Zobrazeno 1 - 10 of 70 pro vyhledávání: '"Yiping P Du"'
1
Akademický článek
Whole brain multiparametric mapping in two minutes using a dual-flip-angle stack-of-stars blipped multi-gradient-echo acquisition
Autor: Wenlong Feng, Zekang Ding, Quan Chen, Huajun She, Yiping P. Du
Publikováno v: NeuroImage, Vol 297, Iss , Pp 120689- (2024)
A new MRI technique is presented for three-dimensional fast simultaneous whole brain mapping of myelin water fraction (MWF), T1, proton density (PD), R2*, magnetic susceptibility (QSM), and B1 transmit field (B1+). Phantom and human (N = 9) datasets
Externí odkaz: https://doaj.org/article/3055f0deedd940a6845e5c2fa8d0889c
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2
Akademický článek
Risky decisions and their consequences: neural processing by boys with Antisocial Substance Disorder.
Autor: Thomas J Crowley, Manish S Dalwani, Susan K Mikulich-Gilbertson, Yiping P Du, Carl W Lejuez, Kristen M Raymond, Marie T Banich
Publikováno v: PLoS ONE, Vol 5, Iss 9, p e12835 (2010)
Adolescents with conduct and substance problems ("Antisocial Substance Disorder" (ASD)) repeatedly engage in risky antisocial and drug-using behaviors. We hypothesized that, during processing of risky decisions and resulting rewards and punishments,
Externí odkaz: https://doaj.org/article/466fc131693a444a81ce2a7c5804f3fb
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3
Low-Rank Tensor Subspace Decomposition With Weighted Group Sparsity for the Acceleration of Non-Cartesian Dynamic MRI
Autor: Bei Liu, Zekang Ding, Yufei Zhang, Huajun She, Yiping P. Du
Publikováno v: IEEE Transactions on Biomedical Engineering. 70:681-693
Dynamic MR imaging often requires long scan time, and acceleration of data acquisition is highly desirable in clinical applications.We proposed a Low-rank Tensor subspace decomposition with Weighted Group Sparsity (LTWGS) algorithm for non-Cartesian
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b068fdf5a847e844ad68dd1573b6893f
https://doi.org/10.1109/tbme.2022.3200709
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4
Dynamic pulmonary MRI using motion‐state weighted motion‐compensation ( MostMoCo ) reconstruction with ultrashort TE : A structural and functional study
Autor: Zekang Ding, Zenghui Cheng, Huajun She, Bei Liu, Yongfang Yin, Yiping P. Du
Publikováno v: Magnetic Resonance in Medicine. 88:224-238
To improve the quality of structural images and the quantification of ventilation in free-breathing dynamic pulmonary MRI.A 3D radial ultrashort TE (UTE) sequence with superior-inferior navigators was used to acquire pulmonary data during free breath
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7a8c572c454befed62333e3ff819cc2
https://doi.org/10.1002/mrm.29204
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5
Accelerating multi-echo chemical shift encoded water-fat MRI using model-guided deep learning
Autor: Shuo Li, Chenfei Shen, Zekang Ding, Huajun She, Yiping P. Du
Publikováno v: Magnetic resonance in medicineREFERENCES. 88(4)
To accelerate chemical shift encoded (CSE) water-fat imaging by applying a model-guided deep learning water-fat separation (MGDL-WF) framework to the undersampled k-space data.A model-guided deep learning water-fat separation framework is proposed fo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c124b1ad1e444a84d4e3bfcde04ea64f
https://pubmed.ncbi.nlm.nih.gov/35649172
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7
Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI
Autor: Zhijun Wang, Huajun She, Yufei Zhang, Yiping P. Du
Publikováno v: Medical image analysis. 84
Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical and physiological limitations. The spatial-temporal dictionary learning (DL) approach accelerates dynamic MRI by learning spatial-temporal correlations, but the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd6c3b18fb6557f2798ff53d7833dc51
https://pubmed.ncbi.nlm.nih.gov/36470148
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8
Dynamic MRI of the abdomen using parallel non-Cartesian convolutional recurrent neural networks
Autor: Yufei Zhang, Huajun She, Yiping P. Du
Publikováno v: Magnetic resonance in medicineREFERENCES. 86(2)
Purpose To improve the image quality and reduce computational time for the reconstruction of undersampled non-Cartesian abdominal dynamic parallel MR data using the deep learning approach. Methods An algorithm of parallel non-Cartesian convolutional
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b72be293dd9bd559657c8382ae7f75ab
https://pubmed.ncbi.nlm.nih.gov/33749023
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10
Correction of out-of-FOV motion artifacts using convolutional neural network
Autor: Chengyan Wang, Yucheng Liang, Yiping P. Du, Yuan Wu, Siwei Zhao
Publikováno v: Magnetic resonance imaging. 71
Subject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-FOV motio
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::857b8f653dfc2cac13b0f4e6b4eb991a
https://pubmed.ncbi.nlm.nih.gov/32464243
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