Zobrazeno 1 - 10
of 385
pro vyhledávání: '"Yablonskiy, Dmitriy A."'
Autor:
Hu, Yuyang, Kothapalli, Satya V. V. N., Gan, Weijie, Sukstanskii, Alexander L., Wu, Gregory F., Goyal, Manu, Yablonskiy, Dmitriy A., Kamilov, Ulugbek S.
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model. DiffGEPCI can synthesize high-quality Fluid Attenuated Inversion Recovery (FLAIR) and Magnetizat
Externí odkaz:
http://arxiv.org/abs/2311.18073
Autor:
Xu, Xiaojian, Gan, Weijie, Kothapalli, Satya V. V. N., Yablonskiy, Dmitriy A., Kamilov, Ulugbek S.
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately with artifacts arising from accelerated data acquisition, involuntary
Externí odkaz:
http://arxiv.org/abs/2210.06330
Autor:
Samara, Amjad, Xiang, Biao, Judge, Bradley, Ciotti, John R., Yablonskiy, Dmitriy A., Cross, Anne H., Brier, Matthew R
Publikováno v:
In Multiple Sclerosis and Related Disorders October 2024 90
Autor:
Tomaszewski, Michal R., Sukstanskii, Alexander L., Haley, Hyking, Meng, Xiangjun, Miller, Corin O., Yablonskiy, Dmitriy A.
Publikováno v:
In NeuroImage September 2024 298
Autor:
Xu, Xiaojian, Kothapalli, Satya V. V. N., Liu, Jiaming, Kahali, Sayan, Gan, Weijie, Yablonskiy, Dmitriy A., Kamilov, Ulugbek S.
Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data. Me
Externí odkaz:
http://arxiv.org/abs/2109.01622
Autor:
Wen, Jie, Zeng, Feiyan, Yablonskiy, Dmitriy, Sukstansky, Alexander, Liu, Ying, Cai, Bin, Zhang, Yong, Lv, Weifu
Purpose: Previously-developed Voxel Spread Function (VSF) method (Yablonskiy, et al, MRM, 2013;70:1283) provides means to correct artifacts induced by macroscopic magnetic field inhomogeneities in the images obtained by multi-Gradient-Recalled-Echo (
Externí odkaz:
http://arxiv.org/abs/2001.09400
Autor:
Torop, Max, Kothapalli, Satya VVN, Sun, Yu, Liu, Jiaming, Kahali, Sayan, Yablonskiy, Dmitriy A., Kamilov, Ulugbek S.
Purpose: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* maps from multi-gradient recalled echo (mGRE) MRI data. Methods: RoAR trains a convolutional neural
Externí odkaz:
http://arxiv.org/abs/1912.07087
Publikováno v:
In NeuroImage 15 July 2021 235
Publikováno v:
Magnetic Resonance in Medicine; Jun2024, Vol. 91 Issue 6, p2597-2611, 15p
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2018 Oct 01. 115(41), E9727-E9736.
Externí odkaz:
https://www.jstor.org/stable/26532236