A micro heat flux sensor which can measure the thermal energy transfer per unit area has been designed, fabricated, and calibrated in a convective environment. The sensor which is based on a circular foil gage is composed of thermal paths and a thermopile. The thermal path is made in a LIGA-like process of SU-8 high aspect ratio microstructures and electroplated copper layers. The thermopile, a series of thermocouples, is used to amplify the output signal as a thermometer. When the sensor is placed on a high-temperature wall, heat flux from the wall flows through thermal paths and drains out to the environment, producing a temperature difference along its paths. Heat flux is obtained by calibrating this temperature difference in the thermopile of Ni-Cr or Al-Chromel pairs. The sensitivity of the heat flux sensor of Ni-Cr and Al-Chromel pairs is in the range of 0.1-2.0 and 0.4-2.0 µV mW −1 cm −2 , respectively, in the heat flux range of 0-180 mW cm −2 .
Precise detection of hepatocellular carcinoma (HCC) is crucial for early cancer screening in medical ultrasound. Attenuation coefficient (AC) is emerging as a new biomarker for classifying tumors since it is sensitive to pathological changes in tissues. In this paper, a learning-based method to reconstruct AC image of abdominal regions from pulse-echo data obtained with a single ultrasound convex probe is presented. In the proposed method, the propagation delay caused by the variation of the sound speed of the medium is considered in the training phase to increase the reconstruction accuracy of the distant targets. In addition, the proposed network adaptively compensates the feature map according to the location of target area for accuracy. The proposed network was evaluated through simulation, and in-vivo tests. In simulation tests, the proposed network showed 3.8dB and 6% improvement over the baseline methods in PSNR and SSIM, respectively. In the in-vivo test, the proposed method classifies cysts, benign tumors and malignant tumors in the abdomen with a p-value of less than 0.02. The accuracy and robustness demonstrated by the proposed method show the broad clinical applicability of quantitative imaging in abdominal ultrasound.
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