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We propose the "Tile-net" method based on dividing an image into smaller tiles. Using the tile as the input to the neural network, the network is simplified substantially. The Tile-net learns at a much faster rate than the networks without tiling. The training and reconstruction times for the Tile-net are reduced by 40% and 33%, respectively compared to the networks without tiling. The Tile-net performance is evaluated through the normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structure similarity index measure (SSIM) and the quality of the reconstructed image for test datasets. The Tile-net does not degrade performance; however, it reduces the NMSE by 0.3% compared to the networks without tiling.

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This paper proposes a method to enhance terahertz reflection tomographic imaging by interference cancellation between layers. When the gap between layers is small, the signal reflected on the upper layer interferes with that on the lower layer, which degrades the quality of the reconstructed tomographic image in the lower layer. The proposed method estimates the upper-layer reflection signal by system modeling, which is then eliminated from the acquired signal. In this way, it can provide the correct lower-layer reflection signal, thereby improving the quality of the lower-layer tomographic image. The performance of the proposed method was confirmed using computer simulation data and real terahertz reflection data.

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A method is proposed to measure sample stiffness using terahertz wave and acoustic stimulation. The stiffness-dependent vibration is measured using terahertz wave (T-ray) during an acoustic stimulation. To quantify the vibration, time of the peak amplitude of the reflected T-ray is measured. In our experiment, the T-ray is asynchronously applied during the period of the acoustic stimulation, and multiple measurements are taken to use the standard deviation and the maximum difference in the peak times to estimate the amplitude of the vibration. Some preliminary results are shown using biological samples.

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Correction of an inhomogeneous magnetic field is proposed using partial differential phases in magnetic resonance imaging. Estimation of the inhomogeneous magnetic field from a measured phase is not an easy task due to phase wrapping and chemical-dependent phase shifts. Using the proposed partial differential phase technique, such problems are resolved. The proposed technique uses most of the 3D pixel data regardless of chemical compounds for the estimation of the inhomogeneous magnetic field. A large number of partial difference data compared to the number of expansion terms for the model of inhomogeneous magnetic field provides a very stable estimation, robust to noise. The technique is applicable to in vivo shimming, water-fat imaging, eddy current compensation, and most phase-related measurements and imaging. The efficacy of the proposed technique is demonstrated with in vivo water-fat imaging.

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Reflection-type terahertz tomography is obtained using time-domain spectroscopy. Due to different velocities of the terahertz ray in free space and inside a sample, the tomographic transverse plane is not obtained by a simple reconstruction using time index. A pre-processing method is proposed to compensate for the different velocities of the terahertz ray for tomographic reconstruction. Maximum intensity projection, averaging, and short-time Fourier transform are proposed as post-processing methods along the depth direction for the terahertz tomography. Log-scale display is also suggested for a better visualization. Some experimental results with the pre- and post-processing are demonstrated.

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In this paper, an adaptive compressed sensing is proposed in order to enhance the performance of fast tetrahertz reflection tomography. The proposed method first acquires data at random measurement points in the spatial domain, and estimates the regions in each tomographic image where much degradation is expected. Then, it allocates additional measurement points to those regions, so that more data are acquired adaptively at the regions prone to degradation, thereby improving the quality of the reconstructed tomographic images. The proposed method was applied to the T-ray reflection tomography system, and the image quality enhancement by the proposed method, compared to the conventional method, was verified for the same number of measurement points.

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In this paper, a new fast terahertz reflection tomography is proposed using block-based compressed sensing. Since measuring the time-domain signal on two-dimensional grid requires excessive time, reducing measurement time is highly demanding in terahertz tomography. The proposed technique directly reduces the number of sampling points in the spatial domain without modulation or transformation of the signal. Compressed sensing in spatial domain suggests a block-based reconstruction, which substantially reduces computational time without degrading the image quality. An overlap-average method is proposed to remove the block artifact in the block-based compressed sensing. Fast terahertz reflection tomography using the block-based compressed sensing is demonstrated with an integrated circuit and parched anchovy as examples.

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A technique, based on Echo planar imaging (EPI)-based phase modulation factor maps, is described for correction of EPI distortions resulting from field inhomogeneity. In this paper, a phase modulation factor was employed to remove the distortions. The phase modulation factor was obtained experimentally by collecting EPI images with a spin-echo (TE) spacing, deltaTE, equal to the inter-echo time interval, T(i). Then, the distortions resulting from the field inhomogeneity were removed by modulating the kappa-space data with the phase modulation factor. One of the advantages of this method is that it requires only a few extra scans to collect the information on field inhomogeneity. The proposed method does not require a phase unwrapping procedure for field inhomogeneity correction and, hence, is easier to implement, compared to other techniques. In addition, it corrects geometric distortion as well as intensity distortions simultaneously, which is robust to external noise or estimation error in severely distorted images. In this work, we also compared the proposed technique with others including, a) interpolation method with EPI-based displacement maps, and b) modulation method with phase modulation factor maps generated from spin-echo images. The results suggest the proposed technique is superior in correcting severely distorted images.

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