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.

Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.

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.

Autonomous soft tissue manipulation in robotic surgery remains challenging. Modeling the tool-tissue interaction, analyzing the tissue structural deformation and monitoring the biomechanical status during surgery manipulation may benefit the development of autonomous surgery; however, there are currently inadequate studies. We propose a vision-based surgery environment modeling framework to simultaneously reconstruct and track the forceps and the tissue, leveraging model-based pose estimation and scene flow-based mesh optimization. We also propose a digital twin based on the framework for continuously modeling the tool-tissue interaction and monitoring the deformation and strain of the tissue surface. Quantitative and qualitative ex vivo experiments were conducted to evaluate the proposed system from various perspectives. Results show that the deformation recovery accuracy is $\bm {0.38\pm 0.30}$ mm with robustness to occlusion; the instrument pose estimation accuracy is $\bm {0.85\pm 0.57}$ degree in rotation and $\bm {2.09\pm 1.41}$ mm in translation. The relative positioning between the tissue and the forceps can be correctly modeled in terms of contact detection. The system also correctly reveals the differences in strain distributions in two types of tool-tissue interaction, palpation and traction. With the proposed system, surgical robot systems with the perception of tissue deformation can be developed in the future for optimized and autonomous surgery.

Daily monitoring of internal tissues with shape-conformable and breathable ultrasound (US) imaging probes is promising for early detection of diseases. In recent years, textile substrates are widely used for wearable devices since they satisfy both conformability and breathability. However, it is not currently possible to use textile substrates for US probes due to the reflection or attenuation of US waves at the air gaps in the textiles. In this paper, we fabricated a shape-conformable and breathable US imaging probe by sandwiching the US elements between two woven polyester textiles on which copper electrodes were formed through electroless plating. The air gaps between the fibers at the electrode parts were filled with copper, allowing for high penetration of US waves. On the other hand, the non-electrode parts retain air gaps, leading to high breathability. The fabricated textile-based probe showed low flexural rigidity (0.066 × 10⁻⁴ N m² m⁻¹) and high air permeability (11.7 cm³ cm⁻² s⁻¹). Human neck imaging demonstrated the ability of the probe to monitor the pulsation of the common carotid artery and change in the internal jugular vein diameter, which led to the early detection of health issues such as arteriosclerosis and dehydration, and the capability of stable 24-hour monitoring.