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

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

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

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.

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 In utero MRI identifies consequences of early-gestation alcohol drinking on fetal brain development in rhesus macaques

2020 ◽  
Vol 117 (18) ◽  
pp. 10035-10044
Author(s):  
Xiaojie Wang ◽  
Verginia C. Cuzon Carlson ◽  
Colin Studholme ◽  
Natali Newman ◽  
Matthew M. Ford ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Development ◽  
Ex Vivo ◽  
Early Recognition ◽  
Brain Mri ◽  
Fetal Brain ◽  
Fetal Mri ◽  
Diffusion Anisotropy ◽  
Self Administration ◽  
White Matter Tracts

One factor that contributes to the high prevalence of fetal alcohol spectrum disorder (FASD) is binge-like consumption of alcohol before pregnancy awareness. It is known that treatments are more effective with early recognition of FASD. Recent advances in retrospective motion correction for the reconstruction of three-dimensional (3D) fetal brain MRI have led to significant improvements in the quality and resolution of anatomical and diffusion MRI of the fetal brain. Here, a rhesus macaque model of FASD, involving oral self-administration of 1.5 g/kg ethanol per day beginning prior to pregnancy and extending through the first 60 d of a 168-d gestational term, was utilized to determine whether fetal MRI could detect alcohol-induced abnormalities in brain development. This approach revealed differences between ethanol-exposed and control fetuses at gestation day 135 (G135), but not G110 or G85. At G135, ethanol-exposed fetuses had reduced brainstem and cerebellum volume and water diffusion anisotropy in several white matter tracts, compared to controls. Ex vivo electrophysiological recordings performed on fetal brain tissue obtained immediately following MRI demonstrated that the structural abnormalities observed at G135 are of functional significance. Specifically, spontaneous excitatory postsynaptic current amplitudes measured from individual neurons in the primary somatosensory cortex and putamen strongly correlated with diffusion anisotropy in the white matter tracts that connect these structures. These findings demonstrate that exposure to ethanol early in gestation perturbs development of brain regions associated with motor control in a manner that is detectable with fetal MRI.

scholarly journals Early Findings in Neonatal Cases of RYR1–Related Congenital Myopathies

2021 ◽  
Vol 12 ◽  
Author(s):  
Eleonora Mauri ◽  
Daniela Piga ◽  
Alessandra Govoni ◽  
Roberta Brusa ◽  
Serena Pagliarani ◽  
...  
Keyword(s):  
Ryanodine Receptor ◽  
Brain Mri ◽  
Fetal Brain ◽  
Congenital Myopathy ◽  
Receptor Type ◽  
Congenital Myopathies ◽  
Muscle Mri ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Ryanodine receptor type 1-related congenital myopathies are the most represented subgroup among congenital myopathies (CMs), typically presenting a central core or multiminicore muscle histopathology and high clinical heterogeneity. We evaluated a cohort of patients affected with Ryanodine receptor type 1-related congenital myopathy (RYR1-RCM), focusing on four patients who showed a severe congenital phenotype and underwent a comprehensive characterization at few months of life. To date there are few reports on precocious instrumental assessment. In two out of the four patients, a muscle biopsy was performed in the first days of life (day 5 and 37, respectively) and electron microscopy was carried out in two patients detecting typical features of congenital myopathy. Two patients underwent brain MRI in the first months of life (15 days and 2 months, respectively), one also a fetal brain MRI. In three children electromyography was performed in the first week of life and neurogenic signs were excluded. Muscle MRI obtained within the first years of life showed a typical pattern of RYR1-CM. The diagnosis was confirmed through genetic analysis in three out of four cases using Next Generation Sequencing (NGS) panels. The development of a correct and rapid diagnosis is a priority and may lead to prompt medical management and helps optimize inclusion in future clinical trials.

scholarly journals Automatic linear measurements of the fetal brain on MRI with deep neural networks

2021 ◽  
Author(s):  
Netanell Avisdris ◽  
Bossmat Yehuda ◽  
Ori Ben-Zvi ◽  
Daphna Link-Sourani ◽  
Liat Ben-Sira ◽  
...  
Keyword(s):  
Human Error ◽  
Brain Mri ◽  
Fetal Brain ◽  
Region Of Interest ◽  
Fetal Mri ◽  
Brain Structures ◽  
Automatic Method ◽  
Linear Measurements ◽  
Biparietal Diameter ◽  
Reliable Assessment

Abstract Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI.Methods: The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results: Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (I of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.Conclusions: The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

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