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5. AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Author

Zhang, Kai, Danelljan, Martin, Li, Yawei, Timofte, Radu, Liu, Jie, Tang, Jie, Wu, Gangshan, Zhu, Yu, He, Xiangyu, Xu, Wenjie, Li, Chenghua, Leng, Cong, Cheng, Jian, Wu, Guangyang, Wang, Wenyi, Liu, Xiaohong, Zhao, Hengyuan, Kong, Xiangtao, He, Jingwen, Qiao, Yu, Dong, Chao, Luo, Xiaotong, Chen, Liang, Zhang, Jiangtao, Suin, Maitreya, Purohit, Kuldeep, Rajagopalan, A. N., Li, Xiaochuan, Lang, Zhiqiang, Nie, Jiangtao, Wei, Wei, Zhang, Lei, Muqeet, Abdul, Hwang, Jiwon, Yang, Subin, Kang, JungHeum, Bae, Sung-Ho, Kim, Yongwoo, Qu, Yanyun, Jeon, Geun-Woo, Choi, Jun-Ho, Kim, Jun-Hyuk, Lee, Jong-Seok, Marty, Steven, Marty, Eric, Xiong, Dongliang, Chen, Siang, Zha, Lin, Jiang, Jiande, Gao, Xinbo, Lu, Wen, Wang, Haicheng, Bhaskara, Vineeth, Levinshtein, Alex, Tsogkas, Stavros, Jepson, Allan, Kong, Xiangzhen, Zhao, Tongtong, Zhao, Shanshan, S, Hrishikesh P, Puthussery, Densen, C V, Jiji, Nan, Nan, Liu, Shuai, Cai, Jie, Meng, Zibo, Ding, Jiaming, Ho, Chiu Man, Wang, Xuehui, Yan, Qiong, Zhao, Yuzhi, Chen, Long, Sun, Long, Wang, Wenhao, Liu, Zhenbing, Lan, Rushi, Umer, Rao Muhammad, and Micheloni, Christian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

Published
2020

6. Multi-Attention Based Ultra Lightweight Image Super-Resolution

Author

Muqeet, Abdul, Hwang, Jiwon, Yang, Subin, Kang, Jung Heum, Kim, Yongwoo, and Bae, Sung-Ho

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Lightweight image super-resolution (SR) networks have the utmost significance for real-world applications. There are several deep learning based SR methods with remarkable performance, but their memory and computational cost are hindrances in practical usage. To tackle this problem, we propose a Multi-Attentive Feature Fusion Super-Resolution Network (MAFFSRN). MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block. Each FFG contains a stack of proposed multi-attention blocks (MAB) that are combined in a novel feature fusion structure. Further, the MAB with a cost-efficient attention mechanism (CEA) helps us to refine and extract the features using multiple attention mechanisms. The comprehensive experiments show the superiority of our model over the existing state-of-the-art. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively., Comment: ECCVW AIM2020

Published
2020

8. Fluid-Attenuated Inversion Recovery Hyperintense Ischemic Stroke Predicts Less Favorable 90-Day Outcome after Intravenous Thrombolysis.

Author

Kim, Yongwoo, Luby, Marie, Burkett, Nina-Serena, Norato, Gina, Leigh, Richard, Wright, Clinton, Kern, Kyle, Hsia, Amie, Lynch, John, Adil, Malik, and Latour, Lawrence

Subjects
Diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch, Fluid-attenuated inversion recovery positivity, Functional outcome, Stroke, Therapeutic thrombolysis, Administration, Intravenous, Diffusion Magnetic Resonance Imaging, Humans, Ischemic Stroke, Thrombolytic Therapy, Treatment Outcome
Abstract

INTRODUCTION: The absence of an ischemic lesion on MRI fluid-attenuated inversion recovery (FLAIR) is helpful in predicting stroke onset within 4.5 h. However, some ischemic strokes become visible on FLAIR within 4.5 h. We hypothesized that the early lesion visibility on FLAIR may predict stroke outcome 90 days after intravenous (IV) thrombolysis, independent of time. MATERIALS AND METHODS: We analyzed data from acute ischemic stroke patients presenting over the last 10 years who were screened with MRI and treated with IV thrombolysis within 4.5 h from onset. Three independent readers assessed whether ischemic lesions seen on diffusion-weighted imaging were also FLAIR positive based on visual inspection. Multivariable regression analyses were used to obtain an adjusted odds ratio of favorable clinical and radiological outcomes based on FLAIR positivity. RESULTS: Of 297 ischemic stroke patients, 25% had lesion visibility on initial FLAIR. The interrater agreement for the FLAIR positivity assessment was 84% (κ = 0.604, 95% CI: 0.557-0.652). Patients with FLAIR-positive lesions had more right hemispheric strokes (57 vs. 41%, p = 0.045), were imaged later (129 vs. 104 min, p = 0.036), and had less frequent favorable 90-day functional outcome (49 vs. 63%, p = 0.028), less frequent early neurologic improvement (30 vs. 58%, p = 0.001), and more frequent contrast extravasation to the cerebrospinal fluid space (44 vs. 26%, p = 0.008). CONCLUSIONS: Early development of stroke lesion on FLAIR within 4.5 h of onset is associated with reduced likelihood of favorable 90-day outcome after IV thrombolysis.

Published
2021

10. S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks

Author

Choi, Jae-Seok, Kim, Yongwoo, and Kim, Munchurl

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Recently, many deep-learning-based pan-sharpening methods have been proposed for generating high-quality pan-sharpened (PS) satellite images. These methods focused on various types of convolutional neural network (CNN) structures, which were trained by simply minimizing a spectral loss between network outputs and the corresponding high-resolution multi-spectral (MS) target images. However, due to different sensor characteristics and acquisition times, high-resolution panchromatic (PAN) and low-resolution MS image pairs tend to have large pixel misalignments, especially for moving objects in the images. Conventional CNNs trained with only the spectral loss with these satellite image datasets often produce PS images of low visual quality including double-edge artifacts along strong edges and ghosting artifacts on moving objects. In this letter, we propose a novel loss function, called a spectral-spatial structure (S3) loss, based on the correlation maps between MS targets and PAN inputs. Our proposed S3 loss can be very effectively utilized for pan-sharpening with various types of CNN structures, resulting in significant visual improvements on PS images with suppressed artifacts., Comment: Accepted for publication in IEEE Geoscience and Remote Sensing Letters

Published
2019
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13. PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

Author

Ignatov, Andrey, Timofte, Radu, Van Vu, Thang, Luu, Tung Minh, Pham, Trung X, Van Nguyen, Cao, Kim, Yongwoo, Choi, Jae-Seok, Kim, Munchurl, Huang, Jie, Ran, Jiewen, Xing, Chen, Zhou, Xingguang, Zhu, Pengfei, Geng, Mingrui, Li, Yawei, Agustsson, Eirikur, Gu, Shuhang, Van Gool, Luc, de Stoutz, Etienne, Kobyshev, Nikolay, Nie, Kehui, Zhao, Yan, Li, Gen, Tong, Tong, Gao, Qinquan, Hanwen, Liu, Michelini, Pablo Navarrete, Dan, Zhu, Fengshuo, Hu, Hui, Zheng, Wang, Xiumei, Deng, Lirui, Meng, Rang, Qin, Jinghui, Shi, Yukai, Wen, Wushao, Lin, Liang, Feng, Ruicheng, Wu, Shixiang, Dong, Chao, Qiao, Yu, Vasu, Subeesh, Madam, Nimisha Thekke, Kandula, Praveen, Rajagopalan, A. N., Liu, Jie, and Jung, Cheolkon

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.

Published
2018

17. Templated Assembly of Silk Fibroin for a Bio‐Feedstock‐Derived Heart Valve Leaflet (Adv. Funct. Mater. 14/2024)

Author

Choi, Woojin, primary, Choi, Moonhyun, additional, Jun, Taesuk, additional, Kim, Yongwoo, additional, Ryu, Du Yeol, additional, Kim, Nam Kyun, additional, Lee, Hye‐Jin, additional, Lee, Yoojin, additional, Lee, Seung Hyun, additional, Lee, Wonhwa, additional, Lee, Milae, additional, Kang, Keonwook, additional, Kwon, Jae‐Sung, additional, Jang, Jong Geol, additional, Ha, Hojin, additional, Choi, Jae Young, additional, Lim, Sungmin, additional, Lee, Sangmin, additional, Jung, Se Yong, additional, and Hong, Jinkee, additional

Published
2024
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18. VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results

Author

Du, Dawei, Wen, Longyin, Zhu, Pengfei, Fan, Heng, Hu, Qinghua, Ling, Haibin, Shah, Mubarak, Pan, Junwen, Axenopoulos, Apostolos, Schumann, Arne, Psaltis, Athanasios, Jain, Ayush, Dong, Bin, Li, Changlin, Chen, Chen, Duan, Chengzhen, Zhang, Chongyang, Stadler, Daniel, Pailla, Dheeraj Reddy, Yin, Dong, Khan, Faizan, Meng, Fanman, Gao, Guangyu, Zhang, Guosheng, Chen, Hansheng, Zhou, Hao, Xie, Haonian, Qiu, Heqian, Li, Hongliang, Athanasiadis, Ioannis, Cui, Jincai, Zhou, Jingkai, Ko, Jonghwan, Lee, Joochan, Yu, Jun, Yoo, Jungyeop, Sommer, Lars Wilko, Xiong, Lu, Schleiss, Michael, Yang, Ming-Hsuan, Liu, Mingyu, Zhang, Minjian, Mandal, Murari, Daras, Petros, Narang, Pratik, Liu, Qiong, Shi, Qiu, Lin, Qizhang, Ramaprasad, Rohit, Wang, Sai, Mehta, Sarvesh, Li, Shuai, Huang, Shuqin, Moon, Sungtae, Zhao, Taijin, Sun, Ting, Guo, Wei, Tian, Wei, Qin, Weida, Yu, Weiping, Lin, Wenxiang, Zhao, Xi, Jia, Xiaogang, He, Xin, Zhao, Xingjie, Liu, Xuanxin, Ding, Yan, Luo, Yan, Xiao, Yang, Wang, Yi, Liu, Yingjie, Kim, Yongwoo, Sun, Yu, Yao, Yuehan, Huang, Yuyao, Gong, Zehui, Xu, Zhenyu, Luo, Zhipeng, Cao, Zhiguo, Wei, Zhiwei, Fan, Zhongjie, Song, Zichen, Liu, Ziming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bartoli, Adrien, editor, and Fusiello, Andrea, editor

Published
2020
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19. A CNN-Based Multi-scale Super-Resolution Architecture on FPGA for 4K/8K UHD Applications

Author

Kim, Yongwoo, Choi, Jae-Seok, Lee, Jaehyup, Kim, Munchurl, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ro, Yong Man, editor, Cheng, Wen-Huang, editor, Kim, Junmo, editor, Chu, Wei-Ta, editor, Cui, Peng, editor, Choi, Jung-Woo, editor, Hu, Min-Chun, editor, and De Neve, Wesley, editor

Published
2020
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20. PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

Author

Ignatov, Andrey, Timofte, Radu, Van Vu, Thang, Luu, Tung Minh, Pham, Trung X, Van Nguyen, Cao, Kim, Yongwoo, Choi, Jae-Seok, Kim, Munchurl, Huang, Jie, Ran, Jiewen, Xing, Chen, Zhou, Xingguang, Zhu, Pengfei, Geng, Mingrui, Li, Yawei, Agustsson, Eirikur, Gu, Shuhang, Van Gool, Luc, de Stoutz, Etienne, Kobyshev, Nikolay, Nie, Kehui, Zhao, Yan, Li, Gen, Tong, Tong, Gao, Qinquan, Hanwen, Liu, Michelini, Pablo Navarrete, Dan, Zhu, Fengshuo, Hu, Hui, Zheng, Wang, Xiumei, Deng, Lirui, Meng, Rang, Qin, Jinghui, Shi, Yukai, Wen, Wushao, Lin, Liang, Feng, Ruicheng, Wu, Shixiang, Dong, Chao, Qiao, Yu, Vasu, Subeesh, Thekke Madam, Nimisha, Kandula, Praveen, Rajagopalan, A. N., Liu, Jie, Jung, Cheolkon, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Leal-Taixé, Laura, editor, and Roth, Stefan, editor

Published
2019
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23. Late lesion growth following endovascular therapy: is 24 hours too early to assess acute infarct size including the effects of secondary injury?

Author

Luby, Marie, primary, Hsia, Amie W., additional, Lomahan, Carolyn A., additional, Uche, Victoria, additional, Davis, Rachel, additional, Kim, Yongwoo, additional, Somani, Sana, additional, Burton, Shannon, additional, Cabatbat, Rainier, additional, Craft, Veronica, additional, De Vis, Jill B., additional, Adil, Malik M., additional, Afzal, Mariam M., additional, Thomas, Leila C., additional, Gandler, William, additional, McCreedy, Evan S., additional, Lynch, John K., additional, and Latour, Lawrence L., additional

Published
2024
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24. Abstract WMP64: Late Lesion Growth Following Endovascular Therapy: Is 24 Hours Too Early to Assess Acute Infarct Size Including the Effects of Secondary Injury?

Author

Luby, Marie, primary, Hsia, Amie W, additional, Lomahan, Carolyn A, additional, Uche, Victoria, additional, Davis, Rachel, additional, Kim, Yongwoo, additional, Somani, Sana, additional, Burton, Shannon, additional, Cabatbat, Rainier Michael, additional, Craft, Veronica, additional, De Vis, Jill, additional, Adil, Malik, additional, Afzal, Mariam, additional, Thomas, Leila, additional, Gandler, William, additional, McCreedy, Evan, additional, Lynch, John K, additional, and Latour, Lawrence, additional

Published
2024
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29. Templated Assembly of Silk Fibroin for a Bio‐Feedstock‐Derived Heart Valve Leaflet

Author

Choi, Woojin, primary, Choi, Moonhyun, additional, Jun, Taesuk, additional, Kim, Yongwoo, additional, Ryu, Du Yeol, additional, Kim, Nam Kyun, additional, Lee, Hye‐Jin, additional, Lee, Yoojin, additional, Lee, Seung Hyun, additional, Lee, Wonhwa, additional, Lee, Milae, additional, Kang, Keonwook, additional, Kwon, Jae‐Sung, additional, Jang, Jong Geol, additional, Ha, Hojin, additional, Choi, Jae Young, additional, Lim, Sungmin, additional, Lee, Sangmin, additional, Jung, Se Yong, additional, and Hong, Jinkee, additional

Published
2023
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31. Hybrid Bionic Nerve Interface for Application in Bionic Limbs

Author

Cho, Youngjun, primary, Jeong, Hyung Hwa, additional, Shin, Heejae, additional, Pak, Changsik John, additional, Cho, Jeongmok, additional, Kim, Yongwoo, additional, Kim, Donggeon, additional, Kim, Taehyeon, additional, Kim, Hoijun, additional, Kim, Sohee, additional, Kwon, Soonchul, additional, Hong, Joon Pio, additional, Suh, Hyunsuk Peter, additional, and Lee, Sanghoon, additional

Published
2023
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35. AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Author

Zhang, Kai, primary, Danelljan, Martin, additional, Li, Yawei, additional, Timofte, Radu, additional, Liu, Jie, additional, Tang, Jie, additional, Wu, Gangshan, additional, Zhu, Yu, additional, He, Xiangyu, additional, Xu, Wenjie, additional, Li, Chenghua, additional, Leng, Cong, additional, Cheng, Jian, additional, Wu, Guangyang, additional, Wang, Wenyi, additional, Liu, Xiaohong, additional, Zhao, Hengyuan, additional, Kong, Xiangtao, additional, He, Jingwen, additional, Qiao, Yu, additional, Dong, Chao, additional, Luo, Xiaotong, additional, Chen, Liang, additional, Zhang, Jiangtao, additional, Suin, Maitreya, additional, Purohit, Kuldeep, additional, Rajagopalan, A. N., additional, Li, Xiaochuan, additional, Lang, Zhiqiang, additional, Nie, Jiangtao, additional, Wei, Wei, additional, Zhang, Lei, additional, Muqeet, Abdul, additional, Hwang, Jiwon, additional, Yang, Subin, additional, Kang, JungHeum, additional, Bae, Sung-Ho, additional, Kim, Yongwoo, additional, Qu, Yanyun, additional, Jeon, Geun-Woo, additional, Choi, Jun-Ho, additional, Kim, Jun-Hyuk, additional, Lee, Jong-Seok, additional, Marty, Steven, additional, Marty, Eric, additional, Xiong, Dongliang, additional, Chen, Siang, additional, Zha, Lin, additional, Jiang, Jiande, additional, Gao, Xinbo, additional, Lu, Wen, additional, Wang, Haicheng, additional, Bhaskara, Vineeth, additional, Levinshtein, Alex, additional, Tsogkas, Stavros, additional, Jepson, Allan, additional, Kong, Xiangzhen, additional, Zhao, Tongtong, additional, Zhao, Shanshan, additional, Hrishikesh, P. S., additional, Puthussery, Densen, additional, Jiji, C. V., additional, Nan, Nan, additional, Liu, Shuai, additional, Cai, Jie, additional, Meng, Zibo, additional, Ding, Jiaming, additional, Ho, Chiu Man, additional, Wang, Xuehui, additional, Yan, Qiong, additional, Zhao, Yuzhi, additional, Chen, Long, additional, Sun, Long, additional, Wang, Wenhao, additional, Liu, Zhenbing, additional, Lan, Rushi, additional, Umer, Rao Muhammad, additional, and Micheloni, Christian, additional

Published
2020
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37. VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results

Author

Du, Dawei, primary, Wen, Longyin, additional, Zhu, Pengfei, additional, Fan, Heng, additional, Hu, Qinghua, additional, Ling, Haibin, additional, Shah, Mubarak, additional, Pan, Junwen, additional, Axenopoulos, Apostolos, additional, Schumann, Arne, additional, Psaltis, Athanasios, additional, Jain, Ayush, additional, Dong, Bin, additional, Li, Changlin, additional, Chen, Chen, additional, Duan, Chengzhen, additional, Zhang, Chongyang, additional, Stadler, Daniel, additional, Pailla, Dheeraj Reddy, additional, Yin, Dong, additional, Khan, Faizan, additional, Meng, Fanman, additional, Gao, Guangyu, additional, Zhang, Guosheng, additional, Chen, Hansheng, additional, Zhou, Hao, additional, Xie, Haonian, additional, Qiu, Heqian, additional, Li, Hongliang, additional, Athanasiadis, Ioannis, additional, Cui, Jincai, additional, Zhou, Jingkai, additional, Ko, Jonghwan, additional, Lee, Joochan, additional, Yu, Jun, additional, Yoo, Jungyeop, additional, Sommer, Lars Wilko, additional, Xiong, Lu, additional, Schleiss, Michael, additional, Yang, Ming-Hsuan, additional, Liu, Mingyu, additional, Zhang, Minjian, additional, Mandal, Murari, additional, Daras, Petros, additional, Narang, Pratik, additional, Liu, Qiong, additional, Shi, Qiu, additional, Lin, Qizhang, additional, Ramaprasad, Rohit, additional, Wang, Sai, additional, Mehta, Sarvesh, additional, Li, Shuai, additional, Huang, Shuqin, additional, Moon, Sungtae, additional, Zhao, Taijin, additional, Sun, Ting, additional, Guo, Wei, additional, Tian, Wei, additional, Qin, Weida, additional, Yu, Weiping, additional, Lin, Wenxiang, additional, Zhao, Xi, additional, Jia, Xiaogang, additional, He, Xin, additional, Zhao, Xingjie, additional, Liu, Xuanxin, additional, Ding, Yan, additional, Luo, Yan, additional, Xiao, Yang, additional, Wang, Yi, additional, Liu, Yingjie, additional, Kim, Yongwoo, additional, Sun, Yu, additional, Yao, Yuehan, additional, Huang, Yuyao, additional, Gong, Zehui, additional, Xu, Zhenyu, additional, Luo, Zhipeng, additional, Cao, Zhiguo, additional, Wei, Zhiwei, additional, Fan, Zhongjie, additional, Song, Zichen, additional, and Liu, Ziming, additional

Published
2020
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46. MASCAR: Multidomain Adaptive Spatial–Spectral Variable Compression Artifact Removal Network for Multispectral Remote Sensing Images

Author

Kim, Jaemyung, Kim, Hyun-Ho, Seo, Doo-Chun, Jeong, Jae-Heon, Kang, Jin-Ku, and Kim, Yongwoo

Abstract

In remote sensing environments, image compression is essential to efficiently transmit and store high-resolution images due to the limited bandwidth and storage capacity. However, compression often leads to image quality degradation, requiring compression artifact removal technology in the postprocessing stage. Although deep neural networks have shown remarkable performance in image restoration, most existing methods have not adequately considered the compression conditions specific to remote sensing environments and have been evaluated primarily on synthetic datasets. To solve these issues, we propose a multidomain adaptive spatial–spectral variable compression artifact removal network (MASCAR) that effectively restores the earth surface details of compressed images in remote sensing environments. We introduce a multidomain local-patch collaborative learning strategy that extracts diverse features by decomposing the input local patch into different domains. In addition, we propose a detail focusing approach to direct the network’s focus toward fine-texture detail restoration and ensure stable training of remote sensing images with significant deviations in pixel distribution of local patches. Furthermore, a detail enhancement approach is presented to enhance the details of the restored images. Moreover, we propose an incorporated compressed image quality adaptation mechanism to respond flexibly to unknown compression ratios in remote sensing environments. The performance of MASCAR applied with the proposed method is evaluated on synthetic and real-world remote sensing datasets. Experimental results demonstrate that the proposed method has better quantitative performance and visual quality than existing methods.

Published
2024
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47. The TCD hyperemia index to detect vasospasm and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.

Author

Kim, Yongwoo, Mehta, Amit, Nair, Reshmi, Felbaum, Daniel R., Mai, Jeffrey C., Armonda, Rocco A., and Chang, Jason J.

Subjects
*CEREBRAL vasospasm, *CEREBRAL ischemia, *SUBARACHNOID hemorrhage, *HYPEREMIA, *RECEIVER operating characteristic curves, *CEREBRAL arteriovenous malformations, *INTERNAL carotid artery
Abstract

Background and Purpose: Elevated mean flow velocity (MFV) on transcranial Doppler (TCD) is used to predict vasospasm after aneurysmal subarachnoid hemorrhage (SAH). Hyperemia should be considered when observing elevated MFV. Lindegaard ratio (LR) is commonly used but does not enhance predictive values. We introduce a new marker, the hyperemia index (HI), calculated as bilateral extracranial internal carotid artery MFV divided by initial flow velocity. Methods: We evaluated SAH patients hospitalized ≥7 days between December 1, 2016 and June 30, 2022. We excluded patients with nonaneurysmal SAH, inadequate TCD windows, and baseline TCD obtained after 96 hours from onset. Logistic regression was conducted to assess the significant associations of HI, LR, and maximal MFV with vasospasm and delayed cerebral ischemia (DCI). Receiver operating characteristic analyses were employed to find the optimal cutoff value for HI. Results: Lower HI (odds ratio [OR] 0.10, 95% confidence interval [CI] 0.01‐0.68), higher MFV (OR 1.03, 95% CI 1.01‐1.05), and LR (OR 2.02, 95% CI 1.44‐2.85) were associated with vasospasm and DCI. Area under the curve (AUC) for predicting vasospasm was 0.70 (95% CI 0.58‐0.82) for HI, 0.87 (95% CI 0.81‐0.94) for maximal MFV, and 0.87 (95% CI 0.79‐0.94) for LR. The optimal cutoff value for HI was 1.2. Combining HI <1.2 with MFV improved positive predictive value without altering the AUC value. Conclusions: Lower HI was associated with a higher likelihood of vasospasm and DCI. HI <1.2 may serve as a useful TCD parameter to indicate vasospasm and DCI when elevated MFV is observed, or when transtemporal windows are inadequate. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Abstract TP84: Ring-like Pattern On Post-EVT MRI Is A Marker Of Perihematomal Edema

Author

Hsia, Amie W, primary, Luby, Marie L, additional, Lomahan, Carolyn, additional, Davis, Rachel, additional, Lynch, John K, additional, Adil, Malik, additional, Burton, Shannon P, additional, Kim, Yongwoo, additional, Craft, Veronica, additional, Uche, Victoria, additional, Cabatbat, Rainier Michael, additional, Thomas, Leila, additional, Afzal, Mariam, additional, and Latour, Lawrence, additional

Published
2023
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50. Abstract TMP61: Lesion Growth Continues Beyond 24 Hours Following Endovascular Therapy In Some Patients With Successful Recanalization

Author

Lomahan, Carolyn, primary, Hsia, Amie W, additional, Davis, Rachel, additional, Adil, Malik, additional, Kim, Yongwoo, additional, Lynch, John K, additional, Burton, Shannon, additional, Cabatbat, Rainier Michael, additional, Craft, Veronica, additional, De Vis, Jill, additional, Thomas, Leila, additional, Uche, Victoria, additional, Afzal, Mariam, additional, Latour, Lawrence, additional, and Luby, Marie L, additional

Published
2023
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