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Chronic thromboembolic pulmonary hypertension may be cured by pulmonary endarterectomy (PEA). Thromboembolic disease distribution/PEA success primarily determines prognosis but risk scoring criteria may be adjunctive. Right ventriculoarterial (RV‐PA) and ventriculoatrial (RV‐right atrium [RA]) coupling may be evaluated by cardiac MRI (CMR) feature tracking deformation/strain assessment. We characterized biatrial and biventricular CMR feature tracking (FT) strain parameters following PEA and tested the ability of CMR FT to identify REVEAL 2.0 high‐risk status. We undertook a retrospective single‐center cross‐sectional study of patients (n = 57) who underwent PEA (2015–2020). All underwent pre and postoperative catheterization and CMR. Pulmonary arterial hypertension validated risk scores were calculated. Significant postoperative improvements were observed in mean pulmonary artery pressure (mPAP) (pre‐op 45 ± 11 mmHg vs. post‐op 26 ± 11 mmHg; p < 0.001) and PVR however a large proportion had residual pulmonary hypertension (45%; mPAP ≥25 mmHg). PEA augmented left heart filling with left ventricular end diastolic volume index and left atrial volume index increment. Left ventricular ejection fraction was unchanged postoperatively but LV global longitudinal strain improved (pre‐op median −14.2% vs. post‐op −16.0%; p < 0.001). Right ventricular (RV) geometry and function also improved with reduction in RV mass. Most had uncoupled RV‐PA relationships which recovered (pre‐op right ventricular free wall longitudinal strain −13.2 ± 4.8%, RV stroke volume/right ventricular end systolic volume ratio 0.78 ± 0.53 vs. post‐op −16.8 ± 4.2%, 1.32 ± 0.55; both p < 0.001). Postoperatively, there were six REVEAL 2.0 high‐risk patients, best predicted by impaired RA strain which was superior to traditional volumetric parameters (area under the curve [AUC] 0.99 vs. RVEF AUC 0.88). CMR deformation/strain evaluation can offer insights into coupling recovery; RA strain may be an expeditious surrogate for the more laborious REVEAL 2.0 score.

Abstract Aims Various strain parameters and multiple imaging techniques are presently available including cardiovascular magnetic resonance (CMR) tagging (CMR-TAG), CMR feature tracking (CMR-FT), and speckle tracking echocardiography (STE). This study aims to compare predictive performance of different strain parameters and evaluate results per imaging technique to predict cardiac resynchronization therapy (CRT) response. Methods and results Twenty-seven patients were prospectively enrolled and underwent CMR and echocardiographic examination before CRT implantation. Strain analysis was performed in circumferential (CMR-TAG, CMR-FT, and STE-circ) and longitudinal (STE-long) orientations. Regional strain values, parameters of dyssynchrony, and discoordination were calculated. After 12 months, CRT response was measured by the echocardiographic change in left ventricular (LV) end-systolic volume (LVESV). Twenty-six patients completed follow-up; mean LVESV change was −29 ± 27% with 17 (65%) patients showing ≥15% LVESV reduction. Measures of dyssynchrony (SD-TTPLV) and discoordination (ISFLV) were strongly related to CRT response when using CMR-TAG (R2 0.61 and R2 0.57, respectively), but showed poor correlations for CMR-FT and STE (all R2 ≤ 0.32). In contrast, the end-systolic septal strain (ESSsep) parameter showed a consistent high correlation with LVESV change for all techniques (CMR-TAG R2 0.60; CMR-FT R2 0.50; STE-circ R2 0.43; and STE-long R2 0.43). After adjustment for QRS duration and QRS morphology, ESSsep remained an independent predictor of response per technique. Conclusions End-systolic septal strain was the only parameter with a consistent good relation to reverse remodelling after CRT, irrespective of assessment technique. In clinical practice, this measure can be obtained by any available strain imaging technique and provides predictive value on top of current guideline criteria.

We present EKLT, a feature tracking method that leverages the complementarity of event cameras and standard cameras to track visual features with high temporal resolution. Event cameras are novel sensors that output pixel-level brightness changes, called “events”. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the same scene pattern can produce different events depending on the motion direction, establishing event correspondences across time is challenging. By contrast, standard cameras provide intensity measurements (frames) that do not depend on motion direction. Our method extracts features on frames and subsequently tracks them asynchronously using events, thereby exploiting the best of both types of data: the frames provide a photometric representation that does not depend on motion direction and the events provide updates with high temporal resolution. In contrast to previous works, which are based on heuristics, this is the first principled method that uses intensity measurements directly, based on a generative event model within a maximum-likelihood framework. As a result, our method produces feature tracks that are more accurate than the state of the art, across a wide variety of scenes.

Research in the field of medical image is an important part of the medical robot to operate human organs. A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results. In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed. This method is described based on the triangular matching algorithm. First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm. The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set. After the training, the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame. From this matrix, the inter-frame feature matching results can be obtained. In this way, the inter-frame speckle tracking is completed. On this basis, the results of this method are compared with the matching results based on the convolutional neural network. The experimental results show that the proposed method has higher matching accuracy. In particular, the accuracy of the MNIST handwritten data set has reached more than 90%.

The left atrium (LA) has a crucial function in maintaining left ventricular filling, which is responsible for about one-third of all cardiac filling. A growing body of evidence shows that LA is involved in several cardiovascular diseases from a clinical and prognostic standpoint. LA enlargement has been recognized as a predictor of the outcomes of many diseases. However, LA enlargement itself does not explain the whole LA’s function during the cardiac cycle. For this reason, the recently proposed assessment of atrial strain at advanced cardiac magnetic resonance (CMR) enables the usual limitations of the sole LA volumetric measurement to be overcome. Moreover, the left atrial strain impairment might allow several cardiovascular diseases to be detected at an earlier stage. While traditional CMR has a central role in assessing LA volume and, through cine sequences, a marginal role in evaluating LA function, feature tracking at advanced CMR (CMR-FT) has been increasingly confirmed as a feasible and reproducible technique for assessing LA function through strain. In comparison to atrial function evaluations via speckle tracking echocardiography, CMR-FT has a higher spatial resolution, larger field of view, and better reproducibility. In this literature review on atrial strain analysis, we describe the strengths, limitations, recent applications, and promising developments of studying atrial function using CMR-FT in clinical practice. Key Points • The left atrium has a crucial function in maintaining left ventricular filling; left atrial size has been recognized as a predictor of the outcomes of many diseases . • Left atrial strain has been confirmed as a marker of atrial functional status and demonstrated to be a sensitive tool in the subclinical phase of a disease . • A comprehensive evaluation of the three phases of atrial function by CMR-FT demonstrates an impairment before the onset of atrial enlargement, thus helping clinicians in their decision-making and improving patient outcomes .

Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts.

The objective assessments of left ventricular (LV) and right ventricular (RV) ejection fractions (EFs) are the main important tasks of routine cardiovascular magnetic resonance (CMR). Over the years, CMR has emerged as the reference standard for the evaluation of biventricular morphology and function. However, changes in EF may occur in the late stages of the majority of cardiac diseases, and being a measure of global function, it has limited sensitivity for identifying regional myocardial impairment. On the other hand, current wall motion evaluation is done on a subjective basis and subjective, qualitative analysis has a substantial error rate. In an attempt to better quantify global and regional LV function; several techniques, to assess myocardial deformation, have been developed, over the past years. The aim of this review is to provide a comprehensive compendium of all the CMR techniques to assess myocardial deformation parameters as well as the application in different clinical scenarios.

Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

The unprecedented nature of the Deepwater Horizon oil spill required the application of research methods to estimate the rate at which oil was escaping from the well in the deep sea, its disposition after it entered the ocean, and total reservoir depletion. Here, we review what advances were made in scientific understanding of quantification of flow rates during deep sea oil well blowouts. We assess the degree to which a consensus was reached on the flow rate of the well by comparing in situ observations of the leaking well with a time-dependent flow rate model derived from pressure readings taken after the Macondo well was shut in for the well integrity test. Model simulations also proved valuable for predicting the effect of partial deployment of the blowout preventer rams on flow rate. Taken together, the scientific analyses support flow rates in the range of ∼50,000–70,000 barrels/d, perhaps modestly decreasing over the duration of the oil spill, for a total release of ∼5.0 million barrels of oil, not accounting for BP's collection effort. By quantifying the amount of oil at different locations (wellhead, ocean surface, and atmosphere), we conclude that just over 2 million barrels of oil (after accounting for containment) and all of the released methane remained in the deep sea. By better understanding the fate of the hydrocarbons, the total discharge can be partitioned into separate components that pose threats to deep sea vs. coastal ecosystems, allowing responders in future events to scale their actions accordingly.

Summary Recent reports on America's infrastructure have emphasized the importance of structural health monitoring of civil infrastructures. System identification is a key component of many structural health monitoring strategies. Current system identification methods estimate models of a structure by measuring displacements, accelerations, and strains with wired or wireless sensors. However, these methods typically involve installation of a limited number of sensors at discrete locations and require additional data acquisition devices. To overcome these limitations, computer vision‐based techniques have been introduced recently that employ high‐speed and high‐resolution cameras. Such cameras can be quite costly and require tedious installation of targets. This paper investigates the potential of using consumer‐grade cameras for structural system identification without the need to install targets. The underlying methods for target‐free displacement measurements are introduced, including region of interest selection, feature detection, point tracking, and outlier removal. A set of experiments are conducted to assess the efficacy of the proposed approach by comparing the accuracy of the identified model with one obtained using a conventional wired system. Careful comparison of the results demonstrates the significant potential of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.