arXiv subjects

Shigeki Aoki

Publications and source records attributed to Shigeki Aoki.

16 recordsLinked to original sources

arXiv2026

First overnight balloon flight of the GRAINE 2023 emulsion gamma-ray telescope enabled by a large-scale pressure-vessel gondola

The Gamma-Ray Astro Imager with Nuclear Emulsion (GRAINE) project conducts precision observations of sub-GeV--GeV cosmic gamma rays using a balloon-borne nuclear-emulsion telescope with high angular resolution. In GRAINE 2023, a 2.5-m$^{2}$ telescope was flown in the project's first overnight balloon flight, including observation periods for the Vela pulsar and Galactic center region. To operate the telescope under the low-pressure and low-temperature stratospheric environment, the balloon-style pressure-vessel concept was scaled up to a lightweight gondola with an internal length of 4.9 m. A new aluminum-alloy ring structure and a lightweight membranous-shell material, SHL-300MDL, were developed. While the telescope aperture was increased by a factor of 6.6 over GRAINE 2018, the pressure-vessel gondola mass was limited to 179 kg. Ground tests of the completed flight assembly demonstrated a differential pressure above 100 hPa at room temperature and at a mean temperature of $-66.0^{\circ}$C. The payload was launched from Alice Springs, Australia, in April 2023 and achieved a total flight duration of approximately 27 h, including 24.3 h of level flight. Although the upper membranous shell reached approximately $-60^{\circ}$C at night, the vessel internal pressure remained above the required 100 hPa throughout level flight. These results demonstrate that the developed gondola can accommodate a 2.5-m$^{2}$ emulsion gamma-ray telescope and maintain the required pressure during overnight stratospheric flight. Scientific analyses of astrophysical and atmospheric gamma rays, including dedicated analysis of the Galactic center region, are ongoing using the recovered emulsion data. This development provides a technical basis for repeated observations with future large-area GRAINE telescopes.

astro-ph.IM↗

arXiv2026

Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes

This paper proposes an automated classification method of chest CT volumes based on likelihood of COVID-19 cases. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. We propose a COVID-19 classification convolutional neural network (CNN) that has a 2D/3D hybrid feature extraction flows. The 2D/3D hybrid feature extraction flows are designed to effectively extract image features from anisotropic volumes such as chest CT volumes for diagnosis. The flows extract image features on three mutually perpendicular planes in CT volumes and then combine the features to perform classification. Classification accuracy of the proposed method was evaluated using a dataset that contains 1288 CT volumes. An averaged classification accuracy was 83.3%. The accuracy was higher than that of a classification CNN which does not have 2D and 3D hybrid feature extraction flows.

cs.CV↗

arXiv2026

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer

This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers.

cs.CV↗

arXiv2025

Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks

We address the challenge of parameter-efficient fine-tuning (PEFT) for three-dimensional (3D) U-Net-based denoising diffusion probabilistic models (DDPMs) in magnetic resonance imaging (MRI) image generation. Despite its practical significance, research on parameter-efficient representations of 3D convolution operations remains limited. To bridge this gap, we propose Tensor Volumetric Operator (TenVOO), a novel PEFT method specifically designed for fine-tuning DDPMs with 3D convolutional backbones. Leveraging tensor network modeling, TenVOO represents 3D convolution kernels with lower-dimensional tensors, effectively capturing complex spatial dependencies during fine-tuning with few parameters. We evaluate TenVOO on three downstream brain MRI datasets-ADNI, PPMI, and BraTS2021-by fine-tuning a DDPM pretrained on 59,830 T1-weighted brain MRI scans from the UK Biobank. Our results demonstrate that TenVOO achieves state-of-the-art performance in multi-scale structural similarity index measure (MS-SSIM), outperforming existing approaches in capturing spatial dependencies while requiring only 0.3% of the trainable parameters of the original model. Our code is available at: https://github.com/xiaovhua/tenvoo

eess.IV↗

arXiv2024

Revolutionizing MRI Data Processing Using FSL: Preliminary Findings with the Fugaku Supercomputer

The amount of Magnetic resonance imaging data has grown tremendously recently, creating an urgent need to accelerate data processing, which requires substantial computational resources and time. In this preliminary study, we applied FMRIB Software Library commands on T1-weighted and diffusion-weighted images of a single young adult using the Fugaku supercomputer. The tensor-based measurements and subcortical structure segmentations performed on Fugaku supercomputer were highly consistent with those from conventional systems, demonstrating its reliability and significantly reduced processing time.

physics.med-ph↗

arXiv2024

New high-precision measurement system for electron-positron pairs from sub-GeV/GeV gamma-rays in the emulsion telescope

The GRAINE project observes cosmic gamma-rays, using a balloon-borne emulsion-film-based telescope in the sub-GeV/GeV energy band. We reported in our previous balloon experiment in 2018, GRAINE2018, the detection of the known brightest source, Vela pulsar, with the highest angular resolution ever reported in an energy range of $>$80 MeV. However, the emulsion scanning system used in the experiment was designed to achieve a high-speed scanning, and it was not accurate enough to ensure the optimum spacial resolution of the emulsion film and limited the performance. Here, we report a new high-precision scanning system that can be used to greatly improve the observation result of GRAINE2018 and also be employed in future experiments. The system involves a new algorithm that recognizes each silver grain on an emulsion film and is capable of measuring tracks with a positional resolution for the passing points of tracks of almost the same as the intrinsic resolution of nuclear emulsion film ($\sim$70 nm). This resolution is approximately one order of magnitude smaller than that obtained with the high-speed scanning system. With this system, an angular resolution for gamma-rays of 0.1$^\circ$ at 1 GeV is expected to be achieved. Furthermore, we successfully combine the new high-precision system with the existing high-speed system, establishing the system to make a high-speed and high-precision measurement. Employing these systems, we reanalyze the gamma-ray events detected previously by only the high-speed system in GRAINE2018 and obtain an about three times higher angular resolution (0.22$^\circ$) in 500--700 MeV than the original value. The high-resolution observation may bring new insights into the gamma-ray emission from the Galactic center region and may realize polarization measurements of high-energy cosmic gamma-rays.

astro-ph.IM↗

arXiv2024

CORTEX: Large-Scale Brain Simulator Utilizing Indegree Sub-Graph Decomposition on Fugaku Supercomputer

We introduce CORTEX, an algorithmic framework designed for large-scale brain simulation. Leveraging the computational capacity of the Fugaku Supercomputer, CORTEX maximizes available problem size and processing performance. Our primary innovation, Indegree Sub-Graph Decomposition, along with a suite of parallel algorithms, facilitates efficient domain decomposition by segmenting the global graph structure into smaller, identically structured sub-graphs. This segmentation allows for parallel processing of synaptic interactions without inter-process dependencies, effectively eliminating data racing at the thread level without necessitating mutexes or atomic operations. Additionally, this strategy enhances the overlap of communication and computation. Benchmark tests conducted on spiking neural networks, characterized by biological parameters, have demonstrated significant enhancements in both problem size and simulation performance, surpassing the capabilities of the current leading open-source solution, the NEST Simulator. Our work offers a powerful new tool for the field of neuromorphic computing and understanding brain function.

cs.DC↗

arXiv2023

Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping

This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists through an interpretation of the CT volumes, however, because of the heavy workload, an automatic analysis method using a computer is desired. Most computer-aided diagnosis studies have addressed only a portion of the elements necessary for the identification. In this work, we realize the identification method through a classification task by using a 2.5-dimensional CNN with three-dimensional attention mechanisms. We visualize the suspicious regions by applying a backpropagation based on positive gradients to attention-weighted features. We perform experiments on an in-house dataset and two public datasets to reveal the generalization ability of the proposed method. The proposed architecture achieved AUCs of over 0.900 for all the datasets, and mean sensitivity $0.853 \pm 0.036$ and specificity $0.870 \pm 0.040$. The method can also identify notable lesions pointed out in the radiology report as suspicious regions.

eess.IV↗

arXiv2023

Development of proton beam irradiation system for the NA65/DsTau experiment

Tau neutrino is the least studied lepton of the Standard Model (SM). The NA65/DsTau experiment targets to investigate $D_s$, the parent particle of the $\nu_\tau$, using the nuclear emulsion-based detector and to decrease the systematic uncertainty of $\nu_\tau$ flux prediction from over 50% to 10% for future beam dump experiments. In the experiment, the emulsion detectors are exposed to the CERN SPS 400 GeV proton beam. To provide optimal conditions for the reconstruction of interactions, the protons are required to be uniformly distributed over the detector's surface with an average density of $10^5~\rm{cm^{-2}}$ and the fluctuation of less than 10%. To address this issue, we developed a new proton irradiation system called the target mover. The new target mover provided irradiation with a proton density of $0.98~\rm{cm^{-2}}$ and the density fluctuation of $2.0\pm 0.3$% in the DsTau 2021 run.

physics.ins-det↗

arXiv2022

COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty

This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and deaths. CT image-based diagnosis of COVID-19 can provide quick and accurate diagnosis results. An automated segmentation method of infection regions in the lung provides a quantitative criterion for diagnosis. Previous methods employ whole 2D image or 3D volume-based processes. Infection regions have a considerable variation in their sizes. Such processes easily miss small infection regions. Patch-based process is effective for segmenting small targets. However, selecting the appropriate patch size is difficult in infection region segmentation. We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions. The receptive field sizes can be defined as the patch size and the resolution of volumes where patches are clipped from. This paper proposes an infection segmentation network (ISNet) that performs patch-based segmentation and a scale uncertainty-aware prediction aggregation method that refines the segmentation result. We design ISNet to segment infection regions that have various intensity values. ISNet has multiple encoding paths to process patch volumes normalized by multiple intensity ranges. We collect prediction results generated by ISNets having various receptive field sizes. Scale uncertainty among the prediction results is extracted by the prediction aggregation method. We use an aggregation FCN to generate a refined segmentation result considering scale uncertainty among the predictions. In our experiments using 199 chest CT volumes of COVID-19 cases, the prediction aggregation method improved the dice similarity score from 47.6% to 62.1%.

eess.IV↗

arXiv2019

Convolutional Neural Networks for Estimation of Myelin Maturation in Infant Brain

Myelination plays an important role in the neurological development of infant brain and MRI can visualize the myelination extension as T1 high and T2 low signal intensity at white matter. We tried to construct a convolutional neural network machine learning model to estimate the myelination. Eight layers CNN architecture was constructed to estimate the subjects age with T1 and T2 weighted image at 5 levels associated with myelin maturation in 119 subjects up to 24 months. CNN model learned with all age dataset revealed a strong correlation between the estimated age and the corrected age and the coefficient of correlation, root mean square error and mean absolute error was 0. 81, 3. 40 and 2. 28. Moreover, the adaptation of ensemble learning models with two datasets 0 to 16 months and 8 to 24 months improved that to 0. 93, 2. 12 and 1. 34. Deep learning can be adaptable to myelination estimation in infant brain.

q-bio.QM↗

arXiv2019

DsTau: Study of tau neutrino production with 400 GeV protons from the CERN-SPS

In the DsTau experiment at the CERN SPS, an independent and direct way to measure tau neutrino production following high energy proton interactions was proposed. As the main source of tau neutrinos is a decay of Ds mesons, produced in proton-nucleus interactions, the project aims at measuring a differential cross section of this reaction. The experimental method is based on a use of high resolution emulsion detectors for effective registration of events with short lived particle decays. Here we present the motivation of the study, details of the experimental technique, and the first results of the analysis of the data collected during test runs, which prove feasibility of the full scale study of the process in future.

hep-ex↗

arXiv2019

The utility of a convolutional neural network for generating a myelin volume index map from rapid simultaneous relaxometry imaging

Background and Purpose: A current algorithm to obtain a synthetic myelin volume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI) has a potential problem, that it does not incorporate information from surrounding pixels. The purpose of this study was to develop a method that utilizes a convolutional neural network (CNN) to overcome this problem. Methods: RSRI and magnetization transfer images from 20 healthy volunteers were included. A CNN was trained to reconstruct RSRI-related metric maps into a myelin volume-related index (generated myelin volume index: GenMVI) map using the myelin volume index map calculated from magnetization transfer images (MTMVI) as reference. The SyMVF and GenMVI maps were statistically compared by testing how well they correlated with the MTMVI map. The correlations were evaluated based on: (i) averaged values obtained from 164 atlas-based ROIs, and (ii) pixel-based comparison for ROIs defined in four different tissue types (cortical and subcortical gray matter, white matter, and whole brain). Results: For atlas-based ROIs, the overall correlation with the MTMVI map was higher for the GenMVI map than for the SyMVF map. In the pixel-based comparison, correlation with the MTMVI map was stronger for the GenMVI map than for the SyMVF map, and the difference in the distribution for the volunteers was significant (Wilcoxon sign-rank test, P<.001) in all tissue types. Conclusion: The proposed method is useful, as it can incorporate more specific information about local tissue properties than the existing method.

eess.IV↗

arXiv2018

First demonstration of gamma-ray imaging using balloon-borne emulsion telescope

We promote the precise gamma-ray observation project Gamma-Ray Astro-Imager with Nuclear Emulsion (GRAINE), which uses balloon-borne emulsion gamma-ray telescopes. The emulsion telescope realizes observations with high angular resolution, polarization sensitivity, and large aperture area in the 0.01--100 GeV energy region. Herein, we report the data analysis of emulsion tracks and the first demonstration of gamma-ray imaging via an emulsion telescope by using the flight data from the balloon experiment performed in 2015 (GRAINE 2015). The emulsion films were scanned by the latest read-out system for a total area of 41 m$^2$ in three months, and then the gamma-ray event selection was automatically processed. Millions of electron-pair events are accumulated in the balloon-borne emulsion telescope. The emulsion telescope detected signals from a calibration source (gamma rays from the interaction of cosmic rays with an aluminum plate) with a high significance during the balloon observation and created a gamma-ray image consistent with the source size and the expected angular resolution in the energy range of 100--300 MeV. The flight performance obtained in the GRAINE 2015 experiment proves that balloon-borne emulsion telescope experiments with larger area are feasible while maintaining expected imaging performance.

astro-ph.IM↗

arXiv2016

Demonstration of polarization sensitivity of emulsion-based pair conversion telescope for cosmic gamma-ray polarimetry

Linear polarization of high-energy gamma-rays (10 MeV-100 GeV) can be detected by measuring the azimuthal angle of electron-positron pairs and observing the modulation of the azimuthal distribution. To demonstrate the gamma-ray polarization sensitivity of emulsion, we conducted a test using a polarized gamma-ray beam at SPring-8/LEPS. Emulsion tracks were reconstructed using scanning data, and gamma-ray events were selected automatically. Using an optical microscope, out of the 2381 gamma-ray conversions that were observed, 1372 remained after event selection, on the azimuthal angle distribution of which we measured the modulation. From the distribution of the azimuthal angles of the selected events, a modulation factor of 0.21 + 0.11 - 0.09 was measured, from which the detection of a non-zero modulation was established with a significance of 3.06 $σ$. This attractive polarimeter will be applied to the GRAINE project, a balloon-borne experiment that observes cosmic gamma-rays with an emulsion-based pair conversion telescope.

astro-ph.IM↗

arXiv2012

Balloon-borne gamma-ray telescope with nuclear emulsion : overview and status

Detecting the first electron pairs with nuclear emulsion allows a precise measurement of the direction of incident gamma-rays as well as their polarization. With recent innovations in emulsion scanning, emulsion analyzing capability is becoming increasingly powerful. Presently, we are developing a balloon-borne gamma-ray telescope using nuclear emulsion. An overview and a status of our telescope is given.

astro-ph.IM↗