An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 584-597 ◽  
Author(s):  
Sébastien Tourbier ◽  
Xavier Bresson ◽  
Patric Hagmann ◽  
Jean-Philippe Thiran ◽  
Reto Meuli ◽  
...  
Keyword(s):  
Total Variation ◽  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution ◽  
Adaptive Regularization

Sparse Bayesian framework applied to 3D super-resolution reconstruction in fetal brain MRI

2015 ◽  
Author(s):  
Laura C. Becerra ◽  
Nelson Velasco Toledo ◽  
Eduardo Romero Castro
Keyword(s):  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution ◽  
Bayesian Framework

scholarly journals On Super-Resolution for Fetal Brain MRI

2010 ◽  
pp. 355-362 ◽  
Author(s):  
F. Rousseau ◽  
K. Kim ◽  
C. Studholme ◽  
M. Koob ◽  
J. -L. Dietemann
Keyword(s):  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution

scholarly journals Deep robust residual network for super-resolution of 2D fetal brain MRI

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Liyao Song ◽  
Quan Wang ◽  
Ting Liu ◽  
Haiwei Li ◽  
Jiancun Fan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Efficiency ◽  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution ◽  
Adult Brain ◽  
Superior Performance ◽  
Residual Learning ◽  
2D Data

AbstractSpatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Due to the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI 2D data for evaluation. The experimental results have shown that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods.

scholarly journals Fetal Brain Biometric Measurements on 3D Super-Resolution Reconstructed T2-Weighted MRI: An Intra- and Inter-observer Agreement Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Marie Khawam ◽  
Priscille de Dumast ◽  
Pierre Deman ◽  
Hamza Kebiri ◽  
Thomas Yu ◽  
...  
Keyword(s):  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution ◽  
Multiple Comparisons ◽  
Observer Agreement ◽  
Pediatric Radiologist ◽  
Level Of Confidence ◽  
Reconstructed Volume ◽  
Significant Difference ◽  
Biometric Measurements

We present the comparison of two-dimensional (2D) fetal brain biometry on magnetic resonance (MR) images using orthogonal 2D T2-weighted sequences (T2WSs) vs. one 3D super-resolution (SR) reconstructed volume and evaluation of the level of confidence and concordance between an experienced pediatric radiologist (obs1) and a junior radiologist (obs2). Twenty-five normal fetal brain MRI scans (18–34 weeks of gestation) including orthogonal 3-mm-thick T2WSs were analyzed retrospectively. One 3D SR volume was reconstructed per subject based on multiple series of T2WSs. The two observers performed 11 2D biometric measurements (specifying their level of confidence) on T2WS and SR volumes. Measurements were compared using the paired Wilcoxon rank sum test between observers for each dataset (T2WS and SR) and between T2WS and SR for each observer. Bland–Altman plots were used to assess the agreement between each pair of measurements. Measurements were made with low confidence in three subjects by obs1 and in 11 subjects by obs2 (mostly concerning the length of the corpus callosum on T2WS). Inter-rater intra-dataset comparisons showed no significant difference (p > 0.05), except for brain axial biparietal diameter (BIP) on T2WS and for brain and skull coronal BIP and coronal transverse cerebellar diameter (DTC) on SR. None of them remained significant after correction for multiple comparisons. Inter-dataset intra-rater comparisons showed statistical differences in brain axial and coronal BIP for both observers, skull coronal BIP for obs1, and axial and coronal DTC for obs2. After correction for multiple comparisons, only axial brain BIP remained significantly different, but differences were small (2.95 ± 1.73 mm). SR allows similar fetal brain biometry as compared to using the conventional T2WS while improving the level of confidence in the measurements and using a single reconstructed volume.

scholarly journals Deep Robust Residual Network for Brain MRI Super-Resolution

2020 ◽  
Author(s):  
Liyao Song ◽  
Quan Wang ◽  
Ting Liu ◽  
Haiwei Li ◽  
Jiancun Fan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Efficiency ◽  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution ◽  
Adult Brain ◽  
Superior Performance ◽  
Residual Learning ◽  
Key Factor

Abstract Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Given that the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combine the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI data for evaluation. The experimental results show that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods.

Super-resolution image reconstruction using fractional-order total variation and adaptive regularization parameters

2018 ◽  
Vol 35 (12) ◽  
pp. 1755-1768 ◽  
Author(s):  
Xiaomei Yang ◽  
Jiawei Zhang ◽  
Yanan Liu ◽  
Xiujuan Zheng ◽  
Kai Liu
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Fractional Order ◽  
Super Resolution ◽  
Adaptive Regularization ◽  
Resolution Image ◽  
Regularization Parameters

scholarly journals An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI

NeuroImage ◽  
2020 ◽  
Vol 206 ◽  
pp. 116324 ◽  
Author(s):  
Michael Ebner ◽  
Guotai Wang ◽  
Wenqi Li ◽  
Michael Aertsen ◽  
Premal A. Patel ◽  
...  
Keyword(s):  
Brain Mri ◽  
Fetal Brain ◽  
Super Resolution

scholarly journals Efficient Total Variation Algorithm for Fetal Brain MRI Reconstruction

2014 ◽  
pp. 252-259 ◽  
Author(s):  
Sébastien Tourbier ◽  
Xavier Bresson ◽  
Patric Hagmann ◽  
Jean-Philippe Thiran ◽  
Reto Meuli ◽  
...  
Keyword(s):  
Total Variation ◽  
Brain Mri ◽  
Fetal Brain ◽  
Mri Reconstruction
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