113 citations · 250 across the 7 of their papers we have counts for
7 papers
End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT
Yoseob Han, Dufan Wu, Kyungsang Kim +1
Objective: There exist several X-ray computed tomography (CT) scanning strategies to reduce a radiation dose, such as (1) sparse-view CT, (2) low-dose CT, and (3) region-of-interes…
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
Siyeop Yoon, Rui Hu, Yuang Wang +6
PET imaging is a powerful modality offering quantitative assessments of molecular and physiological processes. The necessity for PET denoising arises from the intrinsic high noise…
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
Sekeun Kim, Pengfei Jin, Cheng Chen +9
Despite achieving impressive results in general-purpose semantic segmentation with strong generalization on natural images, the Segment Anything Model (SAM) has shown less precisio…
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
Aoxiao Zhong, Xiang Li, Dufan Wu +17
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public…
MR-Based PET Attenuation Correction using a Combined Ultrashort Echo Time/Multi-Echo Dixon Acquisition
Paul Kyu Han, Debra E. Horng, Kuang Gong +7
We propose a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LAC) in positron emission tomography (PET) using a physical compartm…
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
Kuang Gong, Jaewon Yang, Kyungsang Kim +3
Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation corr…
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
Kuang Gong, Jiahui Guan, Kyungsang Kim +4
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and su…