Towards a High-quality Visualization of Higher-order Reynold’s Glyphs for Diffusion Tensor Imaging

2012 ◽  
pp. 209-225 ◽  
Author(s):  
Mario Hlawitschka ◽  
Younis Hijazi ◽  
Aaron Knoll ◽  
Bernd Hamann
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Higher Order ◽  
High Quality

scholarly journals Diffusion tensor imaging of the corpus callosum in healthy aging: Investigating higher order polynomial regression modelling

NeuroImage ◽  
2020 ◽  
Vol 213 ◽  
pp. 116675 ◽  
Author(s):  
Wojciech Pietrasik ◽  
Ivor Cribben ◽  
Fraser Olsen ◽  
Yushan Huang ◽  
Nikolai V. Malykhin
Keyword(s):  
Diffusion Tensor Imaging ◽  
Corpus Callosum ◽  
Healthy Aging ◽  
Polynomial Regression ◽  
Diffusion Tensor ◽  
Higher Order ◽  
Regression Modelling ◽  
Order Polynomial

scholarly journals In vivo generalized diffusion tensor imaging (GDTI) using higher-order tensors (HOT)

2009 ◽  
Vol 63 (1) ◽  
pp. 243-252 ◽  
Author(s):  
Chunlei Liu ◽  
Sarah C. Mang ◽  
Michael E. Moseley
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Higher Order ◽  
Higher Order Tensors

scholarly journals CS-GAN for High-Quality Diffusion Tensor Imaging

2020 ◽  
Author(s):  
Ying Cao ◽  
Lihui Wang ◽  
Jianping Huang ◽  
Xinyu Cheng ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Image Quality ◽  
Compressed Sensing ◽  
Spatial Information ◽  
Diffusion Tensor ◽  
High Quality ◽  
Image Domain ◽  
Low Snr ◽  
Sampling Ratio ◽  
Space Data

Abstract Background: Compressed sensing magnetic resonance imaging (CS-MRI) is a promising technique for accelerating MRI speed. However, image quality in CS-MRI is still a pertinent problem. In particular, there is little work on reducing aliasing artefacts in compressed sensing diffusion tensor imaging (CS-DTI), which constitute a serious obstacle to obtaining high-quality images. Method: We propose a CS-DTI de-aliasing method based on conditional generative adversarial (cGAN), called CS-GAN, to tackle de-aliasing problems in CS-DTI with highly undersampled k-space data. The method uses a nested-UNet based generator, a ResNet-based discriminator, and a content loss function defined in both image domain and frequency domain. Result and Concludions: Compared to existing state-of-the-art de-aliasing methods based on deep learning, our method achieves superior imaging quality in terms of both diffusion weighted (DW) image quality and DTI diffusion metrics. Moreover, even at extremely low sampling ratio and low SNR, our method can still reconstruct texture details and spatial information.

scholarly journals Higher Order Positive Semidefinite Diffusion Tensor Imaging

2010 ◽  
Vol 3 (3) ◽  
pp. 416-433 ◽  
Author(s):  
Liqun Qi ◽  
Gaohang Yu ◽  
Ed X. Wu
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Positive Semidefinite ◽  
Higher Order

scholarly journals Higher-Order Motion-Compensation for In Vivo Cardiac Diffusion Tensor Imaging in Rats

2015 ◽  
Vol 34 (9) ◽  
pp. 1843-1853 ◽  
Author(s):  
Christopher L. Welsh ◽  
Edward V. R. DiBella ◽  
Edward W. Hsu
Keyword(s):  
Diffusion Tensor Imaging ◽  
Motion Compensation ◽  
Diffusion Tensor ◽  
Higher Order
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