scholarly journals 3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction

2021 ◽  
Vol 69 ◽  
pp. 101957
Author(s):  
Rewa R. Sood ◽  
Wei Shao ◽  
Christian Kunder ◽  
Nikola C. Teslovich ◽  
Jeffrey B. Wang ◽  
...  
Keyword(s):  
Super Resolution ◽  
3D Registration ◽  
Volume Reconstruction ◽  
Prostate Mri ◽  
Histopathology Images

scholarly journals Weakly Supervised Registration of Prostate MRI and Histopathology Images

2021 ◽  
pp. 98-107
Author(s):  
Wei Shao ◽  
Indrani Bhattacharya ◽  
Simon J. C. Soerensen ◽  
Christian A. Kunder ◽  
Jeffrey B. Wang ◽  
...  
Keyword(s):  
Prostate Mri ◽  
Histopathology Images ◽  
Weakly Supervised

scholarly journals Robust, Fiducial-Free Drift Correction for Super-resolution Imaging

2021 ◽  
Author(s):  
Michael J Wester ◽  
David J Schodt ◽  
Hanieh Mazloom-Farsibaf ◽  
Mohamadreza Fazel ◽  
Sandeep Pallikkuth ◽  
...  
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Processing Algorithm ◽  
Post Processing ◽  
3D Registration ◽  
Drift Correction ◽  
Registration Errors ◽  
Time Period ◽  
Resolution Imaging ◽  
Specialized Hardware

We describe a robust, fiducial-free method of drift correction for use in single molecule localization-based super-resolution methods. The method combines periodic 3D registration of the sample using brightfield images with a fast post-processing algorithm that corrects residual registration errors and drift between registration events. The method is robust to low numbers of collected localizations, requires no specialized hardware, and provides stability and drift correction for an indefinite time period.

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.

Improving vocal tract reconstruction and modeling through super-resolution volume reconstruction technique

2012 ◽  
Vol 132 (3) ◽  
pp. 2088-2088
Author(s):  
Jonghye Woo ◽  
Xinhui Zhou ◽  
Maureen Stone ◽  
Jerry L. Prince ◽  
Carol Y. Espy-Wilson
Keyword(s):  
Vocal Tract ◽  
Super Resolution ◽  
Reconstruction Technique ◽  
Volume Reconstruction

scholarly journals Capsule GAN for prostate MRI super-resolution

2021 ◽  
Author(s):  
Mahdiyar Molahasani Majdabadi ◽  
Younhee Choi ◽  
S. Deivalakshmi ◽  
Seokbum Ko
Keyword(s):  
Super Resolution ◽  
Prostate Mri

scholarly journals Robust, fiducial-free drift correction for super-resolution imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael J. Wester ◽  
David J. Schodt ◽  
Hanieh Mazloom-Farsibaf ◽  
Mohamadreza Fazel ◽  
Sandeep Pallikkuth ◽  
...  
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Processing Algorithm ◽  
Post Processing ◽  
3D Registration ◽  
Drift Correction ◽  
Registration Errors ◽  
Time Period ◽  
Resolution Imaging ◽  
Specialized Hardware

AbstractWe describe a robust, fiducial-free method of drift correction for use in single molecule localization-based super-resolution methods. The method combines periodic 3D registration of the sample using brightfield images with a fast post-processing algorithm that corrects residual registration errors and drift between registration events. The method is robust to low numbers of collected localizations, requires no specialized hardware, and provides stability and drift correction for an indefinite time period.

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