Yusaku Ajichi

Bio: Yusaku Ajichi is an academic researcher from Keio University. The author has contributed to research in topics: Light intensity & Monte Carlo method. The author has an hindex of 1, co-authored 2 publications receiving 269 citations.

Monte Carlo Prediction of Near-Infrared Light Propagation in Realistic Adult and Neonatal Head Models

Abstract: In near-infrared spectroscopy and imaging, the sensitivity of the detected signal to brain activation and the volume of interrogated tissue are clinically important. Light propagation in adult and neonatal heads is strongly affected by the presence of a low-scattering cerebrospinal fluid layer. The effect of the heterogeneous structure of the head on light propagation in the adult brain is likely to be different from that in the neonatal brain because the thickness of the superficial tissues and the optical properties of the brain of the neonatal head are quite different from those of the adult head. In this study, light propagation in the two-dimensional realistic adult and neonatal head models, whose geometries are generated from a magnetic resonance imaging scan of the human heads, is predicted by Monte Carlo simulation. The sandwich structure, which is a low-scattering cerebrospinal fluid layer held between the high-scattering skull and gray matter, strongly affects light propagation in the brain of the adult head. The sensitivity of the absorption change in the gray matter is improved; however, the intensely sensitive region is confined to the shallow region of the gray matter. The high absorption of the neonatal brain causes a similar effect on light propagation in the head. The intensely sensitive region in the neonatal brain is confined to the gray matter; however, the spatial sensitivity profile penetrates into the deeper region of the white matter.

Analysis of light propagation in three-dimensional neonatal head model by finite difference method

Abstract: The light propagation in the three-dimensional neonatal head model is calculated by the finite-difference method. The model consists of rectangular parallelepiped elements to approximate the curved boundary. The intensity and partial optical path length in both the mismatched and matched boundary models calculated by finite-difference are agree well with those by Monte Carlo method.

HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.

Abstract: Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.

Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units.

Abstract: We report a parallel Monte Carlo algorithm accelerated by graphics processing units (GPU) for modeling time-resolved photon migration in arbitrary 3D turbid media. By taking advantage of the massively parallel threads and low-memory latency, this algorithm allows many photons to be simulated simultaneously in a GPU. To further improve the computational efficiency, we explored two parallel random number generators (RNG), including a floating-point-only RNG based on a chaotic lattice. An efficient scheme for boundary reflection was implemented, along with the functions for time-resolved imaging. For a homogeneous semi-infinite medium, good agreement was observed between the simulation output and the analytical solution from the diffusion theory. The code was implemented with CUDA programming language, and benchmarked under various parameters, such as thread number, selection of RNG and memory access pattern. With a low-cost graphics card, this algorithm has demonstrated an acceleration ratio above 300 when using 1792 parallel threads over conventional CPU computation. The acceleration ratio drops to 75 when using atomic operations. These results render the GPU-based Monte Carlo simulation a practical solution for data analysis in a wide range of diffuse optical imaging applications, such as human brain or small-animal imaging.

Optical brain imaging in vivo: techniques and applications from animal to man

Abstract: Optical brain imaging has seen 30 years of intense development, and has grown into a rich and diverse field. In-vivo imaging using light provides unprecedented sensitivity to functional changes through intrinsic contrast, and is rapidly exploiting the growing availability of exogenous optical contrast agents. Light can be used to image microscopic structure and function in vivo in exposed animal brain, while also allowing noninvasive imaging of hemodynamics and metabolism in a clinical setting. This work presents an overview of the wide range of approaches currently being applied to in-vivo optical brain imaging, from animal to man. Techniques include multispectral optical imaging, voltage sensitive dye imaging and speckle-flow imaging of exposed cortex, in-vivo two-photon microscopy of the living brain, and the broad range of noninvasive topography and tomography approaches to near-infrared imaging of the human brain. The basic principles of each technique are described, followed by examples of current applications to cutting-edge neuroscience research. In summary, it is shown that optical brain imaging continues to grow and evolve, embracing new technologies and advancing to address ever more complex and important neuroscience questions.