Simulated Ultrasound Images Research Articles

Computed tomography-based radial endobronchial ultrasound image simulation of peripheral pulmonary lesions using deep learning.

Radial endobronchial ultrasound (R-EBUS) plays an important role during transbronchial sampling of peripheral pulmonary lesions (PPLs). However, existing navigational bronchoscopy systems provide no guidance for R-EBUS. To guide intraoperative R-EBUS probe manipulation, we aimed to simulate R-EBUS images of PPLs from preoperative computed tomography (CT) data using deep learning. Preoperative CT and intraoperative ultrasound data of PPLs in 250 patients who underwent R-EBUS-guided transbronchial lung biopsy were retrospectively collected. Two-dimensional CT sections perpendicular to the biopsy path were transformed into ultrasonic reflection and transmission images using an ultrasound propagation model to obtain the initial simulated R-EBUS images. A cycle generative adversarial network was trained to improve the realism of initial simulated images. Objective and subjective indicators were used to evaluate the similarity between real and simulated images. Wasserstein distances showed that utilizing the cycle generative adversarial network significantly improved the similarity between real and simulated R-EBUS images. There was no statistically significant difference in the long axis, short axis, and area between real and simulated lesions (all P > 0.05). Based on the experts' evaluation, a median similarity score of ≥4 on a 5-point scale was obtained for lesion size, shape, margin, internal echoes, and overall similarity. Simulated R-EBUS images of PPLs generated by our method can closely mimic the corresponding real images, demonstrating the potential of our method to provide guidance for intraoperative R-EBUS probe manipulation.

Microbubble detection on ultrasound imaging by utilizing phase patterned waves

Objective. Super-resolution ultrasonography offers the advantage of visualization of intricate microvasculature, which is crucial for disease diagnosis. Mapping of microvessels is possible by localizing microbubbles (MBs) that act as contrast agents and tracking their location. However, there are limitations such as the low detectability of MBs and the utilization of a diluted concentration of MBs, leading to the extension of the acquisition time. We aim to enhance the detectability of MBs to reduce the acquisition time of acoustic data necessary for mapping the microvessels. Approach. We propose utilizing phase patterned waves (PPWs) characterized by spatially patterned phase distributions in the incident beam to achieve this. In contrast to conventional ultrasound irradiation methods, this irradiation method alters bubble interactions, enhancing the oscillation response of MBs and generating more significant scattered waves from specific MBs. This enhances the detectability of MBs, thereby enabling the detection of MBs that were undetectable by the conventional method. The objective is to maximize the overall detection of bubbles by utilizing ultrasound imaging with additional PPWs, including the conventional method. In this paper, we apply PPWs to ultrasound imaging simulations considering bubble–bubble interactions to elucidate the characteristics of PPWs and demonstrate their efficacy by employing PPWs on MBs fixed in a phantom by the experiment. Main results. By utilizing two types of PPWs in addition to the conventional ultrasound irradiation method, we confirmed the detection of up to 93.3% more MBs compared to those detected using the conventional method alone. Significance. Ultrasound imaging using additional PPWs made it possible to increase the number of detected MBs, which is expected to improve the efficiency of bubble detection.

A Multiscale Deep Encoder–Decoder with Phase Congruency Algorithm Based on Deep Learning for Improving Diagnostic Ultrasound Image Quality

Ultrasound imaging is widely used as a noninvasive lesion detection method in diagnostic medicine. Improving the quality of these ultrasound images is very important for accurate diagnosis, and deep learning-based algorithms have gained significant attention. This study proposes a multiscale deep encoder–decoder with phase congruency (MSDEPC) algorithm based on deep learning to improve the quality of diagnostic ultrasound images. The MSDEPC algorithm included low-resolution (LR) images and edges as inputs and constructed a multiscale convolution and deconvolution network. Simulations were conducted using the Field 2 program, and data from real experimental research were obtained using five clinical datasets containing images of the carotid artery, liver hemangiomas, breast malignancy, thyroid carcinomas, and obstetric nuchal translucency. LR images, bicubic interpolation, and super-resolution convolutional neural networks (SRCNNs) were modeled as comparison groups. Through visual assessment, the image processed using the MSDEPC was the clearest, and the lesions were clearly distinguished. The structural similarity index metric (SSIM) value of the simulated ultrasound image using the MSDEPC algorithm improved by approximately 38.84% compared to LR. In addition, the peak signal-to-noise ratio (PSNR) and SSIM values of clinical ultrasound images using the MSDEPC algorithm improved by approximately 2.33 times and 88.58%, respectively, compared to LR. In conclusion, the MSDEPC algorithm is expected to significantly improve the spatial resolution of ultrasound images.

Anatomy-aware computed tomography-to-ultrasound spine registration.

Ultrasound (US) has demonstrated to be an effective guidance technique for lumbar spine injections, enabling precise needle placement without exposing the surgeon or the patient to ionizing radiation. However, noise and acoustic shadowing artifacts make US data interpretation challenging. To mitigate these problems, many authors suggested using computed tomography (CT)-to-US registration to align the spine in pre-operative CT to intra-operative US data, thus providing localization of spinallandmarks. In this paper, we propose a deep learning (DL) pipeline for CT-to-US registration and address the problem of a need for annotated medical data for network training. Firstly, we design a data generation method to generate paired CT-US data where the spine is deformed in a physically consistent manner. Secondly, we train a point cloud (PC) registration network using anatomy-aware losses to enforce anatomically consistentpredictions. Our proposed pipeline relies on training the network on realistic generated data. In our data generation method, we model the properties of the joints and disks between vertebrae based on biomechanical measurements in previous studies. We simulate the supine and prone position deformation by applying forces on the spine models. We choose the spine models from 35 patients in VerSe dataset. Each spine is deformed 10 times to create a noise-free data with ground-truth segmentation at hand. In our experiments, we use one-leave-out cross-validation strategy to measure the performance and the stability of the proposed method. For each experiment, we choose generated PCs from three spines as the test set. From the remaining, data from 3 spines act as the validation set and we use the rest of the data for training thealgorithm. To train our network, we introduce anatomy-aware losses and constraints on the movement to match the physics of the spine, namely, rigidity loss and bio-mechanical loss. We define rigidity loss based on the fact that each vertebra can only transform rigidly while the disks and the surrounding tissue are deformable. Second, by using bio-mechanical loss we stop the network from inferring extreme movements by penalizing the force needed to get to a certainpose. To validate the effectiveness of our fully automated data generation pipeline, we qualitatively assess the fidelity of the generated data. This assessment involves verifying the realism of the spinal deformation and subsequently confirming the plausibility of the simulated ultrasound images. Next, we demonstrate that the introduction of the anatomy-aware losses brings us closer to state-of-the-art (SOTA) and yields a reduction of 0.25 mm in terms of target registration error (TRE) compared to using only mean squared error (MSE) loss on the generated dataset. Furthermore, by using the proposed losses, the rigidity loss in inference decreases which shows that the inferred deformation respects the rigidity of the vertebrae and only introduces deformations in the soft tissue area to compensate the difference to the target PC. We also show that our results are close to the SOTA for the simulated US dataset with TRE of 3.89 mm and 3.63 mm for the proposed method and SOTA respectively. In addition, we show that our method is more robust against errors in the initialization in comparison to SOTA and significantly achieves better results (TRE of 4.88 mm compared to 5.66 mm) in this experiment. In conclusion, we present a pipeline for spine CT-to-US registration and explore the potential benefits of utilizing anatomy-aware losses to enhance registration results. Additionally, we propose a fully automatic method to synthesize paired CT-US data with physically consistent deformations, which offers the opportunity to generate extensive datasets for network training. The generated dataset and the source code for data generation and registration pipeline can be accessed via https://github.com/mfazampour/medphys_ct_us_registration.

Towards Estimating MRI-Ultrasound Registration Error in Image-Guided Neurosurgery.

Image-guided neurosurgery allows surgeons to view their tools in relation to pre-operatively acquired patient images and models. To continue using neuronavigation systems throughout operations, image registration between pre-operative images (typically MRI) and intra-operative images (e.g., ultrasound) are common to account for brain shift (deformations of the brain during surgery). We implemented a method to estimate MRI-ultrasound registration errors, with the goal of enabling surgeons to quantitatively assess the performance of linear or nonlinear registrations. To the best of our knowledge, this is the first dense error estimating algorithm applied to multimodal image registrations. The algorithm is based on a previously proposed sliding-window convolutional neural network that operates on a voxel-wise basis. To create training data where the true registration error is known, simulated ultrasound images were created from pre-operative MRI images and artificially deformed. The model was evaluated on artificially deformed simulated ultrasound data as well as real ultrasound data with manually annotated landmark points. The model achieved a mean absolute error of 0.977 ± 0.988 mm and correlation of 0.8 ± 0.062 on the simulated ultrasound data, and a mean absolute error of 2.24 ± 1.89 mm and a correlation of 0.246 on the real ultrasound data. We discuss concrete areas to improve the results on real ultrasound data. Our progress lays the foundation for future developments and ultimately implementation on clinical neuronavigation systems.

Anatomy preserving GAN for realistic simulation of intraoperative liver ultrasound images

In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images with important anatomical structures remains an obstacle hindering the successful application of AI to ultrasound guidance. In this case, intraoperative ultrasound (iUS) simulation should be conducted from preoperative magnetic resonance (pMR), which not only helps doctors understand the characteristics of iUS in advance, but also expands the iUS dataset from various imaging positions, thereby promoting the automatic iUS analysis in ultrasound guidance. Herein, a novel anatomy preserving generative adversarial network (ApGAN) framework was proposed to generate simulated intraoperative ultrasound (Sim-iUS) of liver with precise structure information from pMR. Specifically, the low-rank factors based bimodal fusion was first established focusing on the effective information of hepatic parenchyma. Then, a deformation field based correction module was introduced to learn and correct the slight structural distortion from surgical operations. Meanwhile, the multiple loss functions were designed to constrain the simulation of the content, structures, and style. Empirical results of clinical data showed that the proposed ApGAN obtained higher Structural Similarity (SSIM) of 0.74 and Fr´echet Inception Distance (FID) of 35.54 compared to existing methods. Furthermore, the average Hausdorff Distance (HD) error of the liver capsule structure was less than 0.25 mm, and the average relative (Euclidean Distance) ED error for polyps was 0.12 mm, indicating the high-level precision of this ApGAN in simulating the anatomical structures and focal areas.

Quantifying tongue tip visibility in ultrasound images of /r/ tongue shapes using numerical ultrasound simulations

Midsagittal ultrasound images provide important articulatory information about tongue shape by showing much of the tongue surface from the root to the tip. However, portions of the tongue tip are often obscured due to shadowing from the mandible bone and the sublingual airspace. Information about tongue tip position can be important for understanding tongue movement patterns, e.g., classification of a retroflex or bunched shape for the American English rhotic /r/. Here, numerical simulations of acoustic wave propagation in tissue were performed using the open-source k-Wave toolbox to recreate ultrasound images of the tongue for a range of retroflex and bunched tongue shapes segmented from magnetic resonance imaging (MRI). Extent of the tongue tip visibly missing from the simulated ultrasound images is quantified by comparison with the known tongue shape from MRI. The fraction of the tongue surface visible in ultrasound images is compared between tongue shapes that appear retroflex versus bunched in MRI. Implications for ultrasound biofeedback therapy are discussed, including possible ambiguity of retroflex versus bunched classification.

A simulation framework for pulse wave and vector flow imaging using fluid–structure interaction and FIELD-II simulations

Pulse Wave Imaging (PWI) utilizes ultrasound elasticity imaging and normalized cross-correlation (NCC) tracking pulse-induced arterial wall distension propagations to perform localized pulse wave velocity and assess arterial compliance mapping for the early detection of vulnerable carotid plaques. At the same time, Vector Flow Imaging (VFI) using a singular value decomposition filter and NCC with axially and laterally shifted axial kernels provides blood flow information. In this study, fluid–structure interaction (FSI) simulations (FEBio) were integrated with FIELD-II ultrasound imaging simulations to optimize both PWI and VFI for simultaneous estimation of compliance and flow in structures of variable vessel geometry and plaque stiffness at distinct ultrasound parameters. The spatiotemporal maps of wall distension rate between FSI ground truth and PWI method were in excellent agreement (r2 = 0.968–0.982) at plane wave angles compounding at (−1°, 0, 1°) and NCC window size of 7 wavelengths in both straight and 87% stenotic vessel geometries. Similar agreement was found in 2D blood flow velocity estimation between FSI ground truth and VFI with a maximum correlation of 0.9464 at the systolic upstroke and r2 of 0.7426 at the angles of (−2°, 0, 2°) and NCC window size of 60 wavelengths. Overall, the framework presented herein allowed PWI and VFI technique validation and optimization prior to in vivo application.

Efficient Stratified 3-D Scatterer Sampling for Freehand Ultrasound Simulation.

Ultrasound image simulation is a well-explored field with the main objective of generating realistic synthetic images, further used as ground truth for computational imaging algorithms or for radiologists' training. Several ultrasound simulators are already available, most of them consisting in similar steps: 1) generate a collection of tissue mimicking individual scatterers with random spatial positions and random amplitudes; 2) model the ultrasound probe and the emission and reception schemes; and 3) generate the radio frequency (RF) signals resulting from the interaction between the scatterers and the propagating ultrasound waves. This article is focused on the first step. To ensure fully developed speckle, a few tens of scatterers by resolution cell are needed, demanding to handle high amounts of data (especially in 3-D) and resulting into important computational time. The objective of this work is to explore new scatterer spatial distributions, with application to multiple coherent 2-D slice simulations from 3-D volumes. More precisely, lazy evaluation of pseudorandom schemes proves them to be highly computationally efficient compared with uniform random distribution commonly used. We also propose an end-to-end method from the 3-D tissue volume to resulting ultrasound images using coherent and 3-D-aware scatterer generation and usage in a real-time context.

Style transfer-enabled Sim2Real framework for efficient learning of robotic transesophageal echocardiography using simulation

Recently, various machine learning techniques have been proposed to realize automatic ultrasound (US) image analysis for robotic US acquisition tasks. However, obtaining large amounts of real US images for training is usually expensive or even infeasible in some clinical applications, such as transesophageal echocardiography (TEE). An alternative is to build a simulator to generate synthetic US data for training, but the differences between simulated and real US images may result in poor model performance. This work presents a novel Sim2Real framework to learn robotic US image analysis based on simulated data to address the challenges of limited real US data for robotic TEE. A style transfer model is proposed based on unsupervised contrastive learning to convert real US images into the simulation style. Moreover, a task-relevant model is designed to combine CNNs with vision transformers and trained only on the simulated data to generate task-dependent predictions. We demonstrate the effectiveness of our method in an image regression task to predict the probe position based on US images in robotic TEE. Our results show that using only simulated US data and a small amount of unlabelled real data for training, our method can achieve comparable performance to semi-supervised and fully supervised learning methods. Moreover, the effectiveness of our previously proposed CT-based US image simulation method is also indirectly confirmed.

Endobronchial Ultrasound Image Simulation for Image-Guided Bronchoscopy.

Accurate disease diagnosis and staging are essential for patients suspected of having lung cancer. The state-of-the-art minimally invasive tools used by physicians to perform these operations are bronchoscopy, for navigating the lung airways, and endobronchial ultrasound (EBUS), for localizing suspect extraluminal cancer lesions. While new image-guided systems enable accurate bronchoscope navigation close to a lesion, no means exists for guiding the final EBUS localization of an extraluminal lesion. We propose an EBUS simulation method to assist with EBUS localization. The method draws on a patient's chest computed-tomography (CT) scan to model the ultrasound signal propagation through the tissue media. The method, which is suitable for simulating EBUS images for both radial-probe and convex-probe EBUS devices, entails three steps: 1) image preprocessing, which generates a 2D CT equivalent of the EBUS scan plane; 2) EBUS scan-line computation, which models ultrasound transmission to map the CT plane into a preliminary simulated EBUS image; and 3) image post-processing, which increases realism by introducing simulated EBUS imaging effects and artifacts. Results show that the method produces simulated EBUS images that strongly resemble images generated live by a real device and compares favorably to an existing ultrasound simulation method. It also produces images at a rate greater than real time (i.e., 53 frames/sec). We also demonstrate a successful integration of the method into an image-guided EBUS bronchoscopy system. The method is effective and practical for procedure planning/preview and follow-on live guidance of EBUS bronchoscopy.

Unsupervised domain adaptation method for segmenting cross-sectional CCA images

Background and Objectives: Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners. However, these large datasets are rare, given that it is challenging and time-consuming to acquire and manually annotate in-vivo data.Methods: In this work, we present a model-based, unsupervised domain adaptation method that consists of two stages. In the first stage, the network is trained on simulated ultrasound images, which have an accurate ground truth. In the second stage, the network continues training on in-vivo data in an unsupervised way, therefore not requiring the data to be labelled. Rather than using an adversarial neural network, prior knowledge on the elliptical shape of the segmentation mask is used to detect unexpected outputs.Results: The segmentation performance was quantified using manually segmented images as ground truth. Due to the proposed domain adaptation method, the median Dice similarity coefficient increased from 0 to 0.951, outperforming a domain adversarial neural network (median Dice 0.922) and a state-of-the-art Star-Kalman algorithm that was specifically designed for this dataset (median Dice 0.942).Conclusions: The results show that it is feasible to first train a neural network on simulated data, and then apply model-based domain adaptation to further improve segmentation performance by training on unlabeled in-vivo data. This overcomes the limitation of conventional deep learning approaches to require large amounts of manually labeled in-vivo data. Since the proposed domain adaptation method only requires prior knowledge on the shape of the segmentation mask, performance can be explored in various domains and applications in future research.

An Improved Parameter Estimator of the Homodyned K Distribution Based on the Maximum Likelihood Method for Ultrasound Tissue Characterization.

The homodyned K distribution (HK) can generally describe the ultrasound backscatter envelope statistics distribution with parameters that have specific physical meaning. However, creating robust and reliable HK parameter estimates remains a crucial concern. The maximum likelihood estimator (MLE) usually yields a small variance and bias in parameter estimation. Thus, two recent studies have attempted to use MLE for parameter estimation of HK distribution. However, some of the statements in these studies are not fully justified and they may hinder the application of parameter estimation of HK distribution based on MLE. In this study, we propose a new parameter estimator for the HK distribution based on the MLE (i.e., MLE1), which overcomes the disadvantages of conventional MLE of HK distribution. The MLE1 was compared with other estimators, such as XU estimator (an estimation method based on the first moment of the intensity and tow log-moments) and ANN estimator (an estimation method based on artificial neural networks). We showed that the estimations of parameters α and k are the best overall (in terms of the relative bias, normalized standard deviation, and relative root mean squared errors) using the proposed MLE1 compared with the others based on the simulated data when the sample size was N = 1000. Moreover, we assessed the usefulness of the proposed MLE1 when the number of scatterers per resolution cell was high (i.e., α up to 80) and when the sample size was small (i.e., N = 100), and we found a satisfactory result. Tests on simulated ultrasound images based on Field II were performed and the results confirmed that the proposed MLE1 is feasible and reliable for the parameter estimation from the ultrasonic envelope signal. Therefore, the proposed MLE1 can accurately estimate the HK parameters with lower uncertainty, which presents a potential practical value for further ultrasonic applications.

Comparison of ultrasound vector flow imaging and CFD simulations with PIV measurements of flow in a left ventricular outflow trackt phantom - Implications for clinical use and in silico studies

In this study we have compared two modalities for flow quantification from measurement data; ultrasound (US) and shadow particle image velocimetry (PIV), and a flow simulation model using computational fluid dynamics (CFD). For the comparison we have used an idealized Quasi-2D phantom of the human left ventricular outflow tract (LVOT). The PIV data will serve as a reference for the true flow field in our setup. Furthermore, the US vector flow imaging (VFI) data has been post processed with model-based regularization developed to both smooth noise and sharpen physical flow features. The US VFI flow reconstruction results in an underestimation of the flow velocity magnitude compared to PIV and CFD. The CFD results coincide very well with the PIV flow field maximum velocities and curl intensity, as well as with the detailed vortex structure, however, this correspondence is subject to exact boundary conditions.

Adaptation of Cardinal Points Symmetry Landmarks Distribution Model to in-vivo B-Mode Ultrasound Images of Transverse Cross-section of Carotid Arteries

The geometry of the imaged transverse cross-section of carotid arteries in in-vivo B-mode ultrasound images are most times irregular, unsymmetrical, full of speckles and usually non-uniform. We had earlier developed a technique of cardinal point symmetry landmark distribution model (CPS-LDM) to completely characterize the Region of Interest (ROI) of the geometric shape of thick-walled simulated B-mode ultrasound images of carotid artery imaged in the transverse plane, but this was based on the symmetric property of the image. In this paper, this developed technique was applied to completely characterize the region of interest of the geometric shape of in-vivo B-mode ultrasound images of non-uniform carotid artery imaged in the transverse plane. In order to adapt the CPS-LD Model to the in-vivo carotid artery images, the single VS-VS vertical symmetry line common to the four ROIs of the symmetric image is replaced with each ROI having its own VS-VS vertical symmetry line. This adjustment enables the in-vivo carotid artery images possess symmetric properties, hence, ensuring that all mathematical operations of the CPS-LD Model are conveniently applied to them. This adaptability was observed to work well in segmenting the in-vivo carotid artery images. This paper shows the adaptive ability of the developed CPS-LD Model to successfully annotate and segment in-vivo B-mode ultrasound images of carotid arteries in the transverse cross-sectional plane either they are symmetrical or unsymmetrical.

Study on viscosity induced contrast in ultrasound color flow imaging of carotid atherosclerosis

Efficient imaging of blood flow disturbances resulted from carotid atherosclerosis plays a vital role clinically to predict brain stroke risk. Carotid atherosclerosis and its development is closely linked with raised blood viscosity. Therefore, study of viscosity changing hemodynamic effect has importance and it might be useful for improved examination of carotid atherosclerosis incorporating the viscosity induced contrast in conventional ultrasound imaging. This work considered the design of realistic models of atherosclerotic carotid artery of different stages and solved to compute the hemodisturbances using computational fluid dynamics (CFD) by finite element method (FEM) to investigate viscosity changes effect. Ultrasound color flow image of velocities of blood have been constructed using phase shift information estimated with autocorrelation of Hilbert transformed simulated backscattered radiofrequency (RF) signals from moving blood particles. The simulated ultrasound images have been compared with CFD simulation images and identified a good match between them. The atherosclerosis stages of the models have been investigated from the estimated velocity data. It has been observed that the blood velocities increase noticeably in carotid atherosclerotic growths and velocity distribution changes with viscosity variations. It is also found importantly that the viscosity induced contrast associated to atherosclerosis is detectable in ultrasound color flow imaging. The findings of this work might be useful for better investigation of carotid atherosclerosis as well as prediction of its progression to reduce the stroke risk.

Texture based Clustering Technique for Fetal Ultrasound Image Segmentation

Segmentation of fetal ultrasound image is an important and necessary task in the automation of fetal biometric measurement. Fetal ultrasound image segmentation is tedious because of the fuzzy nature and textured appearance of fetal structures. Hence, texture based Clustering is proposed for segmenting fetal ultrasound images. Clustering is performed using texture properties of the images which are used for segmenting ultrasound images of fetus. Texture based clustering technique can be used for segmenting all fetal anatomies specifically abdomen, the boundaries of which are very vague and difficult to delineate. Synthetic, simulated ultrasound images and 120 ultrasound fetal images were used for validating the method achieving an accuracy of 90%.

Robotic Intra-Operative Ultrasound: Virtual Environments and Parallel Systems

Robotic intra-operative ultrasound has the potential to improve the conventional practice of diagnosis and procedure guidance that are currently performed manually. Working towards automatic or semi-automatic ultrasound, being able to define ultrasound views and the corresponding probe poses via intelligent approaches become crucial. Based on the concept of parallel system which incorporates the ingredients of artificial systems, computational experiments, and parallel execution, this paper utilized a recent developed robotic trans-esophageal ultrasound system as the study object to explore the method for developing the corresponding virtual environments and present the potential applications of such systems. The proposed virtual system includes the use of 3D slicer as the main workspace and graphic user interface (GUI), Matlab engine to provide robotic control algorithms and customized functions, and PLUS (Public software Library for UltraSound imaging research) toolkit to generate simulated ultrasound images. Detailed implementation methods were presented and the proposed features of the system were explained. Based on this virtual system, example uses and case studies were presented to demonstrate its capabilities when used together with the physical TEE robot. This includes standard view definition and customized view optimization for pre-planning and navigation, as well as robotic control algorithm evaluations to facilitate real-time automatic probe pose adjustments. To conclude, the proposed virtual system would be a powerful tool to facilitate the further developments and clinical uses of the robotic intra-operative ultrasound systems.

Ultrasound imaging simulations using Fullwave

The latest advancements in diagnostic ultrasound imaging simulations based on the first principles of nonlinear attenuating propagation and reflection in the human body are presented.

Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms.

Accurate multimodal registration of intraoperative ultrasound (US) and preoperative computed tomography (CT) is a challenging problem. Construction of public datasets of US and CT images can accelerate the development of such image registration techniques. This can help ensure the accuracy and safety of spinal surgeries using image-guided surgery systems where an image registration is employed. In addition, we present two algorithms to register US and CT images. We present three different datasets of vertebrae with corresponding CT, US, and simulated US images. For each of the two latter datasets, we also provide 16 landmark pairs of matching structures between the CT and US images and performed fiducial registration to acquire a silver standard for assessing image registration. Besides, we proposed two patch-based rigid image registration algorithms, one based on normalized cross-correlation (NCC) and the other based on correlation ratio (CR) to register misaligned CT and US images. The CT and corresponding US images of the proposed database were pre-processed and misaligned with different error intervals, resulting in 6000 registration problems solved using both NCC and CR methods. Our results show that the methods were successful in aligning the pre-processed CT and US images by decreasing the warping index. The database provides a resource for evaluating image registration techniques. The simulated data have two applications. First, they provide the gold standard ground-truth which is difficult to obtain with ex vivo and in vivo data for validating US-CT registration methods. Second, the simulated US images can be used to validate real-time US simulation methods. Besides, the proposed image registration techniques can be useful for developing methods in clinical application.