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Purpose Tissue deformation recovery is to reconstruct the change in shape and surface strain caused by tool-tissue interaction or respiration, which is essential for providing motion and shape information that benefits the improvement of the safety of minimally invasive surgery. The binocular vision-based approach is a practical candidate for deformation recovery as no extra devices are required. However, previous methods suffer from limitations such as the reliance on biomechanical priors and the vulnerability to the occlusion caused by surgical instruments. To address the issues, we propose a deformation recovery method incorporating mesh structures and scene flow. Methods The method can be divided into three modules. The first one is the implementation of the two-step scene flow generation module to extract the 3D motion from the binocular sequence. Second, we propose a strain-based filtering method to denoise the original scene flow. Third, a mesh optimization model is proposed that strengthens the robustness to occlusion by employing contextual connectivity. Results In a phantom and an in vivo experiment, the feasibility of the method in recovering surface deformation in the presence of tool-induced occlusion was demonstrated. Surface reconstruction accuracy was quantitatively evaluated by comparing the recovered mesh surface with the 3D scanned model in the phantom experiment. Results show that the overall error is 0.70 ± 0.55 mm. Conclusion The method has been demonstrated to be capable of continuously recovering surface deformation using mesh representation with robustness to the occlusion caused by surgical forceps and promises to be suitable for the application in actual surgery.

We propose an in-silico deep reinforcement learning scheme that combines a computer simulation model of cardiac tissue and deep reinforcement learning to unveil an effective ablation strategy for atrial fibrillation (AF). The deep neural network-based ablation model (DAM) was designed to input a membrane potential movie and output an ablation pattern. As a virtual environment for in-silico learning, a numerical 2D cardiac tissue model was used. After training, the trained DAM was compared with two different strategies: the random ablation strategy (RND) and the rotor ablation strategy (ROT). We demonstrated the ablation area percentage of RND, ROT, and the best learned DAM was 7.0 ± 2.8%, 12.5 ± 5.7%, 6.5 ± 2.4%, and the AF termination rate was 12.6%, 8.5%, 74.1%, respectively. Results suggest that the DAM learned to effectively terminate spiral excitations by ablating the area from near the spiral center towards the tissue boundary without any prior knowledge. The learned DAM achieved a better AF termination rate compared to the other ablation strategies. This study showed feasibility of in-silico learning for an effective ablation strategy. To the best of our knowledge, this is the first approach to optimize spatial ablation patterns by deep learning. The proposed learning method has possibility to contribute to establishing an effective ablation strategy for more complex excitations for which conventional heuristic ablation is ineffective.

Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and body. Flexible probes, which can deform and effectively adhere to the body, are a promising technology for long-term monitoring applications. However, owing to the flexible element array geometry, the reconstructed image becomes blurred and distorted. In this study, we propose a flexible probe U.S. imaging method based on element array geometry estimation from radio frequency (RF) data using a deep neural network (DNN). The input and output of the DNN are the RF data and parameters that determine the element array geometry, respectively. The DNN was first trained from scratch with simulation data and then fine-tuned with in vivo data. The DNN performance was evaluated according to the element position mean absolute error (MAE) and the reconstructed image quality. The reconstructed image quality was evaluated with peak-signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM). In the test conducted with simulation data, the average element position MAE was 0.86 mm, and the average reconstructed image PSNR and MSSIM were 20.6 and 0.791, respectively. In the test conducted with in vivo data, the average element position MAE was 1.11 mm, and the average reconstructed image PSNR and MSSIM were 19.4 and 0.798, respectively. The average estimation time was 0.045 s. These results demonstrate the feasibility of the proposed method for long-term real-time monitoring using flexible probes.

The visualization of motion is important in the diagnosis and treatment of aortic valve disease. It is difficult to perform using computed tomography (CT) because of motion blur. Existing research focuses on suppressing or removing motion blur. The purpose of this study is to prove the feasibility of inferring motion images using motion information from a motion-blurred CT image. An in silico learning method is proposed, to infer 60 motion images from a two-dimensional (2D) motion-blurred CT image, to verify the concept. A dataset of motion-blurred CT images and motion images was generated using motion and CT simulators to train a deep neural network. The trained model was evaluated using two image similarity evaluation metrics, a structural similarity index measure (0.97 ± 0.01), and a peak signal-to-noise ratio (36.0 ± 1.3 dB), as well as three motion feature evaluation metrics, maximum opening distance error between endpoints (0.7 ± 0.6 mm), maximum-swept area velocity error between adjacent images (393.3 ± 423.3 mm2/s), and opening time error (5.5 ± 5.5 ms). According to the results, the trained model can successfully infer 60 motion images from a motion-blurred CT image. This study demonstrates the feasibility of inferring motion images from a motion-blurred CT image under simulated conditions.

A flexible ultrasonic array transducer able to be attached to the body has the potential to achieve long-term continuous unconstrained ultrasound (US) imaging. However, the quality of reconstructed US images is affected by the accuracy of the estimated array shape because the array shape is primitive for time-delay calculation in delay-and-sum beamforming. In this study, we propose an algorithm for estimating the array shape only from the backscattered US signal without using any external device. The proposed algorithm is based on the assumption that beam-summed images reconstructed using an array shape estimated at higher accuracy would have smaller entropy. The array shape is estimated by searching for the shape with minimal entropy, which was used as the index of the beam-summed image quality. Simulation experiments and phantom experiments were used to evaluate the proposed algorithm. In the simulation experiments, three different array shapes with 2.0 MHz, 20 elements, and 0.8-mm pitch transducers were estimated. In the phantom experiments, the array shapes of commercially available linear, convex, and concave transducers were estimated. The results showed that the proposed algorithm can estimate the correct array shape with an average element position error of less than one-eighth of the wavelength of the transmitted signals. These results indicate that the proposed algorithm can achieve sufficiently accurate shape estimation and has the potential to enable clear US imaging with flexible array transducers.

Purpose We hypothesized that linear regional cooling (LRC) toward the atrio-ventricular groove (AV-G) can move the spiral wave (SW) center to the AV-G effectively and terminate SW. The effectiveness of LRC in ex vivo 2D ventricle rabbit experiments was tested. Methods We developed an experimental system to operate LRC and optical mapping simultaneously. To realize simultaneous cooling and optical mapping, a transparent cooling device was developed. LRC for 60 s toward 2D subepicardial ventricular myocardium of Langendorff-perfused rabbit hearts (n = 4) was conducted during constant pacing and persistent ventricular tachyarrhythmias (VTs). Results Action potential duration at 90% repolarization (APD90) at the cooling area was prolonged by LRC from 187 to 228 ms. 41% of persistent VTs were terminated by LRC (12/29 cases). Cases where the original SW center moved toward the AV-G were observed via optical mapping. However, there were some cases where VT was not terminated by LRC. When the action potential duration (APD) of VT sustained cases were analyzed, LRC prolonged APD, but the APD prolonged area did not move toward the AV-G in most VT sustained cases Conclusion Proper LRC toward the AV-G near the original SW center could move this center toward the AV-G and terminate SW excitation.

Background Electrical storm (ES) is a life-threatening emergency in patients at high risk of ventricular tachycardia/ventricular fibrillation (VF), but the pathophysiology and molecular basis are poorly understood. Objective The purpose of this study was to explore the electrophysiological substrate for experimental ES. Methods A model was created by inducing chronic complete atrioventricular block in defibrillator-implanted rabbits, which recapitulates QT prolongation, torsades des pointes (TdP), and VF episodes. Results Optical mapping revealed island-like regions with action potential duration (APD) prolongation in the left ventricle, leading to increased spatial APD dispersion, in rabbits with ES (defined as ≥3 VF episodes/24 h). The maximum APD and its dispersion correlated with the total number of VF episodes in vivo. TdP was initiated by an ectopic beat that failed to enter the island and formed a reentrant wave and perpetuated by rotors whose centers swirled in the periphery of the island. Epinephrine exacerbated the island by prolonging APD and enhancing APD dispersion, which was less evident after late Na+ current blockade with 10 μM ranolazine. Nonsustained ventricular tachycardia in a non-ES rabbit heart with homogeneous APD prolongation resulted from multiple foci with an electrocardiographic morphology different from TdP driven by drifting rotors in ES rabbit hearts. The neuronal Na+-channel subunit NaV1.8 was upregulated in ES rabbit left ventricular tissues and expressed within the myocardium corresponding to the island location in optically mapped ES rabbit hearts. The NaV1.8 blocker A-803467 (10 mg/kg, intravenously) attenuated QT prolongation and suppressed epinephrine-evoked TdP. Conclusion A tissue island with enhanced refractoriness contributes to the generation of drifting rotors that underlies ES in this model. NaV1.8-mediated late Na+ current merits further investigation as a contributor to the substrate for ES.

Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arrival time and the actual arrival time of the echo signal, which will result in phase aberration, leading to the quality degradation of the reconstructed B-mode image. The conventional correction methods that use the correlation of each different channel require the presence of strong point scatterers and involve the problem of local solutions due to excessive correction. In this study, we propose a novel approach to correcting the signal distortion due to sound speed heterogeneity using a deep neural network (DNN). The DNN was trained to convert the distorted radio frequency (RF) inputs for the heterogeneous medium to the distortion-free RF outputs for the homogeneous medium. The network with U-net architecture using ResNet-34 as a backbone was trained using the hetero-homo corresponding channel-domain RF data generated via numerical simulations. The trained network performed phase aberration correction in the channel-domain RF, with the B-mode images reconstructed with the corrected RF demonstrating a higher contrast and an improved resolution compared with uncorrected cases. It was also demonstrated that the DNN model is robust to both varied reflection intensities and varied sound speed heterogeneities. The successful results demonstrated that the proposed DNN-based method is effective for phase aberration correction in US imaging.

Laparoscopic vision-based ultrasound probe tracking systems have gained considerable attention in ultrasound-guided laparoscopic surgeries as replacements for external tracking systems (e.g. optical tracking and electromagnetic tracking systems), which increase cost and setting time, require additional operation space, and introduce new limitations. Most existing laparoscopic ultrasound (LUS) probe tracking systems rely on fiducial markers, which cannot easily realise fast and robust vision-based tracking in laparoscopic surgery owing to their design limitations. Therefore, we propose a novel binary dot array marker to realise a robust and fast LUS probe tracking system. The binary dot array marker comprises two dots (green and blue), which form multiple unique identification dot subarrays in the binary dot array. The binary dot array marker can be tracked when one of the identification dot subarrays is detected and identified; this novel design makes the binary dot array marker-based probe tracking system robust against occlusions during surgery. The evaluation results indicate that the proposed binary dot marker performs better in terms of robustness, computational efficiency, and tracking accuracy compared to the state-of-the-art fiducial markers used for vision-based probe tracking.

Cardiac arrhythmias are a primary contributor to sudden cardiac death, a major unmet medical need. Because right ventricular (RV) dysfunction increases the risk for sudden cardiac death, we examined responses to RV stress in mice. Among immune cells accumulated in the RV after pressure overload-induced by pulmonary artery banding, interfering with macrophages caused sudden death from severe arrhythmias. We show that cardiac macrophages crucially maintain cardiac impulse conduction by facilitating myocardial intercellular communication through gap junctions. Amphiregulin (AREG) produced by cardiac macrophages is a key mediator that controls connexin 43 phosphorylation and translocation in cardiomyocytes. Deletion of Areg from macrophages led to disorganization of gap junctions and, in turn, lethal arrhythmias during acute stresses, including RV pressure overload and β-adrenergic receptor stimulation. These results suggest that AREG from cardiac resident macrophages is a critical regulator of cardiac impulse conduction and may be a useful therapeutic target for the prevention of sudden death., Cardiac immune cells play various roles in the maintenance of homeostasis and diseases in the heart. Here the authors show that cardiac resident macrophages are a critical regulator of cardiac impulse conduction through amphiregulin production, contributing to the prevention of sudden death.

In order to analyze stress and flow field around the aortic valve, various numerical method have been developed. The validity of the results has been confirmed from the point of hemo dynamical, fluid dynamical, view. However, it has not been evaluated from the point of view of structural mechanics. The purpose of this study is to construct a stress measurement experiment using a phantom model and to establish a numerical analysis method for it. The phantom model of the aortic valve was created with reference to the valve shape used for aortic valve reconstruction surgery. The displacement field of the phantom model is measured using Digital Image Correlation (DIC), which measures the three-dimensional displacement field on object surface using a stereo camera. In the numerical analysis, a finite element model simulating a phantom experiment is created, and quasi-static analysis is performed by an explicit method using LS-Dyna. Though the experimental results have not been obtained yet, according to the numerical analysis, max principle strain will increase at the fixed commissure and center of the coaptation.

The aortic valve is always under great mechanical stress. It is known that stress is greatly involved in the progression of the disease. For treatment, information such as valve stress, strain, and blood flow are helpful. Until now, the relationship between mechanical stress and disease progression has been studied in clinical research, phantom experiments, and numerical simulations.

Background Resection using a stapler is a popular approach to distal pancreatectomy. However, the resulting leakage of pancreatic juice represents a serious problem. We have developed a force-fluorescence measurement as a first step towards the quantitative evaluation of pancreatic leakage due to tissue tearing under compression. Methods The system comprises a testing machine with an indenter, similar in size to a stapler, which controls compression speed and measures reaction force, and a fluorescence measurement system to measure pancreatic juice leakage. Pancreatic juice leakage is measured as the maximum value of the increasing rate of fluorescence intensity (max value). Ten excised swine pancreases were compressed at a speed of 500, 100, and 10 mm/min until their thicknesses became 2 mm. Results A strong positive correlation (0.804) was observed between the increase in max value before and after compression and the amount of reaction force drop due to tissue destruction. No pancreatic juice leakage was observed when compressed slowly (10 mm/min). Conclusions We have successfully developed a novel force-fluorescence measurement system that can detect and quantify pancreatic juice leakage caused by tissue tearing. This system can determine the optimal compression conditions for preventing pancreatic juice leakage.

Background Recently, an interoperative catheter electrode mapping system, termed ExTRa Mapping (EXT), was developed for precise diagnosis and effective treatment of non-paroxysmal atrial fibrillations (non-PAF). However, the mapping accuracy of EXT is still unclear.Methods and Results:In this study, the reliability of the EXT in comparison with that of high-resolution optical membrane potential mapping was compared. Spiral wave re-entries (SWRs) were induced in the excised rabbit hearts (n=8, 42 episodes). Electrical signals were measured by electrodes on a transparent silicone plate, with the same arrangement as in the clinical catheter, and fluorescence signals were recorded simultaneously across the plate. Based on the phase maps derived by EXT, activation patterns (one-directed propagations: 26, rotational activities: 16) were identified correctly with 95% accuracy (40/42), and the correlation coefficient of the ratio of the non-passive period was 0.95. In the rotational episodes (15), the mean position error of the centers of gravity of the SWR trajectory (2,000 ms) was 2.0 mm. For the one-directional episodes (25), the correlation coefficient of the directions of one-way propagation was 0.99. Conclusions The phase map sequence by EXT is consistent with that by the analyses of high-resolution optical mapping. EXT is reliable for analyzing the activation pattern in the region of interest.

The action mechanism of stimulation toward spiral waves (SWs) owing to the complex excitation patterns that occur just after point stimulation has not yet been experimentally clarified. This study sought to test our hypothesis that the effect of capturing excitable gap of SWs by stimulation can also be explained as the interaction of original phase singularity (PS) and PSs induced by the stimulation on the wave tail (WT) of the original SW. Phase variance analysis was used to quantitatively analyze the postshock PS trajectories. In a two-dimensional subepicardial layer of Langendorff-perfused rabbit hearts, optical mapping was used to record the excitation pattern during stimulation. After a SW was induced by S1–S2 shock, single biphasic point stimulation S3 was applied. In 70 of the S1-S2-S3 stimulation episodes applied on 6 hearts, the original PS was clearly observed just before the S3 point stimulation in 37 episodes. Pairwise PSs were newly induced by the S3 in 20 episodes. The original PS collided with the newly induced PSs in 16 episodes; otherwise, they did not interact with the original PS. SW shift occurred most efficiently when the S3 shock was applied at the relative refractory period, and PS shifted in the direction of the WT. In conclusion, quantitative tracking of PS clarified that stimulation in desirable conditions induces pairwise PSs on WT and that the collision of PSs causes SW shift along the WT. The results of this study indicate the importance of the interaction of shock-induced excitation with the WT for effective stimulation. NEW & NOTEWORTHY The quantitative analysis of spiral wave dynamics during stimulation clarified the action mechanism of capturing the excitable gap, i.e., the induction of pairwise phase singularities on the wave tail and spiral wave shift along the wave tail as a result of these interactions. The importance of the wave tail for effective stimulation was revealed.

Flexible ultrasonic transducer array probe can realize successive monitoring by pasting it to the body. Imaging with a flexible ultrasonic probe requires information of the elements’ relative position. In this paper, we propose an algorithm to estimate the array shape from radio frequency data acquired by synthetic aperture. With this algorithm, the array shape is estimated based on the entropy of the reconstructed image using the assumed array shape. Entropy is used to evaluate the sharpness of an image. The real array shape can be estimated by searching for the shape which minimizes the entropy. Simulation experiments to verify the proposed algorithm were conducted. A 16 elements, 5 MHz, 1mm pitch flexible transducer array was used. The average position error of all elements of 4 trials between the real shape and the estimated shape was 0.037 mm. These results indicate that the proposed algorithm has a possibility to enable medical imaging with flexible ultrasonic transducer array probe.

Automatic aortic valve segmentation in cardiac CT scans is of high significance for surgeons’ diagnosis on aortic valve disease and planning of aortic valve-sparing surgery. However, the very fast flapping speed, ambiguous shapes and extremely thin structures of the aortic valve lead to great difficulties in developing automatic segmentation algorithms. In this paper, we proposed an end-to-end deep learning method to address the problem of segmentation of the aortic valve from cardiac CT scans. Our method uses 3D voxel-wise dilated residual network (DRN) as backbone network and we equip it with novel attention-guided decoder modules to suppress non-valve artifacts and noise and pay attention on the fine leaflets in order to acquire accurate valve segmentation results. We conducted qualitative and quantitative analysis to compare with state-of-the-art (SOTA) 3D medical image segmentation models. Experiment results corroborate that the proposed method has very high competence.

Background Precise analysis of cardiac spiral wave (SW) dynamics is essential for effective arrhythmia treatment. Although the phase singularity (PS) point in the spatial phase map has been used to determine the cardiac SW center for decades, quantitative detection algorithms that assume PS as a point fail to trace complex and rapid PS dynamics. Through a detailed analysis of numerical simulations, we examined our hypothesis that a boundary of spatial phase discontinuity induced by a focal conduction block exists around the moving SW center in the phase map. Method In a numerical simulation model of a 2D cardiac sheet, three different types of SWs (short wavelength; long wavelength; and low excitability) were induced by regulating ion channels. Discontinuities of all boundaries among adjacent cells at each instance were evaluated by calculating the phase bipolarity (PB). The total amount of phase transition (PTA) in each cell during the study period was evaluated. Results Pivoting, drifting, and shifting SWs were observed in the short-wavelength, low-excitability, and long-wavelength models, respectively. For both the drifting and shifting cases, long high-PB edges were observed on the SW trajectories. In all cases, the conduction block (CB) was observed at the same boundaries. These were also identical to the boundaries in the PTA maps. Conclusions The analysis of the simulations revealed that the conduction block at the center of a moving SW induces discontinuous boundaries in spatial phase maps that represent a more appropriate model of the SW center than the PS point.

Spiral wave (SW) plays a key role in generation and termination of fatal tachyarrhythmia. In our previous study, we proposed a novel objective tracking method of SW center trajectories by using phase variance (PV) analysis. This method evaluates local variance of phase values around each point to detect the position of SW center as the peak of PV map. PV analysis improved the detection accuracy of meandering SW centers and complex multiple SW centers significantly. However, the algorithm still includes some hyperparameters, the window size for the evaluation of phase variance and the binarization threshold for the peak detection. Thus, some guideline for proper determination of those parameters has been required. In this study, we hypothesized that the appropriate binarization threshold depends on the window size, and it can be determined as the level of PV value with specific p-value on Rayleigh’s test. We compared the detection results with various window sizes (3, 6, 9, 12 pixels) and various thresholds (from 0.4 to 0.9). Optical mapping measurement data with single SW center and four SW centers were evaluated. As a result, proper numbers of SW centers were detected with the thresholds corresponding to the p-value of 0.05, even with the smallest window size (3 pixels). From these results, it was indicated that the suggested method based on Rayleigh’s test is valuable for proper determination of PV analysis parameters.

To measure the activity of tissue at the microscopic level, laminar optical tomography (LOT), which is a microscopic form of diffuse optical tomography, has been developed. However, obtaining sufficient recording speed to determine rapidly changing dynamic activity remains major challenges. For a high frame rate of the reconstructed data, we here propose a new LOT method using compressed sensing theory, called compressive laminar optical tomography (CLOT), in which novel digital micromirror device-based illumination and data reduction in a single reconstruction are applied. In the simulation experiments, the reconstructed volumetric images of the action potentials that were acquired from 5 measured images with random pattern featured a wave border at least to a depth of 2.5 mm. Consequently, it was shown that CLOT has potential for over 200 fps required for the cardiac electrophysiological phenomena.

Objective : Spiral reentry is a recognized cause of tachycardia. Detection and tracking of the spiral core are essential for understanding the spiral wave dynamics. The core of the spiral corresponds to a phase singularity (PS), which can be identified in an optical mapping image by a kernel convolution method. However, because of a large number of false positives, this method cannot automatically and stably track the core of sustaining spiral reentry in optical mapping data. Method : We developed a new PS detection algorithm that quantifies the variance of phase values in a phase map and identifies the position of PS as its peak. Results : In comparison with the kernel convolution method, our method improved the precision of detecting a single sustaining spiral wave core from 73.1% to 99.8%. The precision of the proposed method for virtual-electrode-polarization-induced multiple PSs detections was also higher than the convolutional method. Conclusion : The proposed method detects PS by finding the peaks in the phase variance distribution of cardiac optical mapping image. It improved the precision of the core detection of the spiral wave in cardiac optical mapping images in comparison with the conventional kernel convolution method. Significance : The proposed method will reveal the spiral wave dynamics in optical mapping images better than existing approaches. The objective analysis method of a spiral wave is important for understanding the mechanisms and dynamics of serious heart arrhythmias.