Showing 1–9 of 9 results for author: Ozaki, S

Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models

Authors: Sho Ozaki, Shizuo Kaji, Toshikazu Imae, Kanabu Nawa, Hideomi Yamashita, Keiichi Nakagawa

Abstract: 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… ▽ More 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. △ Less

Submitted 12 September, 2024; v1 submitted 6 August, 2024; originally announced August 2024.

Comments: 20 pages, 10 figures

Current-readout technique for ultra-high-rate experiments

Authors: Maki Wakata, Shoei Akamatsu, Takuhiro Fujiie, Taisei Furuyama, Lisa Hara, Yumi Ishikawa, Tadashi Ito, Takahiro Kikuchi, Tsutomu Mibe, Sachi Ozaki, Mitsuhiko Yokomizo, Jiro Murata

Abstract: This study developed a new current-readout technique capable of handling measurements with high count rates reaching 1 Gcps. By directly capturing the output current of a photomultiplier as a digitized waveform, we estimated event rates, overcoming the limitations imposed by pulse pileup constraints and deadtimes. This innovative method was applied to a muon spin rotation/relaxation/resonance expe… ▽ More This study developed a new current-readout technique capable of handling measurements with high count rates reaching 1 Gcps. By directly capturing the output current of a photomultiplier as a digitized waveform, we estimated event rates, overcoming the limitations imposed by pulse pileup constraints and deadtimes. This innovative method was applied to a muon spin rotation/relaxation/resonance experiment at the Japan Proton Accelerator Research Complex, demonstrating its anticipated performance. Furthermore, we explored methods for estimating statistical uncertainty and investigated potential applications in analog-logic OR/AND gates. Overall, our findings reveal that the developed technique opens up avenues for the development of future non-binary logic circuits operating based on n-adic numbers. △ Less

Submitted 17 January, 2025; v1 submitted 10 June, 2024; originally announced June 2024.

Elucidation of Unique Developmental Mechanism of Storm Surge along Northern Coast of Kyushu Island, Japan

Authors: Shinichiro Ozaki, Yoshihiko Ide, Masaki Niimi, Masaru Yamashiro, Mitsuyoshi Kodama

Abstract: Along the northern coast of Kyushu Island, significant storm surges were unlikely to occur because the strong wind does not blow directly to the coast when typhoons passes. However, during Typhoon Maysak, various areas along the coast experienced flooding due to the storm surges. Additionally, inundation occurred when the typhoon was more than 600 km away from the coast. In this study, we classifi… ▽ More Along the northern coast of Kyushu Island, significant storm surges were unlikely to occur because the strong wind does not blow directly to the coast when typhoons passes. However, during Typhoon Maysak, various areas along the coast experienced flooding due to the storm surges. Additionally, inundation occurred when the typhoon was more than 600 km away from the coast. In this study, we classified the past typhoons into northeastward-moving, northward-moving and direct-passing overhead types and analyzed the storm surge using observational data and numerical simulations. Regarding northeastward-moving types, Hakata Bay located in the coast experienced two surge peaks. The first peak was induced by the inverted barometer effect and the stagnation of seawater in the Tsushima Strait. The second peak occurred because of the 10-hour oscillation and Ekman transport in the Tsushima Strait. For northward-moving types, Ekman transport was further intensified, resulting in a high storm surge that lasted for more than 10 hours. Regarding directly passing overhead types, one or two peaks occurred in a short period during the closest approach. The first peak was caused by the inverted barometer effect and Ekman transport, whereas the second peak was caused by the 2-hour harbor oscillation in Hakata Bay. △ Less

Submitted 17 April, 2024; originally announced April 2024.

Comments: 33 pages, 25 figures

Solvable toy model of negative energetic elasticity

Authors: Atsushi Iwaki, Soshun Ozaki

Abstract: Recent experiments have established negative energetic elasticity, the negative contribution of energy to the elastic modulus, as a universal property of polymer gels. To reveal the microscopic origin of this phenomenon, Shirai and Sakumichi investigated a polymer model on a cubic lattice with the energy effect from the solvent in finite-size calculations [Phys. Rev. Lett. 130, 148101 (2023)]. Mot… ▽ More Recent experiments have established negative energetic elasticity, the negative contribution of energy to the elastic modulus, as a universal property of polymer gels. To reveal the microscopic origin of this phenomenon, Shirai and Sakumichi investigated a polymer model on a cubic lattice with the energy effect from the solvent in finite-size calculations [Phys. Rev. Lett. 130, 148101 (2023)]. Motivated by this work, we provide a simple platform to study the elasticity of polymer chains by considering a one-dimensional random walk with the energy effect. This model can be mapped onto the classical Ising chain, leading to an exact form of the free energy in the thermodynamic or continuous limit. Our analytical results are qualitatively consistent with Shirai and Sakumichi's work. Our model serves as a fundamental benchmark for studying negative energetic elasticity. △ Less

Submitted 24 September, 2024; v1 submitted 10 April, 2024; originally announced April 2024.

Comments: 7 pages, 5 figures

Journal ref: Phys. Rev. E 110, L032102 (2024)

sparse-ir: optimal compression and sparse sampling of many-body propagators

Authors: Markus Wallerberger, Samuel Badr, Shintaro Hoshino, Fumiya Kakizawa, Takashi Koretsune, Yuki Nagai, Kosuke Nogaki, Takuya Nomoto, Hitoshi Mori, Junya Otsuki, Soshun Ozaki, Rihito Sakurai, Constanze Vogel, Niklas Witt, Kazuyoshi Yoshimi, Hiroshi Shinaoka

Abstract: We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, a central object in finite-temperature quantum many-body calculations. We leverage two concepts: firstly, the intermediate representation (IR), an optimal compression of the propagator with robust a-priori error estimates, and secondly, sparse sampling, near-optimal grids in imaginary time and imagi… ▽ More We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, a central object in finite-temperature quantum many-body calculations. We leverage two concepts: firstly, the intermediate representation (IR), an optimal compression of the propagator with robust a-priori error estimates, and secondly, sparse sampling, near-optimal grids in imaginary time and imaginary frequency from which the propagator can be reconstructed and on which diagrammatic equations can be solved. IR and sparse sampling are packaged into stand-alone, easy-to-use Python, Julia and Fortran libraries, which can readily be included into existing software. We also include an extensive set of sample codes showcasing the library for typical many-body and ab initio methods. △ Less

Submitted 23 June, 2022; originally announced June 2022.

Comments: 8 pages, 4 figures

Journal ref: SoftwareX 21, 101266 (2023)

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

Authors: Sho Ozaki, Shizuo Kaji, Kanabu Nawa, Toshikazu Imae, Atsushi Aoki, Takahiro Nakamoto, Takeshi Ohta, Yuki Nozawa, Hideomi Yamashita, Akihiro Haga, Keiichi Nakagawa

Abstract: 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… ▽ More 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?" △ Less

Submitted 5 April, 2022; v1 submitted 12 July, 2021; originally announced July 2021.

Comments: 3+27 pages, 13 figures, version published in Medical Physics

Fast Statistical Iterative Reconstruction for MVCT in TomoTherapy

Authors: Sho Ozaki, Akihiro Haga, Edward Chao, Calvin Maurer, Kanabu Nawa, Takeshi Ohta, Takahiro Nakamoto, Yuki Nozawa, Taiki Magome, Masahiro Nakano, Keiichi Nakagawa

Abstract: 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… ▽ More 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. △ Less

Submitted 24 March, 2019; originally announced March 2019.

Comments: 11 pages, 4 figures

Visual enhancement of Cone-beam CT by use of CycleGAN

Authors: S. Kida, S. Kaji, K. Nawa, T. Imae, T. Nakamoto, S. Ozaki, T. Ohta, Y. Nozawa, K. Nakagawa

Abstract: Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. CBCT has found a wide variety of applications in patient positioning for image-guided radiation therapy, extracting radiomic information for designing patient-specific treatment, and computing fractional dose distributions for adaptive radiatio… ▽ More Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. CBCT has found a wide variety of applications in patient positioning for image-guided radiation therapy, extracting radiomic information for designing patient-specific treatment, and computing fractional dose distributions for adaptive radiation therapy. However, CBCT images suffer from low soft-tissue contrast, noise, and artifacts compared to conventional fan-beam CT images. Therefore, it is essential to improve the image quality of CBCT. In this paper, we propose a synthetic approach to translate CBCT images with deep neural networks. Our method requires only unpaired and unaligned CBCT images and planning fan-beam CT (PlanCT) images for training. Once trained, 3D reconstructed CBCT images can be directly translated to high-quality PlanCT-like images. We demonstrate the effectiveness of our method with images obtained from 24 prostate patients, and we provide a statistical and visual comparison. The image quality of the translated images shows substantial improvement in voxel values, spatial uniformity, and artifact suppression compared to those of the original CBCT. The anatomical structures of the original CBCT images were also well preserved in the translated images. Our method enables more accurate adaptive radiation therapy, and opens up new applications for CBCT that hinge on high-quality images. △ Less

Submitted 25 November, 2019; v1 submitted 17 January, 2019; originally announced January 2019.

Development of a novel scintillation-trigger detector for the MTV experiment using aluminum-metallized film tapes

Authors: S. Tanaka, S. Ozaki, Y. Sakamoto, R. Tanuma, T. Yoshida, J. Murata

Abstract: A new type of a trigger-scintillation counter array designed for the MTV experiment at TRIUMF-ISAC has been developed, which uses aluminum-metallized film tape for wrapping to achieve the required assembling precision of $\pm$0.5 mm. The MTV experiment uses a cylindrical drift chamber (CDC) as the main electron-tracking detector. The barrel-type trigger counter is placed inside the CDC to generate… ▽ More A new type of a trigger-scintillation counter array designed for the MTV experiment at TRIUMF-ISAC has been developed, which uses aluminum-metallized film tape for wrapping to achieve the required assembling precision of $\pm$0.5 mm. The MTV experiment uses a cylindrical drift chamber (CDC) as the main electron-tracking detector. The barrel-type trigger counter is placed inside the CDC to generate a trigger signal using 1 mm thick, 300 mm long thin plastic scintillation counters. Detection efficiency and light attenuation compared with conventional wrapping materials are studied. △ Less

Submitted 13 March, 2014; v1 submitted 20 January, 2014; originally announced January 2014.

Comments: to be published in Nuclear Instruments and Methods A

Journal ref: Nuclear Instruments and Methods in Physics Research A 752 (2014) 47-53