No abstract

Metasurfaces have been extensively studied for generating electromagnetic waves carrying orbital angular momentum (OAM). In particular, programmable metasurfaces enable real‐time switching between multiple OAM modes in a digital manner. However, the current programmable metasurfaces are mostly based on reflective mode, which suffer from low efficiency as well as serious feed blockage. In this paper, a transmissive programmable metasurface is presented for the highly efficient generation of multimode convergent OAM beams. The proposed transmissive metasurface is composed of electronically reconfigurable units with 1‐bit phase resolution (0/π), which are obtained by integrating two PIN diodes in the radiating layer for current direction modulation. Through the antisymmetry configuration of the two PIN diodes, nearly uniform transmission magnitudes but inversed phase states in a wide band can be obtained. The simulation results show that the proposed reconfigurable unit can achieve good 1‐bit phase tuning, with minimum insertion loss of 0.2 dB and 2 dB transmission bandwidth of more than 10%. Through the dynamic modulation of the quantized code distributions on the metasurface, programmable multimode OAM beams can thus be constructed. Both simulated and measured results verify the effectiveness of the proposed design.

No abstract

To conduct a patient-specific computational modeling of the aortic valve, 3D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3D cardiac images. Currently, most of computational studies involve manual heart valve geometry reconstruction and manual FE model generation, which is both time-consuming and prone to human errors. A seamless computational modeling framework, which can automate this process based on machine learning algorithms, is desirable, as it can not only eliminate human errors and ensure the consistency of the modeling results, but also allows fast feedback to clinicians and permits a future population-based probabilistic analysis of large patient cohorts. In this study, we developed a novel computational modeling method to automatically reconstruct the 3D geometries of the aortic valve from CT images. The reconstructed valve geometries have built-in mesh correspondence, which bridges harmonically for the consequent FE modeling. The proposed method was evaluated by comparing the reconstructed geometries from ten patients to those manually created by human experts, and a mean discrepancy of 0.69 mm was obtained. Based on these reconstructed geometries, FE models of valve leaflets were developed, and aortic valve closure from end systole to mid-diastole was simulated for seven patients and validated by comparing the deformed geometries to those manually created by human experts, and a mean discrepancy of 1.57 mm was obtained. The proposed method offers great potential to streamline the computational modeling process and enables the development of a pre-operative planning system for aortic valve disease diagnosis and treatment.