2D echocardiography images to compare with existing deep learning ultrasound segmentation datasets. A convolutional neural network is trained to segment the left ventricle and left atrium using only synthetic images. Networks trained with synthetic images were extensively tested on four different unseen datasets of real images with median Dice scores of 91, 90, 88, and 87 for left ventricle segmentation. These results match or are better than inter-observer results measured on real ultrasound datasets and are comparable to a network trained on a separate set of real images. Results demonstrate the images produced can effectively be used in place of real data for training. The proposed pipeline opens the door for automatic generation of training data for many tasks in medical imaging as the same process can be applied to other segmentation or landmark detection tasks in any modality. The source code and anatomical models are available to other researchers.1 1https://adgilbert.github.io/data-generation/.

User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing

Spectral Doppler measurements are an important part of the standard echocardiographic examination. These measurements give insight into myocardial motion and blood flow, providing clinicians with parameters for diagnostic decision making. Many of these measurements are performed automatically with high accuracy, increasing the efficiency of the diagnostic pipeline. However, full automation is not yet available because the user must manually select which measurement should be performed on each image. In this work, we develop a pipeline based on convolutional neural networks (CNNs) to automatically classify the measurement type from cardiac Doppler scans. We show how the multi-modal information in each spectral Doppler recording can be combined using a meta parameter post-processing mapping scheme and heatmaps to encode coordinate locations. Additionally, we experiment with several architectures to examine the tradeoff between accuracy, speed, and memory usage for resource-constrained environments. Finally, we propose a confidence metric using the values in the last fully connected layer of the network and show that our confidence metric can prevent many misclassifications. Our algorithm enables a fully automatic pipeline from acquisition to Doppler spectrum measurements. We achieve 96% accuracy on a test set drawn from separate clinical sites, indicating that the proposed method is suitable for clinical adoption.

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.

Detection of Tissue Fibrosis using Natural Mechanical Wave Velocity Estimation: Feasibility Study

In the feasibility study described here, we developed and tested a novel method for mechanical wave velocity estimation for tissue fibrosis detection in the myocardium. High-frame-rate ultrasound imaging and a novel signal processing method called clutter filter wave imaging was used. A mechanical wave propagating through the left ventricle shortly after the atrial contraction was measured in the three different apical acquisition planes, for 20 infarct patients and 10 healthy controls. The results obtained were correlated with fibrosis locations from magnetic resonance imaging, and a sensitivity ≥60% was achieved for all infarcts larger than 10% of the left ventricle. The stability of the wave through several heart cycles was assessed and found to be of high quality. This method therefore has potential for non-invasive fibrosis detection in the myocardium, but further validation in a larger group of subjects is needed.

Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography

3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation, and test sets. Each 3D recording was decomposed into a set of 2D planes, exploiting the symmetry around the centerline of the left ventricle. A deep 2D convolutional neural network was trained to predict the mitral annulus coordinates, and the predictions from neighboring planes were regularized by enforcing continuity around the annulus. Applying the final model and post-processing to the test set data gave a mean error of 2.0 mm - with a standard deviation of 1.9 mm. Fully automatic segmentation of the mitral annulus can alleviate the need for manual interaction in the quantification of an array of mitral annular parameters and has the potential to eliminate inter-observer variability.

Detection of Regional Mechanical Activation of the Left Ventricular Myocardium Using High Frame Rate Ultrasound Imaging

We have investigated the feasibility of noninvasive mapping of mechanical activation patterns in the left ventricular (LV) myocardium using high frame rate ultrasound imaging for the purpose of detecting conduction abnormalities. Five anesthetized, open-chest dogs with implanted combined sonomicrometry and electromyography (EMG) crystals were studied. The animals were paced from the specified locations of the heart, while crystal and ultrasound data were acquired. Isochrone maps of the mechanical activation patterns were generated from the ultrasound data using a novel signal processing method called clutter filter wave imaging (CFWI). The isochrone maps showed the same mechanical activation pattern as the sonomicrometry crystals in 90% of the cases. For electrical activation, the activation sequences from ultrasound were the same in 92% of the cases. The coefficient of determination between the activation delay measured with EMG and ultrasound was R ² = 0.79 , indicating a strong correlation. These results indicate that high frame rate ultrasound imaging processed with CFWI has the potential to be a valuable tool for mechanical activation detection.

An image fusion tool for echo-guided left ventricular lead placement in cardiac resynchronization therapy: Performance and workflow integration analysis

BACKGROUND

The response rate to cardiac resynchronization therapy (CRT) may be improved if echocardiographic-derived parameters are used to guide the left ventricular (LV) lead deployment. Tools to visually integrate deformation imaging and fluoroscopy to take advantage of the combined information are lacking.

METHODS

An image fusion tool for echo-guided LV lead placement in CRT was developed. A personalized average 3D cardiac model aided visualization of patient-specific LV function in fluoroscopy. A set of coronary venography-derived landmarks facilitated registration of the 3D model with fluoroscopy into a single multimodality image. The fusion was both performed and analyzed retrospectively in 30 cases. Baseline time-to-peak values from echocardiography speckle-tracking radial strain traces were color-coded onto the fused LV. LV segments with suspected scar tissue were excluded by cardiac magnetic resonance imaging. The postoperative augmented image was used to investigate: (a) registration accuracy and (b) agreement between LV pacing lead location, echo-defined target segments, and CRT response.

RESULTS

Registration time (264 ± 25 seconds) and accuracy (4.3 ± 2.3 mm) were found clinically acceptable. A good agreement between pacing location and echo-suggested segments was found in 20 (out of 21) CRT responders. Perioperative integration of the proposed workflow was successfully tested in 2 patients. No additional radiation, compared with the existing workflow, was required.

CONCLUSIONS

The fusion tool facilitates understanding of the spatial relationship between the coronary veins and the LV function and may help targeted LV lead delivery.

Estimation of myocardial work from pressure-strain loops analysis: an experimental evaluation

Purpose

The area of left ventricular (LV) pressure-strain loop (PSL) is used as an index of regional myocardial work. The purpose of the present work is to compare the main segmental PSL markers and the derived global work indices, when they are calculated using an estimated pressure signal or an observed pressure signal.

Methods and results

In nine patients implanted with a bi-ventricular pace-maker (CRT), LV pressure was invasively measured in five conditions: CRT-off, LV-pacing, right ventricular-pacing and two different CRT-pacing. For each condition, systolic blood pressure was measured by brachial artery cuff-pressure and transthoracic echocardiography loops were recorded simultaneously. The error and relative root mean square error (rRMSE) between measured and estimated pressure were calculated for each patient and each configuration. Correlation coefficient (R2) and Bland-Altman (BA) analysis were performed for PSL area and work indices. A total of 43 different haemodynamic conditions were compared (774 segmental PSL). The global rRMSE between estimated and measured LV-pressure was 12.3 mmHg. The estimated and measured segmental LV-PSL were strongly correlated, with an R2 of 0.98. BA analysis shows that the mean bias for the estimation of segmental LV-PSL area is 86.0 mmHg.%. A significant bias effect with linearly increasing error with pressure values is observed. R2 ≥ 0.88 and a mean bias in BA analysis ≤41.4 mmHg.% was observed for the estimation of global myocardial work indices.

Conclusion

The non-invasive estimation for LV pressure-strain loop area and the global myocardial work indices obtained from LV-PSL strongly correlates with invasive measurements.

Adaptive Color Gain for Vena Contracta Quantification in Valvular Regurgitation

Severe valvular regurgitation can lead to pulmonary hypertension, atrial fibrillation and heart failure. Vena contracta width is used to estimate the severity of the regurgitation. Parameters affecting visualization of color Doppler have a significant impact on the measurement. We propose a data-driven method for automated adjustment of color gain based on the peak power of the color Doppler signal in the vicinity of the vena contracta. A linear regression model trained on the peak power was used to predict the orifice diameter. According to our study, the color gain should be set to about 6 dB above where color Doppler data completely disappears from the image. Based on our method, orifices with reference diameters of 4, 6.5 and 8.5 mm were estimated with relative diameter errors within 18%, 12% and 14%, respectively.

Method comparison for cardiac image registration of coronary computed tomography angiography and 3-D echocardiography

Treatment decision for coronary artery disease (CAD) is based on both morphological and functional information. Image fusion of coronary computed tomography angiography (CCTA) and three-dimensional echocardiography (3DE) could combine morphology and function into a single image to facilitate diagnosis. Three semiautomatic feature-based methods for CCTA/3DE registration were implemented and applied on CAD patients. Methods were verified and compared using landmarks manually identified by a cardiologist. All methods were found feasible for CCTA/3DE fusion.

Temperature monitoring by channel data delays: Feasibility based on estimated delays magnitude for cardiac ablation

Ultrasound thermometry is based on measuring tissue temperature by its impact on ultrasound wave propagation. This study focuses on the use of transducer array channel data (not beamformed) and examines how a layer of increased velocity (heat induced) affects the travel-times of the ultrasound backscatter signal. Based on geometric considerations, a new equation was derived for the change in time delay as a function of temperature change. The resulting expression provides insight into the key factors that link change in temperature to change in travel time. It shows that velocity enters in combination with heating geometry: complementary information is needed to compute velocity from the changes in travel time. Using the bio-heat equation as a second source of information in the derived expressions, the feasibility of monitoring the temperature increase during cardiac ablation therapy using channel data was investigated. For an intra-cardiac (ICE) probe, using this "time delay error approach" would not be feasible, while for a trans-esophageal array transducer (TEE) transducer it might be feasible.

Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces

With the advancement of three-dimensional (3-D) real-time echocardiography in recent years, automatic creation of patient specific geometric models is becoming feasible and important in clinical decision making. However, the vast majority of echocardiographic segmentation methods presented in the literature focus on the left ventricle (LV) endocardial border, leaving segmentation of the right ventricle (RV) a largely unexplored problem, despite the increasing recognition of the RV's role in cardiovascular disease. We present a method for coupled segmentation of the endo- and epicardial borders of both the LV and RV in 3-D ultrasound images. To solve the segmentation problem, we propose an extension of a successful state-estimation segmentation framework with a geometrical representation of coupled surfaces, as well as the introduction of myocardial incompressibility to regularize the segmentation. The method was validated against manual measurements and segmentations in images of 16 patients. Mean absolute distances of [Formula: see text], [Formula: see text], and [Formula: see text] between the proposed and reference segmentations were observed for the LV endocardium, RV endocardium, and LV epicardium surfaces, respectively. The method was computationally efficient, with a computation time of [Formula: see text].

Delay and Standard Deviation Beamforming to Enhance Specular Reflections in Ultrasound Imaging

Although interventional devices, such as needles, guide wires, and catheters, are best visualized by X-ray, real-time volumetric echography could offer an attractive alternative as it avoids ionizing radiation; it provides good soft tissue contrast, and it is mobile and relatively cheap. Unfortunately, as echography is traditionally used to image soft tissue and blood flow, the appearance of interventional devices in conventional ultrasound images remains relatively poor, which is a major obstacle toward ultrasound-guided interventions. The objective of this paper was therefore to enhance the appearance of interventional devices in ultrasound images. Thereto, a modified ultrasound beamforming process using conventional-focused transmit beams is proposed that exploits the properties of received signals containing specular reflections (as arising from these devices). This new beamforming approach referred to as delay and standard deviation beamforming (DASD) was quantitatively tested using simulated as well as experimental data using a linear array transducer. Furthermore, the influence of different imaging settings (i.e., transmit focus, imaging depth, and scan angle) on the obtained image contrast was evaluated. The study showed that the image contrast of specular regions improved by 5-30 dB using DASD beamforming compared with traditional delay and sum (DAS) beamforming. The highest gain in contrast was observed when the interventional device was tilted away from being orthogonal to the transmit beam, which is a major limitation in standard DAS imaging. As such, the proposed beamforming methodology can offer an improved visualization of interventional devices in the ultrasound image with potential implications for ultrasound-guided interventions.

Diverging Wave Volumetric Imaging Using Subaperture Beamforming

Several clinical settings could benefit from 3-D high frame rate (HFR) imaging and, in particular, HFR 3-D tissue Doppler imaging (TDI). To date, the proposed methodologies are based mostly on experimental ultrasound platforms, making their translation to clinical systems nontrivial as these have additional hardware constraints. In particular, clinically used 2-D matrix array transducers rely on subaperture (SAP) beamforming to limit cabling between the ultrasound probe and the back-end console. Therefore, this paper is aimed at assessing the feasibility of HFR 3-D TDI using diverging waves (DWs) on a clinical transducer with SAP beamforming limitations. Simulation studies showed that the combination of a single DW transmission with SAP beamforming results in severe imaging artifacts due to grating lobes and reduced penetration. Interestingly, a promising tradeoff between image quality and frame rate was achieved for scan sequences with a moderate number of transmit beams. In particular, a sparse sequence with nine transmissions showed good imaging performance for an imaging sector of 70 ^°×70 ^° at volume rates of approximately 600 Hz. Subsequently, this sequence was implemented in a clinical system and TDI was recorded in vivo on healthy subjects. Velocity curves were extracted and compared against conventional TDI (i.e., with focused transmit beams). The results showed similar velocities between both beamforming approaches, with a cross-correlation of 0.90 ± 0.11 between the traces of each mode. Overall, this paper indicates that HFR 3-D TDI is feasible in systems with clinical 2-D matrix arrays, despite the limitations of SAP beamforming.

Spatiotemporal registration of multiple three-dimensional echocardiographic recordings for enhanced field of view imaging

The use of three-dimensional (3-D) echocardiography is limited by signal dropouts and narrow field of view. Data compounding is proposed as a solution to overcome these limitations by combining multiple 3-D recordings to form a wide field of view. The first step of the solution requires registration between the recordings both in the spatial and temporal dimension for dynamic organs such as the heart. Accurate registration between the individual echo recordings is crucial for the quality of compounded volumes. A temporal registration method based on a piecewise one-dimensional cubic B-spline in combination with multiscale iterative Farnebäck optic flow method for spatial registration was described. The temporal registration method was validated on in vivo data sets with annotated timing of mitral valve opening. The spatial registration method was validated using in vivo data and compared to registration with Procrustes analysis using manual contouring as a benchmark. The spatial accuracy was assessed in terms of mean of absolute distance and Hausdorff distance between the left ventricular contours. The results showed that the temporal registration accuracy is in the range of half the time resolution of the echo recordings and the achieved spatial accuracy of the proposed method is comparable to manual registration.

Liver Tumor Cryoablation: A Commentary on the Need of Improved Procedural Monitoring

Cryoablation is a method used for in situ destruction of liver tumors not eligible for surgical resection. Local recurrences following such treatment have been reported at rates of 5–44%. Insufficient procedural monitoring of the ablation is one plausible explanation for these recurrences. The cryoablative procedure is usually monitored by ultrasonography, but acoustic shadowing and loss of signals, compromise visualisation of the cryolesion circumference. Other monitoring modalities such as computer tomography and invasive methods like the use of thermocouples and impedance measurements have also been studied, but are not in common clinical use as single monitoring modalities. Thermodynamic conditions assumed adequate for tumor eradication are likely to occur only in parts of the cryolesion. This tumoricidal part of the cryolesion is not adequately depicted using any of these modalities.

Magnetic resonance imaging (MRI) provides a clear delineation of the cryolesion circumference. Noninvasive temperature measurements assisted by MRI indicate which parts of the cryolesion that may be subject to complete necrosis. In this article MRI monitored cryoablation of liver tumors is discussed. Improved peroperative monitoring as offered by MRI may reduce the rates of local recurrences after treatment, but further technological improvements are required.

Robust Spatio-Temporal Registration of 4D Cardiac Ultrasound Sequences

Registration of multiple 3D ultrasound sectors in order to provide an extended field of view is important for the appreciation of larger anatomical structures at high spatial and temporal resolution. In this paper, we present a method for fully automatic spatio-temporal registration between two partially overlapping 3D ultrasound sequences. The temporal alignment is solved by aligning the normalized cross correlation-over-time curves of the sequences. For the spatial alignment, corresponding 3D Scale Invariant Feature Transform (SIFT) features are extracted from all frames of both sequences independently of the temporal alignment. A rigid transform is then calculated by least squares minimization in combination with random sample consensus. The method is applied to 16 echocardiographic sequences of the left and right ventricles and evaluated against manually annotated temporal events and spatial anatomical landmarks. The mean distances between manually identified landmarks in the left and right ventricles after automatic registration were (mean ± SD) 4.3 ± 1.2 mm compared to a reference error of 2.8 ± 0.6 mm with manual registration. For the temporal alignment, the absolute errors in valvular event times were 14.4 ± 11.6 ms for Aortic Valve (AV) opening, 18.6 ± 16.0 ms for AV closing, and 34.6 ± 26.4 ms for mitral valve opening, compared to a mean inter-frame time of 29 ms.

Left-Atrial Segmentation From 3-D Ultrasound Using B-Spline Explicit Active Surfaces With Scale Uncoupling

Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from three-dimensional (3-D) ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, we propose an extension of the BEAS framework, introducing B-splines with uncoupled scaling. This formulation improves the shape support for less regular and more variable structures, by giving independent control over smoothness and number of control points. Semiautomatic segmentation of the LA endocardium using this framework was tested in a setup requiring little user input, on 20 volumetric sequences of echocardiographic data from healthy subjects. The segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semiautomatic LA segmentation in 3-D transthoracic US, providing reliable quantification of the LA morphology and function.

Automated Segmentation of the Right Ventricle in 3D Echocardiography: A Kalman Filter State Estimation Approach

As the right ventricle's (RV) role in cardiovascular diseases is being more widely recognized, interest in RV imaging, function and quantification is growing. However, there are currently few RV quantification methods for 3D echocardiography presented in the literature or commercially available. In this paper we propose an automated RV segmentation method for 3D echocardiographic images. We represent the RV geometry by a Doo-Sabin subdivision surface with deformation modes derived from a training set of manual segmentations. The segmentation is then represented as a state estimation problem and solved with an extended Kalman filter by combining the RV geometry with a motion model and edge detection. Validation was performed by comparing surface-surface distances, volumes and ejection fractions in 17 patients with aortic insufficiency between the proposed method, magnetic resonance imaging (MRI), and a manual echocardiographic reference. The algorithm was efficient with a mean computation time of 2.0 s. The mean absolute distances between the proposed and manual segmentations were 3.6 ± 0.7 mm. Good agreements of end diastolic volume, end systolic volume and ejection fraction with respect to MRI ( -26±24 mL , -16±26 mL and 0 ± 10%, respectively) and a manual echocardiographic reference (7 ± 30 mL, 13 ± 17 mL and -5±7% , respectively) were observed.

Parametric ultrasound and fluoroscopy image fusion for guidance of left ventricle lead placement in cardiac resynchronization therapy

Recent studies show that the response rate to cardiac resynchronization therapy (CRT) could be improved if the left ventricle (LV) is paced at the site of the latest mechanical activation, but away from the myocardial scar. A prototype system for CRT lead placement guidance that combines LV functional information from ultrasound with live x-ray fluoroscopy was developed. Two mean anatomical models, each containing LV epi-, LV endo- and right ventricle endocardial surfaces, were computed from a database of 33 heart failure patients as a substitute for a patient-specific model. The sphericity index was used to divide the observed population into two groups. The distance between the mean and the patient-specific models was determined using a signed distance field metric (reported in mm). The average error values for LV epicardium were [Formula: see text] and for LV endocardium were [Formula: see text]. The validity of using average LV models for a CRT procedure was tested by simulating coronary vein selection in a group of 15 CRT candidates. The probability of selecting the same coronary branch, when basing the selection on the average model compared to a patient-specific model, was estimated to be [Formula: see text]. This was found to be clinically acceptable.

Acoustic output of multi-line transmit beamforming for fast cardiac imaging: a simulation study

Achieving higher frame rates in cardiac ultrasound could unveil short-lived myocardial events and lead to new insights on cardiac function. Multi-line transmit (MLT) beamforming (i.e., simultaneously transmitting multiple focused beams) is a potential approach to achieve this. However, two challenges come with it: first, it leads to cross-talk between the MLT beams, appearing as imaging artifacts, and second, it presents acoustic summation in the near field, where multiple MLT beams overlap. Although several studies have focused on the former, no studies have looked into the implications of the latter on acoustic safety. In this paper, the acoustic field of 4-MLT was simulated and compared with single-line transmit (SLT). The findings suggest that standard MLT does present potential concerns. Compared with SLT, it shows a 2-fold increase in mechanical index (MI) (from 1.0 to 2.3), a 6-fold increase in spatial-peak pulse-average intensity (I(sppa)) (from 99 to 576 W∙cm(-2)) and a 12-fold increase in spatial-peak temporalaverage intensity (I(spta)) (from 119 to 1407 mW∙cm(-2)). Subsequently, modifications of the transmit pulse and delay line of MLT were studied. These modifications allowed for a change in the spatio-temporal distribution of the acoustic output, thereby significantly decreasing the safety indices (MI = 1.2, I(sppa) = 92 W∙cm(-2) and I(spta) = 366 mW∙cm(-2)). Accordingly, they help mitigate the concerns around MLT, reducing potential tradeoffs between acoustic safety and image quality.

Automatic measurement of aortic annulus diameter in 3-dimensional transoesophageal echocardiography

BACKGROUND

Transcatheter aortic valve implantation involves percutaneously implanting a biomechanical aortic valve to treat severe aortic stenosis. In order to select a proper device, precise sizing of the aortic valve annulus must be completed.

METHODS

In this paper, we describe a fully automatic segmentation method to measure the aortic annulus diameter in patients with aortic calcification, operating on 3-dimensional transesophageal echocardiographic images. The method is based on state estimation of a subdivision surface representation of the left ventricular outflow tract and aortic root. The state estimation is solved by an extended Kalman filter driven by edge detections normal to the subdivision surface.

RESULTS

The method was validated on echocardiographic recordings of 16 patients. Comparison against two manual measurements showed agreements (mean ±SD) of -0.3 ± 1.6 and -0.2 ± 2.3 mm for perimeter-derived diameters, compared to an interobserver agreement of -0.1 ± 2.1 mm.

CONCLUSIONS

With this study, we demonstrated the feasibility of an efficient and fully automatic measurement of the aortic annulus in patients with aortic disease. The algorithm robustly measured the aortic annulus diameter, providing measurements indistinguishable from those done by cardiologists.

A non-parametric permutation method for assessing agreement for distance matrix observations

Distance matrix data are occurring ever more frequently in medical research, particularly in fields such as genetics, DNA research, and image analysis. We propose a non-parametric permutation method for assessing agreement when the data under study are distance matrices. We apply agglomerative hierarchical clustering and accompanying dendrograms to visualize the internal structure of the matrix observations. The accompanying test is based on random permutations of the elements within individual matrix observations and the corresponding matrix mean of these permutations. We compare the within-matrix element sum of squares (WMESS) for the observed mean against the WMESS for the permutation means. The methodology is exemplified using simulations and real data from magnetic resonance imaging.

A motion constrained cross-wire phantom for tracked 2D ultrasound calibration

PURPOSE

Ultrasound-guided 3D interventions require calibration to relate real-time 2D images with the position and orientation of the ultrasound probe. Capturing several images of a single fixed point from different viewpoints is a simple and commonly used approach, but it is cumbersome and tedious. A new phantom for calibration was designed, built and tested to simplify this process.

METHODS

A mechanical phantom that restricts the motion of the ultrasound probe was designed such that the ultrasound image always captures a designated fixed point. Software was implemented which computes calibration parameters. Although the software provides no scientific novelty, it is required to demonstrate the proof of concept and to assess the accuracy and precision of the calibration phantom. The software also illustrates how the phantom enables the fixed point to be located automatically, both in tracker device coordinates and in image pixel coordinates.

RESULTS

The phantom was used to capture several hundred images of a single fixed point in less than 1 min, with different probe positions and orientations around the fixed point and with the single fixed point located in different parts of the ultrasound image. It would not be feasible to capture the same number of images by manual alignment of the probe with the fixed point.

CONCLUSION

Images for single fixed point calibration can be captured easily and quickly with a new calibration phantom. Since a larger number of images can be used to compute the required parameters, the calibration robustness is increased.

Collaborative co-design of emerging multi-technologies for surgery

The EU Research Training Network on Augmented Reality in Surgery (ARIS*ER) was established with two aims: (1) to develop next-generation novel image guidance (augmented reality based on medical images) and cross-linked robotic systems (automatic control loops guided by information sensed from the patient) and (2) to educate young researchers in the user-centred, multidisciplinary design of emerging technologies for minimally invasive surgery (MIS) and intervention radiology. Collaborations between engineers, Human Factors specialists, industrial designers and medical end users were foreseen, but actual methodologies had to be developed. Three applications were used as development vehicles and as demonstrators. The resulting teamwork and process of identifying requirements, finding solutions (in technology and workflow), and shifting between these to optimize and speed development towards quality of care were studied. The ARIS*ER approach solves current problems in collaborative teams, taking a systems approach, and manages the overview of requirements and solutions, which is too complex to manage centrally.