Design consideration on integration of mechanical intravascular ultrasound and electromagnetic tracking sensor for intravascular reconstruction

PURPOSE

Considering vessel deformation, endovascular navigation requires intraoperative geometric information. Mechanical intravascular ultrasound (IVUS) with an electromagnetic (EM) sensor can be used to reconstruct blood vessels with thin diameter. However, the integration design should be evaluated based on the factors affecting the reconstruction error.

METHODS

The interference between the mechanical IVUS and EM sensor was measured in different relative positions. Two designs of the integrated catheter were evaluated by measuring the reconstruction errors using a rigid vascular phantom.

RESULTS

When the distance from the EM sensor to the field generator was 75 mm, the interference from mechanical IVUS to an EM sensor was negligible, with position and rotation errors less than 0.1 mm and 0.6°, respectively. The reconstructed vessel model for proximal IVUS transducer had a smooth surface but an inaccurate shape at large curvature of the vascular phantom. When the distance to the field generator was 175 mm, the error increased significantly.

CONCLUSION

Placing the IVUS transducer on the proximal side of the EM sensor is superior in terms of interference reduction but inferior in terms of mechanical stability compared to a distal transducer. The distal side is preferred due to better mechanical stability during catheter manipulation at larger curvature. With this configuration, surface reconstruction errors less than 1.7 mm (with RMS 0.57 mm) were achieved when the distance to the field generator was less than 175 mm.

An Emerging Era: Conformable Ultrasound Electronics

Conformable electronics are regarded as the next generation of personal healthcare monitoring and remote diagnosis devices. In recent years, piezoelectric-based conformable ultrasound electronics (cUSE) have been intensively studied due to their unique capabilities, including nonradiative monitoring, soft tissue imaging, deep signal decoding, wireless power transfer, portability, and compatibility. This review provides a comprehensive understanding of cUSE for use in biomedical and healthcare monitoring systems and a summary of their recent advancements. Following an introduction to the fundamentals of piezoelectrics and ultrasound transducers, the critical parameters for transducer design are discussed. Next, five types of cUSE with their advantages and limitations are highlighted, and the fabrication of cUSE using advanced technologies is discussed. In addition, the working function, acoustic performance, and accomplishments in various applications are thoroughly summarized. It is noted that application considerations must be given to the tradeoffs between material selection, manufacturing processes, acoustic performance, mechanical integrity, and the entire integrated system. Finally, current challenges and directions for the development of cUSE are highlighted, and research flow is provided as the roadmap for future research. In conclusion, these advances in the fields of piezoelectric materials, ultrasound transducers, and conformable electronics spark an emerging era of biomedicine and personal healthcare.

A Novel Extensible Continuum Robot with Growing Motion Capability Inspired by Plant Growth for Path-Following in Transoral Laryngeal Surgery

This article presents a novel extensible continuum robot (ECR) with growing motion capability for improved flexible access in transoral laryngeal procedures. The robot uses an extensible continuum joint with a staggered V-shaped notched structure as the backbone, driven by the pushing and pulling of superelastic Nitinol rods. The notched structure is optimized to achieve a wide range of extension/contraction and bending motion for the continuum joint. The successive and uniform deflection of the notches provides the continuum joint with excellent constant curvature bending characteristics. The bidirectional rod-driven approach expands the robot's extension capabilities with both pushing and pulling operations, and the superelasticity of the driving rods preserves the robot's bending performance. The ECR significantly increases motion dexterity and reachability through its variable length, which facilitates collision-free access to deep lesions by following the anatomy. To further exploit the advantages of the ECR in path-following for flexible access, a growing motion approach inspired by the plant growth process has been proposed to minimize the path deviation error. Characterization experiments are conducted to verify the performances of the proposed ECR. The extension ratio achieves up to 225.92%, and the average distal positioning error and hysteresis error values are 2.87% and 0.51% within the ±120° bending range. Compared with the typical continuum robot with a fixed length, the path-following deviation of this robot is reduced by more than 58.30%, effectively reducing the risk of collision during access. Phantom experiments validate the feasibility of the proposed concept in flexible access procedures.

Compact Design and Task Space Control of a Robotic Transcatheter Delivery System for Mitral Valve Implant

Mitral regurgitation (MR) is one of the most common valvular abnormalities, and the gold-standard for treatment is surgical mitral valve repair/replacement. Most patients with severe MR are over the age of 75, which makes open-heart surgery challenging. Thus, minimally invasive surgeries using transcatheter approaches are gaining popularity. This paper proposes the next generation of a robotic transcatheter delivery system for the mitral valve implant that focuses on the design of the actuation system, modeling, and task space control. The proposed actuation system is compact while still enabling bidirectional torsion, bending, and prismatic joint motion. A pulley structure is employed to actuate the torsion and bending joints using only one motor per joint in conjunction with an antagonistic passive spring to reduce tendon slack. The robotic transcatheter is also optimized to increase its stability and reduce bending deflection. An inverse kinematics model (with an optimization algorithm), singularity analysis method, and joint hysteresis and compensation model are developed and verified. Finally, a task space controller is also proposed. Experiments, including trajectory tracking and demonstrations of the robot motion in an ex vivo porcine heart and a phantom heart through a tortuous path are presented.

Technical and Clinical Progress on Robot-Assisted Endovascular Interventions: A Review

Prior methods of patient care have changed in recent years due to the availability of minimally invasive surgical platforms for endovascular interventions. These platforms have demonstrated the ability to improve patients' vascular intervention outcomes, and global morbidities and mortalities from vascular disease are decreasing. Nonetheless, there are still concerns about the long-term effects of exposing interventionalists and patients to the operational hazards in the cath lab, and the perioperative risks that patients undergo. For these reasons, robot-assisted vascular interventions were developed to provide interventionalists with the ability to perform minimally invasive procedures with improved surgical workflow. We conducted a thorough literature search and presented a review of 130 studies published within the last 20 years that focused on robot-assisted endovascular interventions and are closely related to the current gains and obstacles of vascular interventional robots published up to 2022. We assessed both the research-based prototypes and commercial products, with an emphasis on their technical characteristics and application domains. Furthermore, we outlined how the robotic platforms enhanced both surgeons' and patients' perioperative experiences of robot-assisted vascular interventions. Finally, we summarized our findings and proposed three key milestones that could improve the development of the next-generation vascular interventional robots.

Discrete soft actor-critic with auto-encoder on vascular robotic system

Instrument delivery is critical part in vascular intervention surgery. Due to the soft-body structure of instruments, the relationship between manipulation commands and instrument motion is non-linear, making instrument delivery challenging and time-consuming. Reinforcement learning has the potential to learn manipulation skills and automate instrument delivery with enhanced success rates and reduced workload of physicians. However, due to the sample inefficiency when using high-dimensional images, existing reinforcement learning algorithms are limited on realistic vascular robotic systems. To alleviate this problem, this paper proposes discrete soft actor-critic with auto-encoder (DSAC-AE) that augments SAC-discrete with an auxiliary reconstruction task. The algorithm is applied with distributed sample collection and parameter update in a robot-assisted preclinical environment. Experimental results indicate that guidewire delivery can be automatically implemented after 50k sampling steps in less than 15 h, demonstrating the proposed algorithm has the great potential to learn manipulation skill for vascular robotic systems.

Multilevel structure-preserved GAN for domain adaptation in intravascular ultrasound analysis

The poor generalizability of intravascular ultrasound (IVUS) analysis methods caused by the great diversity of IVUS datasets is hopefully addressed by the domain adaptation strategy. However, existing domain adaptation models underperform in intravascular structural preservation, because of the complex pathology and low contrast in IVUS images. Losing structural information during the domain adaptation would lead to inaccurate analyses of vascular states. In this paper, we propose a Multilevel Structure-Preserved Generative Adversarial Network (MSP-GAN) for transferring IVUS domains while maintaining intravascular structures. On the generator-discriminator baseline, the MSP-GAN integrates the transformer, contrastive restraint, and self-ensembling strategy, for effectively preserving structures in multi-levels, including global, local, and fine levels. For the global-level pathology maintenance, the generator explores long-range dependencies in IVUS images via an incorporated vision transformer. For the local-level anatomy consistency, a region-to-region correspondence is forced between the translated and source images via a superpixel-wise multiscale contrastive (SMC) constraint. For reducing distortions of fine-level structures, a self-ensembling mean teacher generates the pixel-wise pseudo-label and restricts the translated image via an uncertainty-aware teacher-student consistency (TSC) constraint. Experiments were conducted on 20 MHz and 40 MHz IVUS datasets from different medical centers. Ablation studies illustrate that each innovation contributes to intravascular structural preservation. Comparisons with representative domain adaptation models illustrate the superiority of the MSP-GAN in the structural preservation. Further comparisons with the state-of-the-art IVUS analysis accuracy demonstrate that the MSP-GAN is effective in enlarging the generalizability of diverse IVUS analysis methods and promoting accurate vessel and lumen segmentation and stenosis-related parameter quantification.

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

AwCPM-Net: A Collaborative Constraint GAN for 3D Coronary Artery Reconstruction in Intravascular Ultrasound Sequences

3D coronary artery reconstruction (3D-CAR) in intravascular ultrasound (IVUS) sequences allows quantitative analyses of vessel properties. Existing methods treat two main tasks of the 3D-CAR separately, including the cardiac phase retrieval (CPR) and the membrane border extraction (MBE). They ignore the CPR-MBE connection that could achieve mutual promotions to both tasks. In this paper, we pioneer to achieve one-step 3D-CAR via a collaborative constraint generative adversarial network (GAN) named the AwCPM-Net. The AwCPM-Net consists of a dual-task collaborative generator and a dual-task constraint discriminator. The generator combines a self-supervised CPR branch with a semi-supervised MBE branch via a warming-up connection. The discriminator promotes dual-branch predictions simultaneously. The CPR branch requires no annotations and outputs inter-frame deformation fields used for identifying cardiac phases. Deformation fields are additionally constrained by the MBE branch and the discriminator. The MBE branch predicts membrane boundaries for each frame. Two aspects assist the semi-supervised segmentation: annotation augmentation by deformation fields of the CPR branch; information exploitation on unlabeled images enabled by GAN design. Trained and tested on an IVUS dataset acquired from atherosclerosis patients, the AwCPM-Net is effective in both CPR and MBE tasks, superior to state-of-the-art IVUS CPR or MBE methods. Hence, the AwCPM-Net reconstructs reliable 3D artery anatomy in the IVUS modality.

Three-Dimensional Intravascular Ultrasound Imaging Using a Miniature Helical Ultrasonic Motor

Existing 3-D intravascular ultrasound (IVUS) systems that combine two electromagnetic (EM) motors to drive catheters are bulky and require considerable efforts to eliminate EM interference (EMI). Here, we propose a new scanning method to realize 3-D IVUS imaging using a helical ultrasonic motor to overcome the aforementioned issues. The ultrasonic motor with compact dimensions (7-mm outer diameter and 30-mm longitudinal length), lightweight (20.5 g), and free of EMI exhibits a great application potential in mobile imaging devices. In particular, it can simultaneously perform rotary and linear motions, facilitating precise 3-D scanning of an imaging catheter. Experimental results show that the signal-to-noise ratio (SNR) of raw images obtained using the ultrasonic motor is 5.3 dB better than that of an EM motor. Moreover, the proposed imaging device exhibits the maximum rotary speed of 12.3 r/s and the positioning accuracy of 2.6 [Formula: see text] at a driving voltage of 240 V_p-p. The 3-D wire phantom imaging and 3-D tube phantom imaging are performed to evaluate the performance of the imaging device. Finally, the in vitro imaging of a porcine coronary artery demonstrates that the layered architecture of the vessel can be precisely identified while significantly increasing the SNR of the raw images.

Design and Performance Investigation of a Robot-Assisted Flexible Ureteroscopy System

Flexible ureteroscopy (FURS) has been developed and has become a preferred routine procedure for both diagnosis and treatment of kidney stones and other renal diseases inside the urinary tract. The traditional manual FURS procedure is highly skill-demanding and easily brings about physical fatigue and burnout for surgeons. The improper operational ergonomics and fragile instruments also hinder its further development and patient safety enhancement. A robotic system is presented in this paper to assist the FURS procedure. The system with a master-slave configuration is designed based on the requirement analysis in manual operation. A joint-to-joint mapping strategy and several control strategies are built to realize intuitive and safe operations. Both phantom and animal experiments validate that the robot has significant advantages over manual operations, including the easy-to-use manner, reduced intraoperative time, and improved surgical ergonomics. The proposed robotic system can solve the major drawbacks of manual FURS. The test results demonstrate that the robot has great potential for clinical applications.

The Role of Deep Learning-Based Echocardiography in the Diagnosis and Evaluation of the Effects of Routine Anti-Heart-Failure Western Medicines in Elderly Patients with Acute Left Heart Failure

Objective

The role of deep learning-based echocardiography in the diagnosis and evaluation of the effects of routine anti-heart-failure Western medicines was investigated in elderly patients with acute left heart failure (ALHF).

Methods

A total of 80 elderly patients with ALHF admitted to Affiliated Hangzhou First People's Hospital from August 2017 to February 2019 were selected as the research objects, and they were divided randomly into a control group and an observation group, with 40 cases in each group. Then, a deep convolutional neural network (DCNN) algorithm model was established, and image preprocessing was carried out. The binarized threshold segmentation was used for denoising, and the image was for illumination processing to balance the overall brightness of the image and increase the usable data of the model, so as to reduce the interference of subsequent feature extraction. Finally, the detailed module of deep convolutional layer network algorithm was realized. Besides, the patients from the control group were given routine echocardiography, and the observation group underwent echocardiography based on deep learning algorithm. Moreover, the hospitalization status of patients from the two groups was observed and recorded, including mortality rate, rehospitalization rate, average length of hospitalization, and hospitalization expenses. The diagnostic accuracy of the two examination methods was compared, and the electrocardiogram (ECG) and echocardiographic parameters as well as patients' quality of life were recorded in both groups at the basic state and 5 months after drug treatment.

Results

After comparison, the rehospitalization rate and mortality rate of the observation group were lower than the rates of the control group, but the diagnostic accuracy was higher than that of the control group. However, the difference between the two groups of patients was not statistically marked (P > 0.05). The length and expenses of hospitalization of the observation group were both less than those of the control group. The specificity, sensitivity, and accuracy of the examination methods in the observation group were higher than those of the control group, and the differences were statistically marked (P < 0.05). There was a statistically great difference between the interventricular delay (IVD) of the echocardiographic parameters of patients from the two groups at the basic state and the left ventricular electromechanical delay (LVEMD) parameter values after 5 months of treatment (P < 0.05), but there was no significant difference in the other parameters. After treatment, the quality of life of patients from the two groups was improved, while the observation group was more marked than the control group (P < 0.05).

Conclusion

Echocardiography based on deep learning algorithm had high diagnostic accuracy and could reduce the possibility of cardiovascular events in patients with heart failure, so as to decrease the mortality rate and diagnosis and treatment costs. Moreover, it had an obvious diagnostic effect, which was conducive to the timely detection and treatment of clinical diseases.

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

This paper presents an in-depth study and analysis of the 3D arterial centerline in spiral CT coronary angiography, and constructs its detection and extraction technique. The first time, the distance transform is used to complete the boundary search of the original figure; the second time, the distance transform is used to calculate the value of the distance transform of all voxels, and according to the value of the distance transform, unnecessary voxels are deleted, to complete the initial contraction of the vascular region and reduce the computational consumption in the next process; then, the nonwitnessed voxels are used to construct the maximum inner joint sphere model and find the skeletal voxels that can reflect the shape of the original figure. Finally, the skeletal lines were optimized on these initially extracted skeletal voxels using a dichotomous-like principle to obtain the final coronary artery centerline. Through the evaluation of the experimental results, the algorithm can extract the coronary centerline more accurately. In this paper, the segmentation method is evaluated on the test set data by two kinds of indexes: one is the index of segmentation result evaluation, including dice coefficient, accuracy, specificity, and sensitivity; the other is the index of clinical diagnosis result evaluation, which is to refine the segmentation result for vessel diameter detection. The results obtained in this paper were compared with the physicians' labeling results. In terms of network performance, the Dice coefficient obtained in this paper was 0.89, the accuracy was 98.36%, the sensitivity was 93.36%, and the specificity was 98.76%, which reflected certain advantages in comparison with the advanced methods proposed by previous authors. In terms of clinical evaluation indexes, by performing skeleton line extraction and diameter calculation on the results obtained by the segmentation method proposed in this paper, the absolute error obtained after comparing with the diameter of the labeled image was 0.382 and the relative error was 0.112, which indicates that the segmentation method in this paper can recover the vessel contour more accurately. Then, the results of coronary artery centerline extraction with and without fine branch elimination were evaluated, which proved that the coronary artery centerline has higher accuracy after fine branch elimination. The algorithm is also used to extract the centerline of the complete coronary artery tree, and the results prove that the algorithm has better results for the centerline extraction of the complete coronary vascular tree.

Visual-electromagnetic system: A novel fusion-based monocular localization, reconstruction, and measurement for flexible ureteroscopy

BACKGROUND

During flexible ureteroscopy (FURS), surgeons may lose orientation due to intrarenal structural similarities and complex shape of the pyelocaliceal cavity. Decision-making required after initially misjudging stone size will also increase the operative time and risk of severe complications.

METHODS

A intraoperative navigation system based on electromagnetic tracking (EMT) and simultaneous localization and mapping (SLAM) was proposed to track the tip of the ureteroscope and reconstruct a dense intrarenal three-dimensional (3D) map. Furthermore, the contour lines of stones were segmented to measure the size.

RESULTS

Our system was evaluated on a kidney phantom, achieving an absolute trajectory accuracy root mean square error (RMSE) of 0.6 mm. The median error of the longitudinal and transversal measurements was 0.061 and 0.074 mm, respectively. The in vivo experiment also demonstrated the effectiveness.

CONCLUSION

The proposed system worked effectively in tracking and measurement. Further, this system can be extended to other surgical applications involving cavities, branches and intelligent robotic surgery.

A Single Image 3D Reconstruction Method Based on a Novel Monocular Vision System

Three-dimensional (3D) reconstruction and measurement are popular techniques in precision manufacturing processes. In this manuscript, a single image 3D reconstruction method is proposed based on a novel monocular vision system, which includes a three-level charge coupled device (3-CCD) camera and a ring structured multi-color light emitting diode (LED) illumination. Firstly, a procedure for the calibration of the illumination's parameters, including LEDs' mounted angles, distribution density and incident angles, is proposed. Secondly, the incident light information, the color distribution information and gray level information are extracted from the acquired image, and the 3D reconstruction model is built based on the camera imaging model. Thirdly, the surface height information of the detected object within the field of view is computed based on the built model. The proposed method aims at solving the uncertainty and the slow convergence issues arising in 3D surface topography reconstruction using current shape-from-shading (SFS) methods. Three-dimensional reconstruction experimental tests are carried out on convex, concave, angular surfaces and on a mobile subscriber identification module (SIM) card slot, showing relative errors less than 3.6%, respectively. Advantages of the proposed method include a reduced time for 3D surface reconstruction compared to other methods, demonstrating good suitability of the proposed method in reconstructing surface 3D morphology.