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579 results on '"Zhang, Jiazhen"'

2. Fast-MC-PET: A Novel Deep Learning-aided Motion Correction and Reconstruction Framework for Accelerated PET

Author

Zhou, Bo, Tsai, Yu-Jung, Zhang, Jiazhen, Guo, Xueqi, Xie, Huidong, Chen, Xiongchao, Miao, Tianshun, Lu, Yihuan, Duncan, James S., and Liu, Chi

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

Patient motion during PET is inevitable. Its long acquisition time not only increases the motion and the associated artifacts but also the patient's discomfort, thus PET acceleration is desirable. However, accelerating PET acquisition will result in reconstructed images with low SNR, and the image quality will still be degraded by motion-induced artifacts. Most of the previous PET motion correction methods are motion type specific that require motion modeling, thus may fail when multiple types of motion present together. Also, those methods are customized for standard long acquisition and could not be directly applied to accelerated PET. To this end, modeling-free universal motion correction reconstruction for accelerated PET is still highly under-explored. In this work, we propose a novel deep learning-aided motion correction and reconstruction framework for accelerated PET, called Fast-MC-PET. Our framework consists of a universal motion correction (UMC) and a short-to-long acquisition reconstruction (SL-Reon) module. The UMC enables modeling-free motion correction by estimating quasi-continuous motion from ultra-short frame reconstructions and using this information for motion-compensated reconstruction. Then, the SL-Recon converts the accelerated UMC image with low counts to a high-quality image with high counts for our final reconstruction output. Our experimental results on human studies show that our Fast-MC-PET can enable 7-fold acceleration and use only 2 minutes acquisition to generate high-quality reconstruction images that outperform/match previous motion correction reconstruction methods using standard 15 minutes long acquisition data., Comment: Accepted at Information Processing in Medical Imaging (IPMI 2023)

Published
2023

3. Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT

Author

Chen, Xiongchao, Zhou, Bo, Xie, Huidong, Guo, Xueqi, Zhang, Jiazhen, Sinusas, Albert J., Onofrey, John A., and liu, Chi

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

Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (u-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated two input modalities as an early feature fusion or extracted image features using two separate CNN modules for a late fusion. These methods do not fully extract or fuse the cross-modality information. Besides, deep-learning-based rigid registration of cardiac SPECT and CT-derived u-maps has not been investigated before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived u-maps. DuSFE fuses the knowledge from multiple modalities to recalibrate both channel-wise and spatial features for each modality. DuSFE can be embedded at multiple convolutional layers to enable feature fusion at different spatial dimensions. Our studies using clinical data demonstrated that a network embedded with DuSFE generated substantial lower registration errors and therefore more accurate AC SPECT images than previous methods., Comment: 10 pages, 4 figures, accepted at MICCAI 2022

Published
2022

10. Plant Tracer: A Program to Track and Quantify Plant Movement from Cellphone Captured Time-Lapse Movies

Author

Guercio, Angelica M., Mao, Yixiang, Carvalho, Victor N. D., Zhang, Jiazhen, Li, Changyuan, Ren, Zheng, Zhao, Winnie, Wang, Yao, and Brenner, Eric D.

Abstract

Despite the fundamental importance of plants to our very survival, student interest in plant biology is in decline as technology draws us further away from nature. Here we introduce "Plant Tracer" (http://www.planttracer.com), a Matlab-based program, which can quantify time-lapse videos of plant movement. We demonstrate that Plant Tracer can be used to distinguish altered movement qualities in the inflorescence (flowering) stem in the Arabidopsis "pgm-1" (phosphoglucomutase) mutant when compared to wildtype, providing a genetic platform for students to evaluate how plants sense and respond to gravity and circumnutation (the back-and-forth swaying of plant organs). We show that both gravitropism and circumnutation is diminished in the "pgm-1" mutant when compared to wildtype. In this way, "Plant Tracer" is a promising instructional tool for biology labs to quantify the genetics of plant movement using smartphones.

Published
2019

19. Cross-Attention for Improved Motion Correction in Brain PET

Author

Cai, Zhuotong, Zeng, Tianyi, Lieffrig, Eléonore V., Zhang, Jiazhen, Chen, Fuyao, Toyonaga, Takuya, You, Chenyu, Xin, Jingmin, Zheng, Nanning, Lu, Yihuan, Duncan, James S., Onofrey, John A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abdulkadir, Ahmed, editor, Bathula, Deepti R., editor, Dvornek, Nicha C., editor, Govindarajan, Sindhuja T., editor, Habes, Mohamad, editor, Kumar, Vinod, editor, Leonardsen, Esten, editor, Wolfers, Thomas, editor, and Xiao, Yiming, editor

Published
2023
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20. Fast Reconstruction for Deep Learning PET Head Motion Correction

Author

Zeng, Tianyi, Zhang, Jiazhen, Lieffrig, Eléonore V., Cai, Zhuotong, Chen, Fuyao, You, Chenyu, Naganawa, Mika, Lu, Yihuan, Onofrey, John A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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35. A Study on the Financial Situation of China's Retail Industry in the Context of the COVID-19 Epidemic

Author

Zhang, Jiazhen, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Jiang, Yushi, editor, Shvets, Yuriy, editor, and Mallick, Hrushikesh, editor

Published
2022
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36. Atlas-Based Semantic Segmentation of Prostate Zones

Author

Zhang, Jiazhen, Venkataraman, Rajesh, Staib, Lawrence H., Onofrey, John A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Linwei, editor, Dou, Qi, editor, Fletcher, P. Thomas, editor, Speidel, Stefanie, editor, and Li, Shuo, editor

Published
2022
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37. Supervised Deep Learning for Head Motion Correction in PET

Author

Zeng, Tianyi, Zhang, Jiazhen, Revilla, Enette, Lieffrig, Eléonore V., Fang, Xi, Lu, Yihuan, Onofrey, John A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Linwei, editor, Dou, Qi, editor, Fletcher, P. Thomas, editor, Speidel, Stefanie, editor, and Li, Shuo, editor

Published
2022
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