Jongduk Baek
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View article: Shielding Si Armed with CoSi <sub>2</sub> Nanoplates and Dual‐Carbon Shells: 3D Porous Microspheres as High‐Performance Anodes for Li‐Ion Batteries
Shielding Si Armed with CoSi <sub>2</sub> Nanoplates and Dual‐Carbon Shells: 3D Porous Microspheres as High‐Performance Anodes for Li‐Ion Batteries Open
In this study, a novel multi‐core–dual‐shell strategy is employed to synthesize 3D porous microspheres. These microspheres consist of Si armed with CoSi 2 nanoplates multi‐cores encapsulated within dual protective shells of metallic‐Co nan…
View article: Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM
Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM Open
Recently, multimodal large language models (MLLMs) have emerged as a key approach in achieving artificial general intelligence. In particular, vision-language MLLMs have been developed to generate not only text but also visual outputs from…
View article: Standardization of Lung CT Number Using COPD Gene2 Phantom Under Various Scanning Protocols
Standardization of Lung CT Number Using COPD Gene2 Phantom Under Various Scanning Protocols Open
Lung computed tomography (CT) images are widely used to diagnose chronic obstructive pulmonary disease (COPD) by evaluating signs of lung tissue destruction. Accurate diagnosis requires standardizing the CT numbers in lung CT images to dis…
View article: Continuous representation‐based reconstruction for computed tomography
Continuous representation‐based reconstruction for computed tomography Open
Background Computed tomography (CT) imaging has been developed to acquire a higher resolution image for detecting early‐stage lesions. However, the lack of spatial resolution of CT images is still a limitation to fully utilize the capabili…
View article: X‐ray CT metal artifact reduction using neural attenuation field prior
X‐ray CT metal artifact reduction using neural attenuation field prior Open
Background The presence of metal objects in computed tomography (CT) imaging introduces severe artifacts that degrade image quality and hinder accurate diagnosis. While several deep learning‐based metal artifact reduction (MAR) methods hav…
View article: Dreamweaver: Learning Compositional World Models from Pixels
Dreamweaver: Learning Compositional World Models from Pixels Open
Humans have an innate ability to decompose their perceptions of the world into objects and their attributes, such as colors, shapes, and movement patterns. This cognitive process enables us to imagine novel futures by recombining familiar …
View article: Low-dose computed tomography perceptual image quality assessment
Low-dose computed tomography perceptual image quality assessment Open
In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered…
View article: Iterative reconstruction for limited-angle CT using implicit neural representation
Iterative reconstruction for limited-angle CT using implicit neural representation Open
Objective. Limited-angle computed tomography (CT) presents a challenge due to its ill-posed nature. In such scenarios, analytical reconstruction methods often exhibit severe artifacts. To tackle this inverse problem, several supervised dee…
View article: Carbon nanotube-based multiple source C-arm CT system: feasibility study with prototype system
Carbon nanotube-based multiple source C-arm CT system: feasibility study with prototype system Open
To extend the field of view while reducing dimensions of the C-arm, we propose a carbon nanotube (CNT)-based C-arm computed tomography (CT) system with multiple X-ray sources. A prototype system was developed using three CNT X-ray sources,…
View article: Impact of using sinogram domain data in the super‐resolution of CT images on diagnostic information
Impact of using sinogram domain data in the super‐resolution of CT images on diagnostic information Open
Background In recent times, deep‐learning‐based super‐resolution (DL‐SR) techniques for computed tomography (CT) images have shown outstanding results in terms of full‐reference image quality (FR‐IQ) metrics (e.g., root mean square error a…
View article: Power-law spectrum-based objective function to train a generative adversarial network with transfer learning for the synthetic breast CT image
Power-law spectrum-based objective function to train a generative adversarial network with transfer learning for the synthetic breast CT image Open
Objective. This paper proposes a new objective function to improve the quality of synthesized breast CT images generated by the GAN and compares the GAN performances on transfer learning datasets from different image domains. Approach. The…
View article: Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images
Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images Open
Objective. In this work, we propose a convolutional neural network (CNN)-based multi-slice ideal model observer using transfer learning (TL-CNN) to reduce the required number of training samples. Approach. To train model observers, we gene…
View article: Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023) Open
Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition…
View article: Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023) Open
Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition…
View article: Convolutional neural network‐based model observer for signal known statistically task in breast tomosynthesis images
Convolutional neural network‐based model observer for signal known statistically task in breast tomosynthesis images Open
Background Since human observer studies are resource‐intensive, mathematical model observers are frequently used to assess task‐based image quality. The most common implementation of these model observers assume that the signal information…
View article: Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique
Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique Open
Helical computed tomography (CT) scans are often performed to obtain three-dimensional images of an object that is longer than the detector. However, the existing quasi-exact and exact reconstruction methods, such as re-binning and Katsevi…
View article: Deep learning‐based multimodal fusion network for segmentation and classification of breast cancers using B‐mode and elastography ultrasound images
Deep learning‐based multimodal fusion network for segmentation and classification of breast cancers using B‐mode and elastography ultrasound images Open
Ultrasonography is one of the key medical imaging modalities for evaluating breast lesions. For differentiating benign from malignant lesions, computer‐aided diagnosis (CAD) systems have greatly assisted radiologists by automatically segme…
View article: No-reference perceptual CT image quality assessment based on a self-supervised learning framework
No-reference perceptual CT image quality assessment based on a self-supervised learning framework Open
Accurate image quality assessment (IQA) is crucial to optimize computed tomography (CT) image protocols while keeping the radiation dose as low as reasonably achievable. In the medical domain, IQA is based on how well an image provides a u…
View article: Sparsier2Sparse: weakly supervised learning for streak artifact reduction with unpaired sparse-view CT data
Sparsier2Sparse: weakly supervised learning for streak artifact reduction with unpaired sparse-view CT data Open
Sparse-view computed tomography (CT) becomes a major concern in the medical imaging field due to its reduced X-ray radiation dose. Recently, various convolutional neural network (CNN)-based approaches have been proposed, requiring the pair…
View article: Effect of optical blurring of X-ray source on breast tomosynthesis image quality: Modulation transfer function, anatomical noise power spectrum, and signal detectability perspectives
Effect of optical blurring of X-ray source on breast tomosynthesis image quality: Modulation transfer function, anatomical noise power spectrum, and signal detectability perspectives Open
We investigated the effect of the optical blurring of X-ray source on digital breast tomosynthesis (DBT) image quality using well-designed DBT simulator and table-top experimental systems. To measure the in-plane modulation transfer functi…
View article: Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images
Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images Open
In digital breast tomosynthesis (DBT) systems, projection data are acquired from a limited number of angles. Consequently, the reconstructed images contain severe blurring artifacts that might heavily degrade the DBT image quality and caus…
View article: A Methodology to Train a Convolutional Neural Network-Based Low-Dose CT Denoiser With an Accurate Image Domain Noise Insertion Technique
A Methodology to Train a Convolutional Neural Network-Based Low-Dose CT Denoiser With an Accurate Image Domain Noise Insertion Technique Open
To mitigate the public health risks associated with the increasing utilization of computed tomography (CT), it is desirable to implement a low-dose scanning protocol. However, low-dose CT produces poor image quality due to the increased qu…
View article: Perceptual CT Loss: Implementing CT Image Specific Perceptual Loss for CNN-Based Low-Dose CT Denoiser
Perceptual CT Loss: Implementing CT Image Specific Perceptual Loss for CNN-Based Low-Dose CT Denoiser Open
Convolutional neural network (CNN)-based denoisers have been successful in low-dose CT (LDCT) denoising tasks. However, image blurring in the denoised images remains a problem, and it is mainly caused by pixel-level losses during the train…
View article: A metal artifact reduction method for small field of view CT imaging
A metal artifact reduction method for small field of view CT imaging Open
Several sinogram inpainting based metal artifact reduction (MAR) methods have been proposed to reduce metal artifact in CT imaging. The sinogram inpainting method treats metal trace regions as missing data and estimates the missing informa…
View article: Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection
Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection Open
For digital breast tomosynthesis (DBT) systems, we investigate the effects of the reconstruction filters for different data acquisition angles on signal detection. We simulated a breast phantom with a 30% volume glandular fraction (VGF) of…
View article: A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data
A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data Open
Various imaging parameters in X-ray computed tomography (CT) should be examined and optimized by task-based assessment of human observer performance. Recently, convolutional neural networks (CNNs) have been introduced as anthropomorphic mo…
View article: A metal artifact reduction method for small field of view CT imaging
A metal artifact reduction method for small field of view CT imaging Open
To reduce metal artifacts, several sinogram inpainting-based metal artifact reduction (MAR) methods have been proposed where projection data within the metal trace region of the sinogram are treated as missing and subsequently estimated. H…
View article: A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images
A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images Open
Purpose Convolutional neural network (CNN)‐based image denoising techniques have shown promising results in low‐dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel‐level loss f…
View article: GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images Open
We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and independent of t…
View article: Multi-pass approach to reduce cone-beam artifacts in a circular orbit cone-beam CT system
Multi-pass approach to reduce cone-beam artifacts in a circular orbit cone-beam CT system Open
We propose a multi-pass approach to reduce cone-beam artifacts in a circular orbit cone-beam computed tomography (CT) system. Employing a large 2D detector array reduces the scan time but produces cone-beam artifacts in the Feldkamp, Davis…