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• W2962994363 abstract "Microwave-induced thermoacoustic imaging (MITAI) has found diversified applications in biomedical related disciplines and holds the potential to serve as an auxiliary measure for diagnosis and treatment in clinics. Conventional imaging algorithms for MITAI, for example, back-projection (BP), require exceedingly intensive sampling of thermoacoustic signals stemming from a sample under test, and thus, suffer from deficiencies such as low time efficiency, high system cost, and more microwave radiation received by patients. Combining compressive sensing (CS) and MITAI, referred to as CS-MITAI, is promising to address the problem. Reported experiments in two dimensions and simulations in three dimensions (3D) have proved that the CS-MITAI approach can reliably reconstruct images with much fewer measurements than the BP algorithm. However, there is a pressing need for the experimental validation of the CS-MITAI technique in a real three-dimensional (3-D) environment, which has profound implications for its practical applications and further improvement. This article experimentally investigates the 3-D CS-MITAI technique by imaging a sample with 3-D features. Establishment of a dictionary is crucial for the successful implementation of the 3-D CS-MITAI approach. An analytical method is proposed to build the dictionary with much higher efficiency than other reported measures, and its detailed derivation is provided. In addition, modeling work and parametric studies are performed to explore the robustness of the 3-D CS-MITAI mechanism in dealing with different cases. Both simulation and experimental results show that the 3-D CS-MITAI modality can offer comparable imaging quality as the BP method but requires 200 and 68 times fewer measurements, respectively." @default.
• W2962994363 created "2019-07-30" @default.
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• W2962994363 date "2020-01-01" @default.
• W2962994363 modified "2023-09-30" @default.
• W2962994363 title "Three-Dimensional Microwave-Induced Thermoacoustic Imaging Based on Compressive Sensing Using an Analytically Constructed Dictionary" @default.
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• W2962994363 doi "https://doi.org/10.1109/tmtt.2019.2936568" @default.
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