scholarly journals Joint-Saliency Structure Adaptive Kernel Regression with Adaptive-Scale Kernels for Deformable Registration of Challenging Images

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 330-343 ◽  
Author(s):  
Binjie Qin ◽  
Zhuangming Shen ◽  
Zeshan Fu ◽  
Zien Zhou ◽  
Yisong Lv ◽  
...  
Keyword(s):  
Kernel Regression ◽  
Deformable Registration ◽  
Adaptive Kernel

Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression

NeuroImage ◽  
2012 ◽  
Vol 59 (3) ◽  
pp. 2255-2265 ◽  
Author(s):  
Ahmed Serag ◽  
Paul Aljabar ◽  
Gareth Ball ◽  
Serena J. Counsell ◽  
James P. Boardman ◽  
...  
Keyword(s):  
Kernel Regression ◽  
Developing Brain ◽  
High Definition ◽  
Adaptive Kernel ◽  
Spatio Temporal

scholarly journals A locally adaptive kernel regression method for facies delineation

2015 ◽  
Vol 531 ◽  
pp. 62-72
Author(s):  
D. Fernàndez-Garcia ◽  
M. Barahona-Palomo ◽  
C.V. Henri ◽  
X. Sanchez-Vila
Keyword(s):  
Kernel Regression ◽  
Regression Method ◽  
Locally Adaptive ◽  
Adaptive Kernel

Structure matching driven by joint-saliency-structure adaptive kernel regression

2016 ◽  
Vol 46 ◽  
pp. 851-867 ◽  
Author(s):  
Binjie Qin ◽  
Zhuangming Shen ◽  
Zien Zhou ◽  
Jiawei Zhou ◽  
Yisong Lv
Keyword(s):  
Kernel Regression ◽  
Adaptive Kernel

scholarly journals Erratum to Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression [NeuroImage 59/3(2012) 2255–2265]

NeuroImage ◽  
2012 ◽  
Vol 63 (2) ◽  
pp. 998
Author(s):  
Ahmed Serag ◽  
Paul Aljabar ◽  
Gareth Ball ◽  
Serena J. Counsell ◽  
James P. Boardman ◽  
...  
Keyword(s):  
Kernel Regression ◽  
Developing Brain ◽  
High Definition ◽  
Adaptive Kernel ◽  
Spatio Temporal

scholarly journals A Sparse Volume Reconstruction Method for Fetal Brain MRI Using Adaptive Kernel Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qian Ni ◽  
Yi Zhang ◽  
Tiexiang Wen ◽  
Ling Li
Keyword(s):  
Brain Mri ◽  
Kernel Regression ◽  
Fetal Brain ◽  
Sparse Sampling ◽  
Motion Artifacts ◽  
Reconstruction Method ◽  
3D Registration ◽  
Volume Reconstruction ◽  
Computational Performance ◽  
Adaptive Kernel

Slice-to-volume reconstruction (SVR) method can deal well with motion artifacts and provide high-quality 3D image data for fetal brain MRI. However, the problem of sparse sampling is not well addressed in the SVR method. In this paper, we mainly focus on the sparse volume reconstruction of fetal brain MRI from multiple stacks corrupted with motion artifacts. Based on the SVR framework, our approach includes the slice-to-volume 2D/3D registration, the point spread function- (PSF-) based volume update, and the adaptive kernel regression-based volume update. The adaptive kernel regression can deal well with the sparse sampling data and enhance the detailed preservation by capturing the local structure through covariance matrix. Experimental results performed on clinical data show that kernel regression results in statistical improvement of image quality for sparse sampling data with the parameter setting of the structure sensitivity 0.4, the steering kernel size of 7 × 7 × 7 and steering smoothing bandwidth of 0.5. The computational performance of the proposed GPU-based method can be over 90 times faster than that on CPU.

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