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Background: Transseptal puncture (TSP) is a critical prerequisite for left-sided cardiac interventions, such as atrial fibrillation (AF) ablation and left atrial appendage closure. Despite its routine nature, TSP can be technically demanding and carries a risk of complications. This study presents a novel, patient-specific, anthropomorphic phantom for TSP simulation training that can be used with X-ray fluoroscopy and ultrasound imaging., Methods: The TSP phantom was developed using additive manufacturing techniques and features a replaceable fossa ovalis (FO) component to allow for multiple punctures without replacing the entire model. Four cardiologists and one cardiology trainee performed TSP on the simulator, and their performance was assessed using four metrics: global isotropy index, distance from the centroid, time taken to perform TSP, and a set of 5-point Likert scale questions to evaluate the clinicians' perception of the phantom's realism and utility., Results: The results demonstrate the simulator's potential as a training tool for interventional cardiology, providing a realistic and controllable environment for clinicians to refine their TSP skills. Experienced cardiologists tended to cluster their puncture points closer to regions of the FO associated with higher global isotropy index scores, indicating a relationship between experience and optimal puncture localization. The questionnaire analysis revealed that participants generally agreed on the phantom's realistic anatomical representation and ability to accurately visualize the TSP site under fluoroscopic guidance., Conclusions: The TSP simulator can be incorporated into training programs, offering trainees the opportunity to improve tool handling, spatial coordination, and manual dexterity prior to performing the procedure on patients. Further studies with larger sample sizes and longitudinal assessments are needed to establish the simulator's impact on TSP performance and patient outcomes., (© 2024. The Author(s).)

Cardiac catheterization for congenital heart disease (CHD) performed under fluoroscopic guidance still lacks definition and requires exposure to ionizing radiation and contrast agents, with most patients needing multiple procedures through their lifetime, leading to cumulative radiation risks. While fusion overlay techniques have been employed in the past to aid, these have been limited to a single plane, while interventions are traditionally performed under biplane fluoroscopy. We describe our initial experience performing cardiac catheterizations guided by an enhanced biplane GuideCCI system © (Siemens Healthcare, Germany) augmented by 3D magnetic resonance imaging and computed tomography modeling. Twenty-one children and young adults with CHD undergoing catheterization procedures between October 2019 and May 2021 were chosen based on their degree of complexity of cardiac anatomy. 3D stereolithography models were generated, overlayed, and displayed in real time, alongside angiographs in both planes on the screen during these procedures. We report successful implementation of this novel technology for performance of 26 interventions including stent placements, balloon dilations, vessel occlusion and percutaneous valve and transvenous pacemaker implantation all in patients with various complex cardiac anatomies. A statistically significant reduction in radiation and contrast use was noted for coarctation of the aorta stent angioplasty and transcatheter pulmonary valve replacement when compared with national benchmarks and local institutional metrics (with and without single plane overlay). No complications were encountered with the use of this technology. Use of a tracheal registration technique provided very good correlation in most cases. Operators preferred using biplane augmented catheterization over traditional fluoroscopy in patients with complex cardiac anatomy undergoing interventions., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Introduction: Ultrasound is well-established as an imaging modality for diagnostic and interventional purposes. However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies. Automating the image acquisition task could improve acquisition reproducibility and quality but training such an algorithm requires large amounts of navigation data, not saved in routine examinations., Methods: We propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions, such that this pipeline can later be used by learning algorithms for navigation. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized volumetric data representation and GPU-accelerated Monte Carlo path tracing to generate view-dependent and patient-specific ultrasound images., Results: We extensively validate the correctness of our pipeline with a phantom experiment, where structures' sizes, contrast and speckle noise properties are assessed. Furthermore, we demonstrate its usability to train neural networks for navigation in an echocardiography view classification experiment by generating synthetic images from more than 1,000 patients. Networks pre-trained with our simulations achieve significantly superior performance in settings where large real datasets are not available, especially for under-represented classes., Discussion: The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated., Competing Interests: PK, KP, VS, Y-HK and FCG are employed by Siemens Healthineers. RL and TM were employed by Siemens Healthineers. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Amadou, Peralta, Dryburgh, Klein, Petkov, Housden, Singh, Liao, Kim, Ghesu, Mansi, Rajani, Young and Rhode.)

This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24-36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66-100% visibility of relevant craniofacial and head structures in the SVR output, and 20-100% and 60-90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20-36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing.Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy.Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment., (© 2024. Crown.)

Purpose: Robot-assisted vitreoretinal surgery provides precise and consistent operations on the back of the eye. To perform this safely, knowledge of the surgical instrument's remote centre of motion (RCM) and the location of the insertion point into the eye (trocar) is required. This enables the robot to align both positions to pivot the instrument about the trocar, thus preventing any damaging lateral forces from being exerted., Methods: Building on a system developed in previous work, this study presents a trocar localisation method that uses a micro-camera mounted on a vitreoretinal surgical forceps, to track two ArUco markers attached on either side of a trocar. The trocar position is the estimated midpoint between the markers., Results: Experimental evaluation of the trocar localisation was conducted. Results showed an RMSE of 1.82 mm for the localisation of the markers and an RMSE of 1.24 mm for the trocar localisation., Conclusions: The proposed camera-based trocar localisation presents reasonable consistency and accuracy and shows improved results compared to other current methods. Optimum accuracy for this application would necessitate a 1.4 mm absolute error margin, which corresponds to the trocar's radius. The trocar localisation results are successfully found within this margin, yet the marker localisation would require further refinement to ensure consistency of localisation within the error margin. Further work will refine these position estimates and ensure the error stays consistently within this boundary., (© 2023. The Author(s).)

Purpose: The use of robotics is emerging for performing interventional radiology procedures. Robots in interventional radiology are typically controlled using button presses and joystick movements. This study identified how different human-robot interfaces affect endovascular surgical performance using interventional radiology simulations., Methods: Nine participants performed a navigation task on an interventional radiology simulator with three different human-computer interfaces. Using Simulation Open Framework Architecture we developed a simulation profile of vessels, catheters and guidewires. We designed and manufactured a bespoke haptic interventional radiology controller for robotic systems to control the simulation. Metrics including time taken for navigation, number of incorrect catheterisations, number of catheter and guidewire prolapses and forces applied to vessel walls were measured and used to characterise the interfaces. Finally, participants responded to a questionnaire to evaluate the perception of the controllers., Results: Time taken for navigation, number of incorrect catheterisations and the number of catheter and guidewire prolapses, showed that the device-mimicking controller is better suited for controlling interventional neuroradiology procedures over joystick control approaches. Qualitative metrics also showed that interventional radiologists prefer a device-mimicking controller approach over a joystick approach., Conclusion: Of the four metrics used to compare and contrast the human-robot interfaces, three conclusively showed that a device-mimicking controller was better suited for controlling interventional neuroradiology robotics., (© 2023. The Author(s).)

Three-dimensional (3D) segmentation, a process involving digitally marking anatomical structures on cross-sectional images such as computed tomography (CT), and 3D printing (3DP) are being increasingly utilized in medical education. Exposure to this technology within medical schools and hospitals remains limited in the United Kingdom. M3dicube UK, a national medical student, and junior doctor-led 3DP interest group piloted a 3D image segmentation workshop to gauge the impact of incorporating 3D segmentation technology on anatomical education. The workshop, piloted with medical students and doctors within the United Kingdom between September 2020 and 2021, introduced participants to 3D segmentation and offered practical experience segmenting anatomical models. Thirty-three participants were recruited, with 33 pre-workshop and 24 post-workshop surveys completed. Two-tailed t-tests were used to compare mean scores. From pre- to post-workshop, increases were noted in participants' confidence in interpreting CT scans (2.36 to 3.13, p = 0.010) and interacting with 3D printing technology (2.15 to 3.33, p = 0.00053), perceived utility of creating 3D models to aid image interpretation (4.18 to 4.45, p = 0.0027), improved anatomical understanding (4.2 to 4.7, p = 0.0018), and utility in medical education (4.45 to 4.79, p = 0.077). This pilot study provides early evidence of the utility of exposing medical students and healthcare professionals in the United Kingdom to 3D segmentation as part of their anatomical education, with additional benefit in imaging interpretation ability., (© 2023 The Authors. Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)

Robotic ultrasonography potentially acts as an essential aid to medical diagnosis. To overcome the limitations in robotic ultrasonography, in this paper, we proposed a novel self-adaptive parallel manipulator (SAPM) that can automatically adjust the ultrasound (US) probe pose to adapt to various contours of scanned areas, provide approximate constant operating forces/torques, achieve mechanical measurement, and cushion undesired produced forces. A novel parallel adjustment mechanism is proposed to attain automatic pose adjustment with 3 degrees of freedom (DOFs). This mechanism enables the US probe to adapt to different scanned areas and to perform the scanning with approximate constant forces and torques. Besides, we present a mechanical measurement and safety protection method that can be integrated into the SAPM and used as operation status monitoring and early warning during scanning procedures by capturing operating forces and torques. Experiments were carried out to calibrate the measurement and buffer units and evaluate the performance of the SAPM. Experimental results show the ability of the SAPM to provide 3-DoFs motion and operating force/torque measurement and automatically adjust the US probe pose to capture US images of equally good quality compared to a manual sonographer scan. Moreover, it has characteristics similar to soft robots that could significantly improve operation safety, and could be extended to some other engineering or medical applications.

Advances in technology allowing the combination of medical imaging and three-dimensional printing have greatly benefitted thoracic surgery, allowing for the creation of complex prostheses. Surgical education is also a significant application of three-dimensional printing, especially for the development of simulation-based training models. Aiming to show how three-dimensional printing can benefit patients and clinicians in thoracic surgery, an optimized method to create patient-specific chest wall prosthesis using three-dimensional printing was developed and clinically validated. An artificial chest simulator for surgical training was also developed, replicating the human anatomy with high realism and accurately simulating a minimally invasive lobectomy., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Aortic stenosis is a condition which is fatal if left untreated. Novel quantitative imaging techniques which better characterise transvalvular pressure drops are being developed but require refinement and validation. A customisable and cost-effective workbench valve phantom circuit capable of replicating valve mechanics and pathology was created. The reproducibility and relationship of differing haemodynamic metrics were assessed from ground truth pressure data alongside imaging compatibility. The phantom met the requirements to capture ground truth pressure data alongside ultrasound and magnetic resonance image compatibility. The reproducibility was successfully tested. The robustness of three different pressure drop metrics was assessed: whilst the peak and net pressure drops provide a robust assessment of the stenotic burden in our phantom, the peak-to-peak pressure drop is a metric that is confounded by non-valvular factors such as wave reflection. The peak-to-peak pressure drop is a metric that should be reconsidered in clinical practice. The left panel shows manufacture of low cost, functional valves. The central section demonstrates circuit layout, representative MRI and US images alongside gross valve morphologies. The right panel shows the different pressure drop metrics that were assessed for reproducibility., (© 2023. The Author(s).)

Objective: Since Augmented Reality (AR) and 3D visualization have proven to be of great significance to the safety and effectiveness of surgical outcomes, will autostereoscopic 3D AR without glasses bring new opportunities for surgical navigation of laparoscopic surgery?, Methods: We used the CPD-based deformation registration algorithm and the proposed virtual view generation algorithm, realizing a deformable autostereoscopic 3D AR navigation framework for laparoscopic surgery. The depth perception and user experience of the 3D AR navigation were evaluated compared with the 2D AR display using an in-vitro porcine heart and offline clinical partial nephrectomy laparoscopic images., Results: The autostereoscopic 3D AR allowed participants to have a more consistent spatial perception as well as a shorter measuring time than 2D AR with significant difference of p < 0.05. It can also improve relative depth perception for smaller distance separation of objects < = 3.28 mm. However, the autostereoscopic 3D AR perceived a worse experience compared to 2D AR in the user experience., Conclusion: Autostereoscopic 3D AR shows a more efficient and robust sense of spatial scale than 2D AR with better potential to shorten the operating time and improve surgical outcomes than 2D AR, but image blur and distortion are issues that must be solved to improve the perception effect. High precise registration and high fluency visualization requirements could make autostereoscopic 3D AR navigation for soft tissue more challenging., Significance: Our work lays a theoretical foundation for the further development of laparoscopic surgical navigation.

Soft robots that grow through eversion/apical extension can effectively navigate fragile environments such as ducts and vessels inside the human body. This paper presents the physics-based model of a miniature steerable eversion growing robot. We demonstrate the robot's growing, steering, stiffening and interaction capabilities. The interaction between two robot-internal components is explored, i.e., a steerable catheter for robot tip orientation, and a growing sheath for robot elongation/retraction. The behavior of the growing robot under different inner pressures and external tip forces is investigated. Simulations are carried out within the SOFA framework. Extensive experimentation with a physical robot setup demonstrates agreement with the simulations. The comparison demonstrates a mean absolute error of 10 - 20% between simulation and experimental results for curvature values, including catheter-only experiments, sheath-only experiments and full system experiments. To our knowledge, this is the first work to explore physics-based modelling of a tendon-driven steerable eversion growing robot. While our work is motivated by early breast cancer detection through mammary duct inspection and uses our MAMMOBOT robot prototype, our approach is general and relevant to similar growing robots.

Medical ultrasound is of increasing importance in medical diagnosis and intraoperative assistance and possesses great potential advantages when integrated with robotics. However, some concerns, including the operation efficiency, operation safety, image quality, and comfort of patients, remain after introducing robotics into medical ultrasound. In this paper, an ultrasound robot integrating a force control mechanism, force/torque measurement mechanism, and online adjustment method, is proposed to overcome the current limitations. The ultrasound robot can measure operating forces and torques, provide adjustable constant operating forces, eliminate great operating forces introduced by accidental operations, and achieve various scanning depths based on clinical requirements. The proposed ultrasound robot would potentially facilitate sonographers to find the targets quickly, improve operation safety and efficiency, and decrease patients' discomfort. Simulations and experiments were carried out to evaluate the performance of the ultrasound robot. Experimental results show that the proposed ultrasound robot is able to detect operating force in the z-direction and torques around the x- and y- directions with errors of 3.53% F.S., 6.68% F.S., and 6.11% F.S., respectively, maintain the constant operating force with errors of less than 0.57N, and achieve various scanning depths for target searching and imaging. This proposed ultrasound robot has good performance and would potentially be used in medical ultrasound.

Objectives: The aim of this study is to develop an automated method of regional scar detection on clinically standard computed tomography angiography (CTA) using encoder-decoder networks with latent space classification., Background: Localising scar in cardiac patients can assist in diagnosis and guide interventions. Magnetic resonance imaging (MRI) with late gadolinium enhancement (LGE) is the clinical gold standard for scar imaging; however, it is commonly contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is widely used as a first-line imaging modality of cardiac applications., Methods: A dataset of 79 patients with both clinically indicated MRI LGE and subsequent CTA scans was used to train and validate networks to classify septal and lateral scar presence within short axis left ventricle slices. Two designs of encoder-decoder networks were compared, with one encoding anatomical shape in the latent space. Ground truth was established by segmenting scar in MRI LGE and registering this to the CTA images. Short axis slices were taken from the CTA, which served as the input to the networks. An independent external set of 22 cases (27% the size of the cross-validation set) was used to test the best network., Results: A network classifying lateral scar only achieved an area under ROC curve of 0.75, with a sensitivity of 0.79 and specificity of 0.62 on the independent test set. The results of septal scar classification were poor (AUC < 0.6) for all networks. This was likely due to a high class imbalance. The highest AUC network encoded anatomical shape information in the network latent space, indicating it was important for the successful classification of lateral scar., Conclusions: Automatic lateral wall scar detection can be performed from a routine cardiac CTA with reasonable accuracy, without any scar specific imaging. This requires only a single acquisition in the cardiac cycle. In a clinical setting, this could be useful for pre-procedure planning, especially where MRI is contraindicated. Further work with more septal scar present is warranted to improve the usefulness of this approach., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Tumors involving the chest wall may require extensive resection and reconstruction. This study aims to evaluate functional, cosmetic results, and quality of life (QoL) in patients who had a reconstruction based on patient-specific 3-dimensional (3D) printing., Methods: The patient-specific chest wall prosthesis was created for 10 patients. The anatomical models were 3D printed and used to produce a silicone mold that was filled with methyl methacrylate to create the customized prosthesis. Evaluation of the reconstruction was completed with a QoL assessment and postoperative tracking of patients' chest motion, using infrared markers. The distance between plot points representing markers on the operated and contralateral sides was measured to assess symmetrical motion., Results: Twenty-three consecutive patients were enrolled, with the median age of 64 years. Thirteen patients underwent a nonrigid reconstruction, and 10 had a patient-specific rigid reconstruction with methyl methacrylate. The median number of ribs resected was 3. No postoperative complications or morbidity related to the prostheses were reported. The median hospital stay in the nonrigid reconstruction group was 8.5 days compared with 7.5 days (p = .167) in the rigid reconstruction group. Postoperatively, most patients had low levels of symptoms, with 82% experiencing chest pain and 53% experiencing dyspnea. Rigid reconstruction patients demonstrated more symmetrical breathing motion compared with nonrigid reconstruction patients. The mean distances were 2.32 ± 2.18 and 7.28 ± 5.87 (P < .00001), respectively., Conclusions: This study shows that a 3D patient-specific prosthesis is feasible and safe, suggesting a possible trend toward improved breathing mechanics, QoL, and cosmetic results., (Copyright © 2022 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training., (© 2022. The Author(s).)

Patients with advanced cancer undergoing chest wall resection may require reconstruction. Currently, rib prostheses are created by segmenting computed tomography images, which is time-consuming and labour intensive. The aim was to optimise the production of digital rib models based on a patient's age, weight, height and gender. A statistical shape model of human ribs was created and used to synthetise rib models, which were compared to the ones produced by segmentation and mirroring. The segmentation took 11.56±1.60 min compared to 0.027 ±0.009 min using the new technique. The average mesh error between the mirroring technique and segmentation was 0.58±0.25 mm (right ribs), and 0.87±0.18 mm (left ribs), compared to 1.37±0.66 mm ( ) and 1.68 ±0.77 mm ( ), respectively, for the new technique. The new technique is promising for the efficiency and ease-of-use in the clinical environment. Clinical Relevance- This is an optimised 3D modelling method providing clinicians with a time-efficient technique to create patient-specific rib prostheses, without any expertise or software knowledge required.

Background: Robotically performed neurointerventional surgery has the potential to reduce occupational hazards to staff, perform intervention with greater precision, and could be a viable solution for teleoperated neurointerventional procedures., Objective: To determine the indication, robotic systems used, efficacy, safety, and the degree of manual assistance required for robotically performed neurointervention., Methods: We conducted a systematic review of the literature up to, and including, articles published on April 12, 2021. Medline, PubMed, Embase, and Cochrane register databases were searched using medical subject heading terms to identify reports of robotically performed neurointervention, including diagnostic cerebral angiography and carotid artery intervention., Results: A total of 8 articles treating 81 patients were included. Only one case report used a robotic system for intracranial intervention, the remaining indications being cerebral angiography and carotid artery intervention. Only one study performed a comparison of robotic and manual procedures. Across all studies, the technical success rate was 96% and the clinical success rate was 100%. All cases required a degree of manual assistance. No studies had clearly defined patient selection criteria, reference standards, or index tests, preventing meaningful statistical analysis., Conclusions: Given the clinical success, it is plausible that robotically performed neurointerventional procedures will eventually benefit patients and reduce occupational hazards for staff; however, there is no high-level efficacy and safety evidence to support this assertion. Limitations of current robotic systems and the challenges that must be overcome to realize the potential for remote teleoperated neurointervention require further investigation., Competing Interests: Competing interests: VMP has acted as a consultant for Corindus and is an investigator in the Corpath GRX Neuro Study. The remaining authors have no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

A COVID-19 diagnosis is primarily determined by RT-PCR or rapid lateral-flow testing, although chest imaging has been shown to detect manifestations of the virus. This article reviews the role of imaging (CT and X-ray), in the diagnosis of COVID-19, focusing on the published studies that have applied artificial intelligence with the purpose of detecting COVID-19 or reaching a differential diagnosis between various respiratory infections. In this study, ArXiv, MedRxiv, PubMed, and Google Scholar were searched for studies using the criteria terms 'deep learning', 'artificial intelligence', 'medical imaging', 'COVID-19' and 'SARS-CoV-2'. The identified studies were assessed using a modified version of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). Twenty studies fulfilled the inclusion criteria for this review. Out of those selected, 11 papers evaluated the use of artificial intelligence (AI) for chest X-ray and 12 for CT. The size of datasets ranged from 239 to 19,250 images, with sensitivities, specificities and AUCs ranging from 0.789-1.00, 0.843-1.00 and 0.850-1.00. While AI demonstrates excellent diagnostic potential, broader application of this method is hindered by the lack of relevant comparators in studies, sufficiently sized datasets, and independent testing.

Purpose: Reducing X-ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low-dose X-ray fluoroscopy images but may compromise clinically important details required by cardiologists., Methods: In order to obtain denoised X-ray fluoroscopy images whilst preserving details, we propose a novel deep-learning-based denoising framework, namely edge-enhancement densenet (EEDN), in which an attention-awareness edge-enhancement module is designed to increase edge sharpness. In this framework, a CNN-based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra-dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X-ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low-dose X-ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre-processing tool., Results: The average signal-to-noise ratio of X-ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images., Conclusion: The proposed deep learning-based framework shows promising capability for denoising interventional X-ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre-processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objective: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task., Method: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed., Results: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved., Conclusion: A novel and fully automatic detection framework and its clinical applications are developed., Significance: The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures.

Reducing radiation dose in cardiac catheter-based X-ray procedures increases safety but also image noise and artifacts. Excessive noise and artifacts can compromise vital image information, which can affect clinical decision-making. Developing more effective X-ray denoising methodologies will be beneficial to both patients and healthcare professionals by allowing imaging at lower radiation dose without compromising image information. This paper proposes a framework based on a convolutional neural network (CNN), namely Ultra-Dense Denoising Network (UDDN), for low-dose X-ray image denoising. To promote feature extraction, we designed a novel residual block which establishes a solid correlation among multiple-path neural units via abundant cross connections in its representation enhancement section. Experiments on synthetic additive noise X-ray data show that the UDDN achieves statistically significant higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) than other comparative methods. We enhanced the clinical adaptability of our framework by training using normally-distributed noise and tested on clinical data taken from procedures at St. Thomas' hospital in London. The performance was assessed by using local SNR and by clinical voting using ten cardiologists. The results show that the UDDN outperforms the other comparative methods and is a promising solution to this challenging but clinically impactful task.

Patients undergoing Magnetic Resonance Imaging (MRI) often experience anxiety and sometimes distress prior to and during scanning. Here a full MRI compatible virtual reality (VR) system is described and tested with the aim of creating a radically different experience. Potential benefits could accrue from the strong sense of immersion that can be created with VR, which could create sense experiences designed to avoid the perception of being enclosed and could also provide new modes of diversion and interaction that could make even lengthy MRI examinations much less challenging. Most current VR systems rely on head mounted displays combined with head motion tracking to achieve and maintain a visceral sense of a tangible virtual world, but this technology and approach encourages physical motion, which would be unacceptable and could be physically incompatible for MRI. The proposed VR system uses gaze tracking to control and interact with a virtual world. MRI compatible cameras are used to allow real time eye tracking and robust gaze tracking is achieved through an adaptive calibration strategy in which each successive VR interaction initiated by the subject updates the gaze estimation model. A dedicated VR framework has been developed including a rich virtual world and gaze-controlled game content. To aid in achieving immersive experiences physical sensations, including noise, vibration and proprioception associated with patient table movements, have been made congruent with the presented virtual scene. A live video link allows subject-carer interaction, projecting a supportive presence into the virtual world., (© 2021. The Author(s).)

Objectives: The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows. Background: Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than Cardiovascular magnetic resonance imaging but is unable to reliably image scar. Methods: A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes, which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA dataset (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts. Results: Note that 84.7% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA-derived data, with no further training, where it achieved an 88.3% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians. Conclusion: Automatic ischemic scar detection can be performed from a routine cardiac CTA, without any scar-specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times, and guide clinical decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 O'Brien, Whitaker, Singh Sidhu, Gould, Kurzendorfer, O'Neill, Rajani, Grigoryan, Rinaldi, Taylor, Rhode, Mountney and Niederer.)

Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this paper presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this paper and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly- developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way.

Three-dimensional (3D) transesophageal echocardiography (TEE) is one of the most significant advances in cardiac imaging. Although TEE provides real-time 3D visualization of heart tissues and blood vessels and has no ionizing radiation, x-ray fluoroscopy still dominates in guidance of cardiac interventions due to TEE having a limited field of view and poor visualization of surgical instruments. Therefore, fusing 3D echo with live x-ray images can provide a better guidance solution. This paper proposes a novel framework for image fusion by detecting the pose of the TEE probe in x-ray images in real-time. The framework does not require any manual initialization. Instead it uses a cascade classifier to compute the position and in-plane rotation angle of the TEE probe. The remaining degrees of freedom are determined by fast marching against a template library. The proposed framework is validated on phantoms and patient data. The target registration error for the phantom was 2.1 mm. In addition, 10 patient datasets, seven of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures, were used to test the clinical feasibility as well as accuracy. A mean registration error of 2.6 mm was achieved, which is well within typical clinical requirements.

Organ phantoms are widely used for evaluating medical technologies, training clinical practitioners, as well as surgical planning. In the context of cardiovascular disease, a patient-specific cardiac phantom can play an important role for interventional cardiology procedures. However, phantoms with complicated structures are difficult to fabricate by conventional manufacturing methods. The emergence of three-dimensional (3D) printing with soft materials provides the opportunity to produce phantoms with complex geometries and realistic properties. In this work, the aim was to explore the use of a direct 3D printing technique to produce multimodal imaging cardiac phantoms and to test the physical properties of the new materials used, namely the Poro-Lay series and TangoPlus. The cardiac phantoms were first modeled using real data segmented from a patient chest computer tomography (CT) scan and then printed with the novel materials. They were then tested quantitatively in terms of stiffness and ultrasound (US) acoustic values and qualitatively with US, CT, and magnetic resonance imaging systems. From the stiffness measurements, Lay-fomm 40 had the closest Young's modulus to real myocardium with an error of 29-54%, while TangoPlus had the largest difference. From the US acoustics measurements, Lay-fomm 40 also demonstrated the closest soft tissue-mimicking properties with both the smallest attenuation and impedance differences. Furthermore, the imaging results show that the phantoms are compatible with multiple imaging modalities and thus have potential to be used for interventional procedure simulation and testing of novel interventional devices. In conclusion, direct 3D printing with Poro-Lay and TangoPlus is a promising method for manufacture of multimodal imaging phantoms with complicated structures, especially for soft patient-specific phantoms., Competing Interests: No competing financial interests exist., (© Shu Wang et al., 2020; Published by Mary Ann Liebert, Inc.)

Objective: In this work we address limitations in state-of-the-art ultrasound robots by designing and integrating a novel soft robotic system for ultrasound imaging. It employs the inherent qualities of soft fluidic actuators to establish safe, adaptable interaction between ultrasound probe and patient., Methods: We acquire clinical data to determine the movement ranges and force levels required in prenatal foetal ultrasound imaging and design the soft robotic end-effector accordingly. We verify its mechanical characteristics, derive and validate a kinetostatic model and demonstrate controllability and imaging capabilities on an ultrasound phantom., Results: The soft robot exhibits the desired stiffness characteristics and is able to reach 100% of the required workspace when no external force is present, and 95% of the workspace when considering its compliance. The model can accurately predict the end-effector pose with a mean error of 1.18±0.29 mm in position and 0.92±0.47 ° in orientation. The derived controller is, with an average position error of 0.39 mm, able to track a target pose efficiently without and with externally applied loads. Ultrasound images acquired with the system are of equally good quality compared to a manual sonographer scan., Conclusion: The system is able to withstand loads commonly applied during foetal ultrasound scans and remains controllable with a motion range similar to manual scanning., Significance: The proposed soft robot presents a safe, cost-effective solution to offloading sonographers in day-to-day scanning routines. The design and modelling paradigms are greatly generalizable and particularly suitable for designing soft robots for physical interaction tasks.

Objectives: Patients undergoing surgery for locally advanced lung cancer involving the chest wall require anatomical lung with extensive en-bloc chest wall resection and appropriate reconstruction.In this proof-of-concept study, we aimed to produce personalized three-dimensional (3D)-printed chest wall prosthesis for a patient undergoing chest wall resection and reconstruction using clinically obtained computed tomography (CT) data., Methods: Preoperative CT scans of three patients undergoing chest wall resection were analyzed and the areas of resection segmented. This was then used to produce a 3D print of the chest wall and a silicone mold was created from the model. This mold was sterilized and used to produce methyl methacrylate prostheses which were then implanted into the patients., Results: Three patients had their chest wall reconstructed using this technique to produce a patient specific prosthesis. There were no early complications or deaths., Conclusions: It is possible to use 3D printing to produce a patient specific chest wall reconstruction for patients undergoing chest wall resection for malignancy that is cost-effective. This chest wall is thought to provide stability in the form of prosthetic ribs as well compliance in the form of an expanded polytetrafluoroethylene patch. Further research is required to measure chest wall compliance during the respiratory cycle and long-term follow-up from this method., Competing Interests: There are no conflicts of interest to declare., (Georg Thieme Verlag KG Stuttgart · New York.)

Background: Using cardiovascular magnetic resonance imaging (CMR), it is possible to detect diffuse fibrosis of the left ventricle (LV) in patients with atrial fibrillation (AF), which may be independently associated with recurrence of AF after ablation. By conducting CMR, clinical, electrophysiology and biomarker assessment we planned to investigate LV myocardial fibrosis in patients undergoing AF ablation., Methods: LV fibrosis was assessed by T1 mapping in 31 patients undergoing percutaneous ablation for AF. Galectin-3, coronary sinus type I collagen C terminal telopeptide (ICTP), and type III procollagen N terminal peptide were measured with ELISA. Comparison was made between groups above and below the median for LV extracellular volume fraction (ECV), followed by regression analysis., Results: On linear regression analysis LV ECV had significant associations with invasive left atrial pressure (Beta 0.49, P = 0.008) and coronary sinus ICTP (Beta 0.75, P < 0.001), which remained significant on multivariable regression., Conclusion: LV fibrosis in patients with AF is associated with left atrial pressure and invasively measured levels of ICTP turnover biomarker.

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/)., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Ex-vivo cardiovascular magnetic resonance (CMR) imaging has played an important role in the validation of in-vivo CMR characterization of pathological processes. However, comparison between in-vivo and ex-vivo imaging remains challenging due to shape changes occurring between the two states, which may be non-uniform across the diseased heart. A novel two-step process to facilitate registration between ex-vivo and in-vivo CMR was developed and evaluated in a porcine model of chronic myocardial infarction (MI)., Methods: Seven weeks after ischemia-reperfusion MI, 12 swine underwent in-vivo CMR imaging with late gadolinium enhancement followed by ex-vivo CMR 1 week later. Five animals comprised the control group, in which ex-vivo imaging was undertaken without any support in the LV cavity, 7 animals comprised the experimental group, in which a two-step registration optimization process was undertaken. The first step involved a heart specific flexible 3D printed scaffold generated from in-vivo CMR, which was used to maintain left ventricular (LV) shape during ex-vivo imaging. In the second step, a non-rigid co-registration algorithm was applied to align in-vivo and ex-vivo data. Tissue dimension changes between in-vivo and ex-vivo imaging were compared between the experimental and control group. In the experimental group, tissue compartment volumes and thickness were compared between in-vivo and ex-vivo data before and after non-rigid registration. The effectiveness of the alignment was assessed quantitatively using the DICE similarity coefficient., Results: LV cavity volume changed more in the control group (ratio of cavity volume between ex-vivo and in-vivo imaging in control and experimental group 0.14 vs 0.56, p < 0.0001) and there was a significantly greater change in the short axis dimensions in the control group (ratio of short axis dimensions in control and experimental group 0.38 vs 0.79, p < 0.001). In the experimental group, prior to non-rigid co-registration the LV cavity contracted isotropically in the ex-vivo condition by less than 20% in each dimension. There was a significant proportional change in tissue thickness in the healthy myocardium (change = 29 ± 21%), but not in dense scar (change = - 2 ± 2%, p = 0.034). Following the non-rigid co-registration step of the process, the DICE similarity coefficients for the myocardium, LV cavity and scar were 0.93 (±0.02), 0.89 (±0.01) and 0.77 (±0.07) respectively and the myocardial tissue and LV cavity volumes had a ratio of 1.03 and 1.00 respectively., Conclusions: The pattern of the morphological changes seen between the in-vivo and the ex-vivo LV differs between scar and healthy myocardium. A 3D printed flexible scaffold based on the in-vivo shape of the LV cavity is an effective strategy to minimize morphological changes in the ex-vivo LV. The subsequent non-rigid registration step further improved the co-registration and local comparison between in-vivo and ex-vivo data.

This paper presents a novel two-axis shape sensor based on optical optoelectronic sensors and integrated with a flexible manipulator arm to measure the overall shape of the robotic arm. The disc-shape bio-compatible sensor presented here can be embedded as a sensing system into flexible manipulators and is applicable to the geometry of its structure and to the structure of any other similar multi-segment robotic manipulator. Design and calibration procedures of the device are introduced: experimental results allow defining a sensor matrix for real-time estimation of the pitch and roll of the plate above the sensor and confirms the usefulness of the proposed optical sensing approach.

Robotic systems have great potential to assist ultrasound (US) examination. Currently, the safety management method to limit the force that a US robot can apply mostly relies on force sensing and software-based algorithms. This causes the concern that the potential failure of sensors, electrical systems, or software could lead to patient injuries. In this paper, we investigated a customized spring-loaded ball clutch joint designed for a newly developed US robot to passively limit the force applied. The working mechanism of the clutch was modelled and the kinematic-based analysis was performed to understand the variation of the limited force at different postures of the robot. The triggering torque of the clutch was found to be 3928 N·mm, which results in the mean limited force 22.10 ± 1.76 N at the US probe end based on potential postures. The real measurement of the implemented design indicated that the limited force could be set between 17 and 24 N at the neutral posture depending on the preload. With the maximum preload, the mean limited force was found to be 21.98 ± 0.96 N based on 30 repeated measurements. The practically measured results meet the expectation from the theoretical calculation, and the resulting small variation has indicated a good repeatability of the clutch. Based on this evidence, it is concluded that the proposed clutch meets the design aim that it can limit the force applied within a safe range while at the same time ensuring that the required force is applied at different postures., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Objective: Electrotherapy remains the most effective direct therapy against lethal cardiac arrhythmias. When an arrhythmic event is sensed, either strong electric shocks or controlled rapid pacing is automatically applied directly to the heart via an implanted cardioverter defibrillator (ICDs). Despite their success, ICDs remain a highly non-optimal therapy: the strong shocks required for defibrillation cause significant extra-cardiac stimulation, resulting in pain and long-term tissue damage, and can also limit battery life. When used in anti-tachycardia pacing mode, ICDs are also often ineffective, as the pacing electrode can be far away from the centre of the arrhythmia, making it hard for the paced wave to interrupt and terminate it., Methods: In this paper, we present two conceptual intra-cardiac directional electrode configurations in silico based on novel arrangements of pairs of positive-negative electrodes. Both configurations have the potential to cause preferential excitation on specific regions of the heart., Results: We demonstrate how the properties of the induced field varies spatially around the electrodes and how it depends upon the specific arrangements of dipole electrode pairs. The results show that when tested within anatomically-realistic rabbit ventricular models, both electrode configurations produce strong virtual electrodes on the targeted endocardial surfaces, with weaker virtual electrodes produced elsewhere., Conclusions: The proposed electrode configurations may facilitate targeted far-field anti-tachycardia pacing and/or defibrillation, which may be useful in cases where conventional anti-tachycardia pacing fails. In addition, the conceptual electrode designs intrinsically confine the electric field to the immediate vicinity of the electrodes, and may, thus, minimize pain due to unnecessary extra-cardiac stimulation.

Background: Careful preoperative planning in thoracic surgery is essential for positive outcomes, especially in video-assisted thoracic surgery (VATS), where palpation and 3-dimensional (3D) imaging is restricted. This study evaluated the ability of different imaging techniques, such as computed tomography (CT) scanning, maximal intensity projection imaging, 3D reconstruction, and 3D printing, to define the anatomy of the hilar structures before anatomical lung resection., Methods: All patients undergoing elective lung resections by VATS for cancer under a single surgeon were identified over a 3-month period. The surgeon was asked to record the number of pulmonary artery branches supplying the lobe to be resected by using the preoperative CT scans, maximal intensity projection images, and 3D-reconstructed CT images. The lung hilum in 3 patients was printed. These were then compared with the intraoperative findings., Results: The preoperative imaging of 16 patients was analyzed. The lung hilum was printed in a further 3 patients. Although not statistically significant, the 3D prints of the hilum were the most accurate measurement, with a correlation of 0.92. CT, 3D-reconstructed CT, and maximal intensity projection images tended to underrecognize the number of arterial branches and therefore scored between 0.26 and 0.39 in absolute agreement with the number of arteries found at operation., Conclusions: 3D printing in the planning of thoracic surgery may suggest a benefit over contemporary available imaging modalities, and the use of 3D printing in practicing operations is being established., (Copyright © 2019 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Trans-esophageal echocardiography (TEE) is a miniatured intra-operative ultrasound system, widely used in routine diagnosis and interventional procedure monitoring, to assess cardiac structures and functions. As a way to assist the operation of TEE remotely, we have developed an add-on robotic system to actuate a commercial TEE probe. For the proposed robot, understanding the inverse kinematics (IK) which relates the probe pose to the joint parameters is the fundamental step towards automatic control of the system. Rather than using conventional numerical-based techniques which may have problems with speed, convergence, and stability when applying to the TEE robot, this paper explores a soft computing approach by constructing an Adaptive Neuro-Fuzzy Inference System (ANFIS) to learn from training data generated by the forward kinematics (FK) and then computing the inverse kinematics in order to control the orientation of the TEE probe. With 1900 training data over 40 epochs, the minimum training error for each joint parameter was found to be less than 0.1 degree. Validation using a separate data set has indicated that the maximum error was less than 0.3 degree for each joint parameter. It is therefore concluded that the ANFIS-based approach is an effective way, with acceptable accuracy, to compute the inverse kinematics of the TEE robot.

With the potential for high precision, dexterity, and repeatability, a self-tracked robotic system can be employed to assist the acquisition of real-time ultrasound. However, limited numbers of robots designed for extra-corporeal ultrasound have been successfully translated into clinical use. In this study, we aim to build a bespoke robotic manipulator for extra-corporeal ultrasound examination, which is lightweight and has a small footprint. The robot is formed by five specially shaped links and custom-made joint mechanisms for probe manipulation, to cover the necessary range of motion with redundant degrees of freedom to ensure the patient's safety. The mechanical safety is emphasized with a clutch mechanism, to limit the force applied to patients. As a result of the design, the total weight of the manipulator is less than 2 kg and the length of the manipulator is about 25 cm. The design has been implemented, and simulation, phantom, and volunteer studies have been performed, to validate the range of motion, the ability to make fine adjustments, mechanical reliability, and the safe operation of the clutch. This paper details the design and implementation of the bespoke robotic ultrasound manipulator, with the design and assembly methods illustrated. Testing results to demonstrate the design features and clinical experience of using the system are presented. It is concluded that the current proposed robotic manipulator meets the requirements as a bespoke system for extra-corporeal ultrasound examination and has great potential to be translated into clinical use.

Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall's thickness. Present studies have commonly measured the wall thickness at few discrete locations. Dense measurements with computer algorithms may be possible on cardiac scans of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The task is challenging as the atrial wall is a thin tissue and the imaging resolution is a limiting factor. It is unclear how accurate algorithms may get and how they compare in this new emerging area. We approached this problem of comparability with the Segmentation of Left Atrial Wall for Thickness (SLAWT) challenge organised in conjunction with MICCAI 2016 conference. This manuscript presents the algorithms that had participated and evaluation strategies for comparing them on the challenge image database that is now open-source. The image database consisted of cardiac CT (n=10) and MRI (n=10) of healthy and diseased subjects. A total of 6 algorithms were evaluated with different metrics, with 3 algorithms in each modality. Segmentation of the wall with algorithms was found to be feasible in both modalities. There was generally a lack of accuracy in the algorithms and inter-rater differences showed that algorithms could do better. Benchmarks were determined and algorithms were ranked to allow future algorithms to be ranked alongside the state-of-the-art techniques presented in this work. A mean atlas was also constructed from both modalities to illustrate the variation in thickness within this small cohort., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: Catheters and guidewires are used extensively in cardiac catheterization procedures such as heart arrhythmia treatment (ablation), angioplasty, and congenital heart disease treatment. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications, for example, motion compensation, coregistration between 2D and 3D imaging modalities, and 3D object reconstruction., Methods: For the generalized framework, a multiscale vessel enhancement filter is first used to enhance the visibility of wire-like structures in the X-ray images. After applying adaptive binarization method, the centerlines of wire-like objects were extracted. Finally, the catheters and guidewires were detected as a smooth path which is reconstructed from centerlines of target wire-like objects. In order to classify electrode catheters which are mainly used in electrophysiology procedures, additional steps were proposed. First, a blob detection method, which is embedded in vessel enhancement filter with no additional computational cost, localizes electrode positions on catheters. Then the type of electrode catheters can be recognized by detecting the number of electrodes and also the shape created by a series of electrodes. Furthermore, for detecting guiding catheters or guidewires, a localized machine learning algorithm is added into the framework to distinguish between target wire objects and other wire-like artifacts. The proposed framework were tested on total 10,624 images which are from 102 image sequences acquired from 63 clinical cases., Results: Detection errors for the coronary sinus (CS) catheter, lasso catheter ring and lasso catheter body are 0.56 ± 0.28 mm, 0.64 ± 0.36 mm, and 0.66 ± 0.32 mm, respectively, as well as success rates of 91.4%, 86.3%, and 84.8% were achieved. Detection errors for guidewires and guiding catheters are 0.62 ± 0.48 mm and success rates are 83.5%., Conclusion: The proposed computational framework do not require any user interaction or prior models and it can detect multiple catheters or guidewires simultaneously and in real-time. The accuracy of the proposed framework is sub-mm and the methods are robust toward low-dose X-ray fluoroscopic images, which are mainly used during procedures to maintain low radiation dose., (© 2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application., Methods: This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images., Results: Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was [Formula: see text] on 1000 test cases, superior to that of manual ([Formula: see text]) and gradient-based ([Formula: see text]) registration. High robustness is shown in 19 clinical CRT cases., Conclusion: Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions.

The use of implantable cardiac devices has increased in the last 30 years. Cardiac resynchronisation therapy (CRT) is a procedure which involves implanting a coin sized pacemaker for reversing heart failure. The pacemaker electrode leads are implanted into cardiac myocardial tissue. The optimal site for implantation is highly patient-specific. Most implanters use empirical placement of the lead. One region identified to have a poor response rate are myocardial tissue with transmural scar. Studies that precisely measure transmurality of scar tissue in the left ventricle (LV) are few. Most studies lack proper validation of their transmurality measurement technique. This study presents an image analysis technique for computing scar transmurality from late-gadolinium enhancement MRI. The technique is validated using phantoms under a CRT image guidance system. The study concludes that scar transmurality can be accurately measured in certain situations and validation with phantoms is important.

Congestive heart failure is associated with significant morbidity and mortality, as first line treatments are not always effective in improving symptoms and quality of life. Furthermore, 30-50% of patients who are treated with cardiac resynchronization therapy (CRT), a minimally invasive intervention, do not respond when assessed by objective criteria such as cardiac remodeling. Positioning of the left ventricular lead in the latest activating myocardial region is associated with the best outcome. Cardiac magnetic resonance (CMR) imaging can detect scar tissue and interventricular dyssynchrony; improving the outcome of CRT. However, MR is currently not standard modality for CRT due to its cost and limited availability. This paper explores a novel method to exploit interventional X-ray fluoroscopy set up in CRT procedures to gain information on mechanical activation of the myocardium by tracking the movement of vessels overlying to left ventricular myocardium. Fluoroscopic images were labelled, to track branch movement and determine the motion along the main principal component associated with cardiac motion, to optimize lead placement in CRT. A comparison between MR- and fluoroscopy-derived mechanical activation was performed on 9 datasets, showing more than 66% agreement in 8 cases.

Purpose: Cardiac resynchronisation therapy (CRT) is an established treatment for symptomatic patients with heart failure, a prolonged QRS duration, and impaired left ventricular (LV) function; however, non-response rates remain high. Recently proposed computer-assisted interventional platforms for CRT provide new routes to improving outcomes. Interventional systems must process information in an accurate, fast and highly automated way that is easy for the interventional cardiologists to use. In this paper, an interventional CRT platform is validated against two offline diagnostic tools to demonstrate that accurate information processing is possible in the time critical interventional setting., Methods: The study consisted of 3 healthy volunteers and 16 patients with heart failure and conventional criteria for CRT. Data analysis included the calculation of end-diastolic volume, end-systolic volume, stroke volume and ejection fraction; computation of global volume over the cardiac cycle as well as time to maximal contraction expressed as a percentage of the total cardiac cycle., Results: The results showed excellent correlation ([Formula: see text] values of [Formula: see text] and Pearson correlation coefficient of [Formula: see text]) with comparable offline diagnostic tools., Conclusion: Results confirm that our interventional system has good accuracy in everyday clinical practice and can be of clinical utility in identification of CRT responders and LV function assessment.

In recent years, 3D trans-oesophageal echocardiography (TOE) has become widely used for monitoring cardiac interventions. The control of the TOE probe during the procedure is a manual task which is tedious and harmful for the operator when exposed to radiation. To improve this technique, an add-on robotic system has been developed for holding and manipulating a commercial TOE probe. This paper focuses on the probe adjustment strategy in order to accurately monitor the moving intra-operative catheters. The positioning strategy is divided into an initialization step based on a pre-planning method, and a localized adjustment step based on the robotic differential kinematics. A series of experiments was performed to evaluate the initialization and the localized adjustment steps. The results indicate a mean error less than 10 mm from the phantom experiments for the initialization step, and a median error less than 1.5 mm from the computer-based simulation experiments for the localized adjustment step. Compared to the much bigger image volume, it is concluded that the proposed methods are feasible for this application. Future work will focus on evaluating the method in a more realistic TOE scanning scenario., Competing Interests: The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Aims: To test the ability of four circulating biomarkers of fibrosis, and of low left atrial voltage, to predict recurrence of atrial fibrillation after catheter ablation., Background: Circulating biomarkers potentially may be used to improve patient selection for atrial fibrillation ablation. Low voltage areas in the left atrium predict arrhythmia recurrence when mapped in sinus rhythm. This study tested type III procollagen N terminal peptide (PIIINP), galectin-3 (gal-3), fibroblast growth factor 23 (FGF-23), and type I collagen C terminal telopeptide (ICTP), and whether low voltage areas in the left atrium predicted atrial fibrillation recurrence, irrespective of the rhythm during mapping., Methods: 92 atrial fibrillation ablation patients were studied. Biomarker levels in peripheral and intra-cardiac blood were measured with enzyme-linked immunosorbent assay. Low voltage (<0.5mV) was expressed as a proportion of the mapped left atrial surface area. Follow-up was one year. The primary endpoint was recurrence of arrhythmia. The secondary endpoint was a composite of recurrence despite two procedures, or after one procedure if no second procedure was undertaken., Results: The biomarkers were not predictive of either endpoint. After multivariate Cox regression analysis, high proportion of low voltage area in the left atrium was found to predict the primary endpoint in sinus rhythm mapping (hazard ratio 4.323, 95% confidence interval 1.337-13.982, p = 0.014) and atrial fibrillation mapping (hazard ratio 5.195, 95% confidence interval 1.032-26.141, p = 0.046). This effect was also apparent for the secondary endpoint., Conclusion: The studied biomarkers do not predict arrhythmia recurrence after catheter ablation. Left atrial voltage is an independent predictor of recurrence, whether the left atrium is mapped in atrial fibrillation or sinus rhythm.

A key component of image guided interventions is the registration of preoperative and intraoperative images. Classical registration approaches rely on cross-modality information; however, in modalities such as MRI and X-ray there may not be sufficient cross-modality information. This paper proposes a fundamentally different registration approach which uses adjacent anatomical structures with superabundant vessel reconstruction and dynamic outlier rejection. In the targeted clinical scenario of cardiac resynchronization therapy (CRT) delivery, preoperative, non contrast-enhanced, MRI is registered to intraoperative, contrasted X-ray fluoroscopy. The adjacent anatomical structures are the left ventricle (LV) from MRI and the coronary veins reconstructed from two contrast-enhanced X-ray images. The novel concept of superabundant vessel reconstruction is introduced to bypass the standard reconstruction problem of establishing one-to-one correspondences. Furthermore, a new dynamic outlier rejection method is proposed, to enable globally optimal point set registration. The proposed approach has been qualitatively and quantitatively evaluated on phantom, clinical CT angiography with ground truth and clinical CRT data. A novel evaluation method is proposed for clinical CRT data based on previously implanted artificial aortic and mitral valves. The registration accuracy in 3D was 2.94 mm for the aortic and 3.86 mm for the mitral valve. The results are below the required accuracy identified by clinical partners to be the half-segment size (16.35 mm) of a standard American Heart Association (AHA) 16 segment model of the LV., (Copyright © 2017 Elsevier B.V. All rights reserved.)