The flow convergence method includes calculation of the proximal isovelocity surface area (PISA) and is widely used to classify mitral regurgitation (MR) with echocardiography. It constitutes a primary decision factor for determination of treatment and should therefore be a robust quantification method. However, it is known for its tendency to underestimate MR and its dependence on user expertise. The present work systematically compares different pulsatile flow profiles arising from different regurgitation orifices using transesophageal echocardiographic (TEE) probe and particle image velocimetry (PIV) as a reference in an in-vitro environment. It is found that the inter-observer variability using echocardiography is small compared to the systematic underestimation of the regurgitation volume for large orifice areas (up to 52%) where a violation of the flow convergence method assumptions occurs. From a flow perspective, a starting vortex was found as a dominant flow pattern in the regurgant jet for all orifice shapes and sizes. A series of simplified computational fluid dynamics (CFD) simulations indicate that selecting a suboptimal aliasing velocity during echocardiography measurements might be a primary source of potential underestimation in MR characterization via the PISA-based method, reaching up to 40%. In this study, it has been noted in clinical observations that physicians often select an aliasing velocity higher than necessary for optimal estimation in diagnostic procedures.

Mitral valve regurgitation is one of the most common heart valve diseases that can occur when the structural composition of the mitral valve is affected. Mitral valve repair, typically performed in a minimally invasive setting, is a complex surgery that aims to reinstate the structural integrity of the valve and thereby restore normal valve function. The current practice lacks quantification of geometrical changes and interactive 3D visualization that could enhance the understanding of pathomorphological changes and impact of surgical correction. This work presents functionalities from smartMVR, a quantitative tool to measure, compare, and analyse changes in mitral valve geometry from data captured on-demand using an RGB-D camera. The feasibility of the approach is demonstrated on a silicone replica of a pathological mitral valve that is iteratively corrected using common surgical techniques.

OBJECTIVES: Minimally invasive mitral valve repair (MVR) is considered one of the most challenging operations in cardiac surgery and requires much practice and experience. Simulation-based surgical training might be a method to support the learning process and help to flatten the steep learning curve of novices. The purpose of this study was to show the possible effects on learning of surgical training using a high-fidelity simulator with patient-specific mitral valve replicas. METHODS: Twenty-five participants were recruited to perform MVR on anatomically realistic valve models during different training sessions. After every session their performance was evaluated by a surgical expert regarding accuracy and duration for each step. A second blinded rater similarly assessed the performance after the study. Through repeated documentation of those parameters, their progress in learning was analysed, and gains in proficiency were evaluated. RESULTS: Participants showed significant performance enhancements in terms of both accuracy and time. Their surgical skills showed sizeable improvements after only 1 session. For example, the time to implant neo-chordae decreased by 24.64% (354 s-264 s, P < 0.001) and the time for annuloplasty by 4.01% (54 s-50 s, P = 0.165), whereas the number of irregular stitches for annuloplasty decreased from 52% to 24%. The significance of simulation-based surgical training as a tool for acquiring and training surgical skills was reviewed positively. CONCLUSIONS: The results of this study indicate that simulation-based surgical training is a valuable and effective method for learning reconstructive techniques of minimally invasive MVR and overall general dexterity. The novel learning and training options should be implemented in the surgical traineeship for systematic teaching of various surgical skills.

Purpose The goal of this study was to show possible effects of performing the actual procedure of mitral valve repair (MVR) on personalized silicone models 1 day before operation. Description Based on preoperative 3-dimensional echocardiography recordings, flexible 3-dimensional replicas of the depicted pathologic mitral valves could be produced and used for a simulation of reconstructive techniques analogous to the upcoming MVR procedure. We integrated this step of personalized surgical planning into the clinical routine of 6 MVR cases with 3 different surgeons. This pilot study was assessed by evaluating questionnaires and by comparing isolated surgical steps with conventional MVRs. Evaluation This approach was considered a better preparation for MVRs with overall positive responses from the surgeons. Simulation helped reduce the time of initial inspection of the valve because of better understanding of the valve’s pathomorphologic features. Annuloplasty benefited from preoperative sizing by reducing the number of sizing attempts. Conclusions These initial findings suggest that simulation-based surgical planning can be implemented into patients’ and physicians’ clinical workflow as a major technologic advancement for future MVR preparation.

Multi-point tracking is a challenging task that involves detecting points in the scene and tracking them across a sequence of frames. Computing detection-based measures like the F-measure on a frame-by-frame basis is not sufficient to assess the overall performance, as it does not interpret performance in the temporal domain. The main evaluation metric available comes from Multi-object tracking (MOT) methods to benchmark performance on datasets such as KITTI with the recently proposed higher order tracking accuracy (HOTA) metric, which is capable of providing a better description of the performance over metrics such as MOTA, DetA, and IDF1. While the HOTA metric takes into account temporal associations, it does not provide a tailored means to analyse the spatial associations of a dataset in a multi-camera setup. Moreover, there are differences in evaluating the detection task for points when compared to objects (point distances vs. bounding box overlap). Therefore in this work, we propose a multi-view higher order tracking metric (mvHOTA) to determine the accuracy of multi-point (multi-instance and multi-class) tracking methods, while taking into account temporal and spatial associations.mvHOTA can be interpreted as the geometric mean of detection, temporal, and spatial associations, thereby providing equal weighting to each of the factors. We demonstrate the use of this metric to evaluate the tracking performance on an endoscopic point detection dataset from a previously organised surgical data science challenge. Furthermore, we compare with other adjusted MOT metrics for this use-case, discuss the properties of mvHOTA, and show how the proposed multi-view Association and the Occlusion index (OI) facilitate analysis of methods with respect to handling of occlusions. The code is available at https://github.com/Cardio-AI/mvhota.

Minimally invasive surgeries have restricted surgical ports, demanding a high skill level from the surgeon. Surgical simulation potentially reduces this steep learning curve and additionally provides quantitative feedback. Markerless depth sensors show great promise for quantification, but most such sensors are not designed for accurate reconstruction of complex anatomical forms in close-range. This work compares three commercially available depth sensors, namely the Intel D405, D415, and the Stereolabs Zed-Mini in the range of 12–20 cm, for use in surgical simulation. Three environments are designed that closely mimic surgical simulation, comprising planar surfaces, rigid objects, and mitral valve models of silicone and realistic porcine tissue. The cameras are evaluated on Z-accuracy, temporal noise, fill rate, checker distance, point cloud comparisons, and visual inspection of surgical scenes, across several camera settings. The Intel cameras show sub-mm accuracy in most static environments. The D415 fails in reconstructing valve models, while the Zed-Mini provides lesser temporal noise and higher fill rate. The D405 could reconstruct anatomical structures like the mitral valve leaflet and a ring prosthesis, but performs poorly for reflective surfaces like surgical tools and thin structures like sutures. If a high temporal resolution is needed and lower spatial resolution is acceptable, the Zed-Mini is the best choice, whereas the Intel D405 is the most suited for close-range applications. The D405 shows potential for applications like deformable registration of surfaces, but is not yet suitable for applications like real-time tool tracking or surgical skill assessment.

Purpose Minimally invasive mitral valve surgery (MIMVS) and transcatheter edge-to-edge repair (TEER) are complex procedures used to treat mitral valve (MV) pathologies, but with limited training opportunities available. To enable training, a realistic hemodynamic environment is needed. In this work we aimed to develop and validate a simulator that enables investigation of MV pathologies and their repair by MIMVS and TEER in a hemodynamic setting. Methods Different MVs were installed in the simulator, and pressure, flow, and transesophageal echocardiographic measurements were obtained. To confirm the simulator’s physiological range, we first installed a biological prosthetic, a mechanical prosthetic, and a competent excised porcine MV. Subsequently, we inserted two porcine MVs—one with induced chordae tendineae rupture and the other with a dilated annulus, along with a patient-specific silicone valve extracted from echocardiography with bi-leaflet prolapse. Finally, TEER and MIMVS procedures were conducted by experts to repair the MVs. Results Systolic pressures, cardiac outputs, and regurgitations volumes (RVol) with competent MVs were 119 ± 1 mmHg, 4.78 ± 0.16 l min −1 , and 5 ± 3 ml respectively, and thus within the physiological range. In contrast, the pathological MVs displayed increased RVols. MIMVS and TEER resulted in a decrease in RVols and mitigated the severity of mitral regurgitation. Conclusion Ex-vivo modelling of MV pathologies and repair procedures using the described simulator realistically replicated physiological in-vivo conditions. Furthermore, we showed the feasibility of performing MIMVS and TEER at the simulator, also at patient-specific level, thus providing new clinical perspectives in terms of training modalities and personalized planning.

BACKGROUND:Transvenous laser lead extraction (TLE) for cardiac implantable electric devices (CIED) is a challenging procedure especially if performed in octogenarians. In this study we evaluated the safety and efficacy of transvenous laser lead extraction in elderly patients.METHODS:This is a retrospective study of octogenarian patients who underwent laser-assisted lead extraction (LLE) (GlideLight laser sheath, Philips, San Diego, USA). 270 Consecutive patients were included. Patients were divided into two groups. Octogenarian group and non-octogenarian group. The Data was gathered from patients treated between September 2013 and January 2020 and is retrospectively analyzed.RESULTS:Of 270 consecutive patients, 38 (14.0%) were 80 years old or more. The total number of the extracted leads was 556 among which 84(15.0%) from the Octogenarian group. From these leads were 155 single coil leads, 82 dual coil leads, 129 right ventricular pacing leads, 155 right atrial leads, and 35 left ventricular leads. In the Octogenarian group the number of removed leads was as follows: 13 single coil leads, 10 dual coil leads, 28 right ventricular pacing leads, 28 right atrial leads and 5 left ventricular leads. No mortality was recorded in the Octogenarian group. One patient in the YG suffered from a superior vena cava tear and one patient suffered from pulmonary embolism.CONCLUSION:In octogenarian laser assisted lead extraction patients is a safe and effective procedure. No increase in morbidity, mortality or perioperative complication could be recorded in this group. Age should not be a limiting factor to perform this procedure.

INTRODUCTION:Laser lead extraction is a well-established method for removing unwanted leads with low morbidity and mortality. In this small series of cases, we documented our experience with venous thrombosis after laser lead extraction.METHODS:Retrospective data of patients who underwent laser lead extraction with postoperative axillo-subclavian vein thrombosis between May 2010 and January 2020 were analyzed. Demographic, operative, clinical, and follow-up characteristics of those patients were collected from our medical database.RESULTS:Six patients underwent percutaneous laser lead extraction. Mean age of the patients was 64±7 years. And four of them were male. A total of 11 leads with a mean age of 92±43.8 months were extracted. Patients presented with painful arm swelling postoperatively.CONCLUSION:Laser lead extraction may lead to symptomatic upper extremity deep venous occlusion.

Background Dextrocardia is a congenital anomaly in which the apex of the heart is abnormally located on the right side of the chest. Situs solitus describes viscera that are in the normal position, with the stomach on the left side. In these patients, implantation of transvenous implantable cardioverter-defibrillator (ICD) can be limited by anatomical abnormalities commonly associated with this condition. Case summary We present the case of a young female patient with absent right atrioventricular connection, morphologically left systemic ventricle, muscular restrictive ventricular septal defect, and dextrocardia with situs solitus who was indicated for secondary prophylactic ICD implantation after resuscitation for polymorphic ventricular tachycardia. Due to a bilateral bidirectional Glenn anastomosis, transvenous access via the vena cava superior to the right ventricle could not be achieved. For this reason, we successfully implanted a subcutaneous ICD (S-ICD) with an individually optimized right parasternal electrode position. Potential complications of epimyocardial implantation via re-thoracotomy could thus be circumvented. Discussion In patients with complex congenital heart disease, the S-ICD is an effective method of preventing sudden cardiac death. Our case report demonstrates the feasibility of left S-ICD implantation even in the presence of dextrocardia with situs solitus.

The CycleGAN framework allows for unsupervised image-to-image translation of unpaired data. In a scenario of surgical training on a physical surgical simulator, this method can be used to transform endoscopic images of phantoms into images which more closely resemble the intra-operative appearance of the same surgical target structure. This can be viewed as a novel augmented reality approach, which we coined Hyperrealism in previous work. In this use case, it is of paramount importance to display objects like needles, sutures or instruments consistent in both domains while altering the style to a more tissue-like appearance. Segmentation of these objects would allow for a direct transfer, however, contouring of these, partly tiny and thin foreground objects is cumbersome and perhaps inaccurate. Instead, we propose to use landmark detection on the points when sutures pass into the tissue. This objective is directly incorporated into a CycleGAN framework by treating the performance of pre-trained detector models as an additional optimization goal. We show that a task defined on these sparse landmark labels improves consistency of synthesis by the generator network in both domains. Comparing a baseline CycleGAN architecture to our proposed extension (DetCycleGAN), mean precision (PPV) improved by +61.32, mean sensitivity (TPR) by +37.91, and mean F1 score by +0.4743. Furthermore, it could be shown that by dataset fusion, generated intra-operative images can be leveraged as additional training data for the detection network itself.

To support minimally-invasive intraoperative mitral valve repair, quantitative measurements from the valve can be obtained using an infra-red tracked stylus. It is desirable to view such manually measured points together with the endoscopic image for further assistance. Therefore, hand-eye calibration is required that links both coordinate systems and is a prerequisite to project the points onto the image plane. A complementary approach to this is to use a vision-based endoscopic stereo-setup to detect and triangulate points of interest, to obtain the 3D coordinates. In this paper, we aim to compare both approaches on a rigid phantom and two patient-individual silicone replica which resemble the intraoperative scenario. The preliminary results indicate that 3D landmark estimation, either labeled manually or through partly automated detection with a deep learning approach, provides more accurate triangulated depth measurements when performed with a tailored image-based method than with stylus measurements.

Mitral valve repair is a surgery to restore the function of the mitral valve. To achieve this, a prosthetic ring is sewed onto the mitral annulus. Analyzing the sutures, which are punctured through the annulus for ring implantation, can be useful in surgical skill assessment, for quantitative surgery and for positioning a virtual prosthetic ring model in the scene via augmented reality. This work presents a neural network approach which detects the sutures in endoscopic images of mitral valve repair and therefore solves a landmark detection problem with varying amount of landmarks, as opposed to most other existing deep learning-based landmark detection approaches. The neural network is trained separately on two data collections from different domains with the same architecture and hyperparameter settings. The datasets consist of more than 1,300 stereo frame pairs each, with a total over 60,000 annotated landmarks. The proposed heatmap-based neural network achieves a mean positive predictive value (PPV) of 66.68$\pm$4.67% and a mean true positive rate (TPR) of 24.45$\pm$5.06% on the intraoperative test dataset and a mean PPV of 81.50\pm5.77\% and a mean TPR of 61.60$\pm$6.11% on a dataset recorded during surgical simulation. The best detection results are achieved when the camera is positioned above the mitral valve with good illumination. A detection from a sideward view is also possible if the mitral valve is well perceptible.

Abstract Background Lead laser extraction is a well-established method for removing unwanted leads with low morbidity and mortality. Objective In this observational study, we documented our experience with venous occlusion after lead laser extraction. Methods Retrospective data of patients who underwent lead laser extraction between May 2010 and August 2018 was analyzed. Two subgroups of patients were identified. First group represented patients after lead laser extraction who suffered postoperative venous occlusion. Second group represents patients after lead laser extraction, who has documented patent venous access after lead laser extraction. Results 219 patients underwent percutaneous laser lead extraction. The mean age of patients was 65 ± 14 years. Of these patients, 74% were male. The Most common indication for extraction was Nonfunctional lead (45.2%, n = 99) followed by pocket infection with 33.8% and endocarditis (17.3%). A total number of 447 leads underwent laser extraction. In 7.8% of the patients, lead extraction was partially successful and lead extraction was not successful in only 2.3% of the patients. Only 13 patients developed a documented venous occlusion postoperatively and 26 patients has documented absence of venous occlusion, of whom 17 were under oral anticoagulation. Conclusion Lead laser extraction may lead to venous occlusion, which is mostly asymptomatic but it prevents future lead implantation. The use of oral anticoagulant may prevent postoperative venous occlusion.

Background The implantation of cardiac implantable electronic devices (CIED) has increased in the last decades with improvement in the quality of life of patients with cardiac rhythm disorders. The presence of bilateral subclavian, innominate or superior vena cava obstruction is a major limitation to device revision and/or upgrade. Methods and material This is retrospective study of patients who underwent laser-assisted lead extraction (LLE) (GlideLight laser sheath, Spectranetics Corporation, Colorado Springs, USA) with lead revision or upgrade using the laser sheath as a guide rail. Patients with known occlusion, severe stenosis or functional obstruction of the venous access vessels with indwelling leads were included in this study. Results 106 patients underwent percutaneous LLE with lead revision and/or upgrade. Preoperative known complete occlusion or severe stenosis of access veins was present in 23 patients (21.5%). More patients with implantable cardioverter-defibrillator (ICD) underwent LLE (64.1%) than patients with CRT-Ds (24.5%) and pacemaker patients (11.3%). In total 172 leads were extracted: 79 (45.9%) single-coil defibrillator leads, 35 (20.3%) dual-coil defibrillator leads, 31 (18.0%) right atrial leads, 24 (13.9%) right ventricular leads and three (1.7%) malfunctional coronary sinus left ventricular pacing leads. The mean age of leads was 99.2±65.6 months. The implantation of new leads after crossing the venous stenosis/obstruction was successful in 98 (92.4%) cases. Postoperative complications were pocket hematoma in two cases and wound infection in one case. No peri-operative and no immediate postoperative death was recorded. One intraoperative superior vena cava tear was treated by immediate thoracotomy and surgical repair. Conclusion In a single-center study on LLE in the presence of supra-cardiac occlusion of the central veins for CIED lead upgrade and revision we could demonstrate a low procedural complication rate with no procedural deaths. Most of the leads could be completely extracted to revise or upgrade the system. Our study showed a low complication rate, with acceptable mortality rates.

Purpose: Mitral valve repair is a complex minimally invasive surgery of the heart valve. In this context, suture detection from endoscopic images is a highly relevant task that provides quantitative information to analyse suturing patterns, assess prosthetic configurations and produce augmented reality visualisations. Facial or anatomical landmark detection tasks typically contain a fixed number of landmarks, and use regression or fixed heatmap-based approaches to localize the landmarks. However in endoscopy, there are a varying number of sutures in every image, and the sutures may occur at any location in the annulus, as they are not semantically unique. Method: In this work, we formulate the suture detection task as a multi-instance deep heatmap regression problem, to identify entry and exit points of sutures. We extend our previous work, and introduce the novel use of a 2D Gaussian layer followed by a differentiable 2D spatial Soft-Argmax layer to function as a local non-maximum suppression. Results: We present extensive experiments with multiple heatmap distribution functions and two variants of the proposed model. In the intra-operative domain, Variant 1 showed a mean F-1 of +0.0422 over the baseline. Similarly, in the simulator domain, Variant 1 showed a mean F-1 of +0.0865 over the baseline. Conclusion: The proposed model shows an improvement over the baseline in the intra-operative and the simulator domains. The data is made publicly available within the scope of the MICCAI AdaptOR2021 Challenge https://adaptor2021.github.io/, and the code at https://github.com/Cardio-AI/suture-detection-pytorch/.

Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are based on deep image-to-image transformation methods. Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences.

In endoscopy, depth estimation is a task that potentially helps in quantifying visual information for better scene understanding. A plethora of depth estimation algorithms have been proposed in the computer vision community. The endoscopic domain however, differs from the typical depth estimation scenario due to differences in the setup and nature of the scene. Furthermore, it is unfeasible to obtain ground truth depth information owing to an unsuitable detection range of off-the-shelf depth sensors and difficulties in setting up a depth-sensor in a surgical environment. In this paper, an existing self-supervised approach, called Monodepth [1], from the field of autonomous driving is applied to a novel dataset of stereo-endoscopic images from reconstructive mitral valve surgery. While it is already known that endoscopic scenes are more challenging than outdoor driving scenes, the paper performs experiments to quantify the comparison, and describe the domain gap and challenges involved in the transfer of these methods.

Purpose Intensive planning and analysis from echocardiography are a crucial step before reconstructive surgeries are applied to malfunctioning mitral valves. Volume visualizations of echocardiographic data are often used in clinical routine. However, they lack a clear visualization of the crucial factors for decision making. Methods We build upon patient-specific mitral valve surface models segmented from echocardiography that represent the valve’s geometry, but suffer from self-occlusions due to complex 3D shape. We transfer these to 2D maps by unfolding their geometry, resulting in a novel 2D representation that maintains anatomical resemblance to the 3D geometry. It can be visualized together with color mappings and presented to physicians to diagnose the pathology in one gaze without the need for further scene interaction. Furthermore, it facilitates the computation of a Pathology Score , which can be used for diagnosis support. Results Quality and effectiveness of the proposed methods were evaluated through a user survey conducted with domain experts. We assessed pathology detection accuracy using 3D valve models in comparison with the novel visualizations. Classification accuracy increased by 5.3% across all tested valves and by 10.0% for prolapsed valves. Further, the participants’ understanding of the relation between 3D and 2D views was evaluated. The Pathology Score is found to have potential to support discriminating pathologic valves from normal valves. Conclusions In summary, our survey shows that pathology detection can be improved in comparison with simple 3D surface visualizations of the mitral valve. The correspondence between the 2D and 3D representations is comprehensible, and color-coded pathophysiological magnitudes further support the clinical assessment.