arXiv subjects

Keiichi Nakagawa

Publications and source records attributed to Keiichi Nakagawa.

6 recordsLinked to original sources

arXiv2025

Compact, widely tunable ultrashort burst pulse generator using four mirrors

Ultrashort burst laser pulses serve as powerful tools for precise laser processing, broadband ultrafast spectroscopy, and high-speed laser-scanning microscopy. However, the performance of conventional burst pulse generators is limited by constraints in the pulse time interval variability, pulse energy variability, pulse number variability, and overall system complexity. Here, we present a compact burst pulse generator that offers a broad tuning range for pulse time intervals, along with control over the number of pulses and pulse energies within the burst. It consists of four mirrors, two of which are parallel to each other, and outputs pulses that are equally spaced both temporally and spatially. We demonstrated the generation of a burst laser pulse by shaping a single ultrashort laser pulse into six pulses with time intervals ranging from femtoseconds to nanoseconds. The pulse time intervals and energies were consistent with theoretical results.

physics.optics↗

arXiv2024

Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models

Image-generative artificial intelligence (AI) has garnered significant attention in recent years. In particular, the diffusion model, a core component of generative AI, produces high-quality images with rich diversity. In this study, we proposed a novel computed tomography (CT) reconstruction method by combining the denoising diffusion probabilistic model with iterative CT reconstruction. In sharp contrast to previous studies, we optimized the fidelity loss of CT reconstruction with respect to the latent variable of the diffusion model, instead of the image and model parameters. To suppress the changes in anatomical structures produced by the diffusion model, we shallowed the diffusion and reverse processes and fixed a set of added noises in the reverse process to make it deterministic during the inference. We demonstrated the effectiveness of the proposed method through the sparse-projection CT reconstruction of 1/10 projection data. Despite the simplicity of the implementation, the proposed method has the potential to reconstruct high-quality images while preserving the patient's anatomical structures and was found to outperform existing methods, including iterative reconstruction, iterative reconstruction with total variation, and the diffusion model alone in terms of quantitative indices such as the structural similarity index and peak signal-to-noise ratio. We also explored further sparse-projection CT reconstruction using 1/20 projection data with the same trained diffusion model. As the number of iterations increased, the image quality improved comparable to that of 1/10 sparse-projection CT reconstruction. In principle, this method can be widely applied not only to CT but also to other imaging modalities.

cs.CV↗

arXiv2024

Development of a near-infrared wide-field integral field unit by ultra-precision diamond cutting

Integral Field Spectroscopy (IFS) is an observational method to obtain spatially resolved spectra over a specific field of view (FoV) in a single exposure. In recent years, near-infrared IFS has gained importance in observing objects with strong dust attenuation or at high redshift. One limitation of existing near-infrared IFS instruments is their relatively small FoV, less than 100 arcsec$^2$, compared to optical instruments. Therefore, we have developed a near-infrared (0.9-2.5 $\mathrmμ$m) image-slicer type integral field unit (IFU) with a larger FoV of 13.5 $\times$ 10.4 arcsec$^2$ by matching a slice width to a typical seeing size of 0.4 arcsec. The IFU has a compact optical design utilizing off-axis ellipsoidal mirrors to reduce aberrations. Complex optical elements were fabricated using an ultra-precision cutting machine to achieve RMS surface roughness of less than 10 nm and a P-V shape error of less than 300 nm. The ultra-precision machining can also simplify alignment procedures. The on-sky performance evaluation confirmed that the image quality and the throughput of the IFU were as designed. In conclusion, we have successfully developed a compact IFU utilizing an ultra-precision cutting technique, almost fulfilling the requirements.

astro-ph.IM↗

arXiv2022

Dispersive coherent Brillouin scattering spectroscopy

Frequency- and time-domain Brillouin scattering spectroscopy are powerful tools to read out the mechanical properties of complex systems in material and life sciences. Indeed, coherent acoustic phonons in the time-domain method offer superior depth resolution and a stronger signal than incoherent acoustic phonons in the frequency-domain method. However, it does not allow multichannel detection and, therefore, falls short in signal acquisition speed. Here, we present Brillouin scattering spectroscopy that spans the time and frequency domains to allow the multichannel detection of Brillouin scattering light from coherent acoustic phonons. Our technique maps the time-evolve Brillouin oscillations at the instantaneous frequency of a chromatic-dispersed laser pulse. The spectroscopic heterodyning of Brillouin oscillations in the frequency domain enhances the signal acquisition speed by at least 100-fold over the time-domain method. As a proof of concept, we imaged heterogeneous thin films and biological cells over a wide bandwidth with nanometer depth resolution. We, therefore, foresee that our approach catalyzes future phonon spectroscopy toward real-time mechanical imaging.

physics.optics↗

arXiv2022

Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion

In recent years, deep-learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep-learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large dataset is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. The proposed method is based on CycleGAN with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several datasets acquired from patients with head and neck cancer. The size of the datasets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?"

cs.CV↗

arXiv2019

Fast Statistical Iterative Reconstruction for MVCT in TomoTherapy

Statistical iterative reconstruction is expected to improve the image quality of megavoltage computed tomography (MVCT). However, one of the challenges of iterative reconstruction is its large computational cost. The purpose of this work is to develop a fast iterative reconstruction algorithm by combining several iterative techniques and by optimizing reconstruction parameters. Megavolt projection data was acquired from a TomoTherapy system and reconstructed using our statistical iterative reconstruction. Total variation was used as the regularization term and the weight of the regularization term was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual assessment of spatial resolution using Gammex and Cheese phantoms. Gradient decent with an adaptive convergence parameter, ordered subset expectation maximization (OSEM), and CPU/GPU parallelization were applied in order to accelerate the present reconstruction algorithm. The SNR and CNR of the iterative reconstruction were several times better than that of filtered back projection (FBP). The GPU parallelization code combined with the OSEM algorithm reconstructed an image several hundred times faster than a CPU calculation. With 500 iterations, which provided good convergence, our method produced a 512$\times$512 pixel image within a few seconds. The image quality of the present algorithm was much better than that of FBP for patient data. An image from the iterative reconstruction in TomoTherapy can be obtained within few seconds by fine-tuning the parameters. The iterative reconstruction with GPU was fast enough for clinical use, and largely improve the MVCT images.

physics.med-ph↗