Koichi Tanji

Phase-separated Al–Si thin films

Phase-separated Al–Si films composed of Al nanocylinders embedded in an amorphous-Si matrix have been prepared by a sputtering method. By controlling the deposition rate, substrate temperature, and film composition, the average diameter of the Al cylinders can be varied systematically from less than 5to13nm with a cylinder density ranging from 1015 to in excess of 1016cylindersm−2. A three-dimensional simulation of phase separation in binary thin films was performed using a modified Cahn-Hilliard [J. Chem. Phys. 28, 258 (1958)] equation to understand the growth mechanism. The simulation studies indicate that the surface diffusion length and film composition are important factors which determine film morphology. Experimental and simulation studies are compared and discussed.

Advanced model-based reconstruction algorithm for practical three-dimensional photoacoustic imaging

In this study, we propose an advanced model-based reconstruction algorithm for three-dimensional photoacoustic imaging. The algorithm is based on accurate forward photoacoustic models and an optimization algorithm which minimizes the square of the error between the measured acoustic signals and the signals predicted by the forward models. The forward photoacoustic models incorporate system-configuration and detector-dependent factors such as frequency response and finite size effect. A conjugate gradient-based optimization algorithm is used for reconstructing images. In addition, we make use of the symmetry and locality of the photoacoustic waves in the computations of the forward photoacoustic models in order to reduce the memory requirements and computation time in three-dimensional image reconstruction. The results show that the proposed algorithm provides high-resolution and high-quality photoacoustic images.

Fabrication of Well-Aligned Al Nanowire Array Embedded in Si Matrix Using Limited Spinodal Decompositon

The morphology of a phase-separated binary film during spinodal decomposition, whose evolution is limited on the growth surface, is studied. The temporal evolution of the Cahn–Hilliard equation without the elastic energy term under frozen bulk approximation is solved to investigate the possibility of well-aligned nanostructures. The model simulation showed that the well-aligned phase pattern on the top surface can be formed when surface diffusion length was optimized and film thickness was larger than a certain value. These simulation results highly agree with the experimental observation of phase-separated Al–Si films. An additional simulation study indicated that well-aligned nanowires can be prepared from the initial growth stage when an initial phase pattern given on the substrate matched the periodicity of the phase pattern of deposited films.

Toward digital staining using stimulated Raman scattering and statistical machine learning

Stimulated Raman scattering (SRS) spectral microscopy is a promising imaging method, based on vibrational spectroscopy, which can visualize biological tissues with chemical specificity. SRS spectral microscopy has been used to obtain two-dimensional spectral images of rat liver tissue, three-dimensional images of a vessel in rat liver, and in vivo spectral images of mouse ear skin. Various multivariate analysis techniques, such as principal component analysis and independent component analysis, have been used to obtain spectral images. In this study, we propose a digital staining method. This method uses SRS spectra and statistical machine learning that makes use of prior knowledge of spectral peaks and their two-dimensional distributional patterns corresponding to the composition of tissue samples. The method selects spectral peaks on the basis of Mahalanobis distance, which is defined as the ratio of inter-group variation to intragroup variation. We also make use of higher-order local autocorrelations as feature values for two-dimensional distributional patterns. This combination of techniques allows groups corresponding to different intracellular structures to be clearly discriminated in the multidimensional feature space. We investigate the performance of our method on mouse liver tissue samples and show that the proposed method can digitally stain each intracellular structure such as cell nuclei, cytoplasm, and erythrocytes separately and clearly without time-consuming chemical staining processes. We anticipate that our method could be applied to computer-aided pathological diagnosis.

Adaptive three-dimensional wavelet analysis for denoising TOF-SIMS images: Toward digital staining of pathological specimens

In this report, we propose a three-dimensional adaptive wavelet analysis algorithm for rapidly denoising data acquired by time-of-flight secondary ion mass spectrometry (TOF-SIMS). In the computation, the TOF-SIMS data are stored in three-dimensional space, where the xy-plane corresponds to the measured area and the z-axis corresponds to the m/z mass spectrum. Reconstructed images for specific m/z peaks along the z-axis indicate the spatial distributions of components such as protein and lipid. In this algorithm, two different basis functions are applied to the xy-plane and the z-axis, and three-dimensional wavelet shrinkage and reconstruction is performed. One basis function is suitable for continuous data such as the distribution of components, and the other basis function is suitable for discrete data such as a mass spectra. We apply this algorithm to denoising of simulated and experimental image data. The results show that the noise is markedly reduced without loss of the original signals and that the signal-to-noise ratio is substantially improved by the three-dimensional noise reduction algorithm.