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 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 High-Efficiency Microsatellite-Using Super-Resolution Algorithm Based on the Multi-Modality Super-CMOS Sensor

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4019
Author(s):  
Ke Zhang ◽  
Cankun Yang ◽  
Xiaojuan Li ◽  
Chunping Zhou ◽  
Ruofei Zhong
Keyword(s):  
Spatial Resolution ◽  
High Efficiency ◽  
Super Resolution ◽  
Remote Sensing Images ◽  
High Resolution Image ◽  
Theory To Practice ◽  
Cmos Sensor ◽  
Resolution Algorithm ◽  
Overlap Ratio

To realize the application of super-resolution technology from theory to practice, and to improve microsatellite spatial resolution, we propose a special super-resolution algorithm based on the multi-modality super-CMOS sensor which can adapt to the limited operation capacity of microsatellite computers. First, we designed an oblique sampling mode with the sensor rotated at an angle of 26.56 ∘ ( arctan 1 2 ) to obtain high overlap ratio images with sub-pixel displacement. Secondly, the proposed super-resolution algorithm was applied to reconstruct the final high-resolution image. Because the satellite equipped with this sensor is scheduled to be launched this year, we also designed the simulation mode of conventional sampling and the oblique sampling of the sensor to obtain the comparison and experimental data. Lastly, we evaluated the super-resolution quality of images, the effectiveness, the practicality, and the efficiency of the algorithm. The results of the experiments showed that the satellite-using super-resolution algorithm combined with multi-modality super-CMOS sensor oblique-mode sampling can increase the spatial resolution of an image by about 2 times. The algorithm is simple and highly efficient, and can realize the super-resolution reconstruction of two remote-sensing images within 0.713 s, which has good performance on the microsatellite.

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 Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

2021 ◽  
Author(s):  
Seyed-Youns Sadat-Nejad
Keyword(s):  
Spatial Resolution ◽  
Variance Estimation ◽  
Statistical Parameter ◽  
Superior Performance ◽  
Source Analysis ◽  
Noise Variance ◽  
Post Processing ◽  
Eeg Data ◽  
Inverse Solutions

Analyzing Electroencephalography (EEG)/Magnetoencephalography (MEG) brain source signals allows for a better understanding and diagnosis of various brain-related activities or injuries. Due to the high complexity of the mentioned measurements and their low spatial resolution, different techniques have been employed to enhance the quality of the obtained results. The objective of this work is to employ state-of-the-art approaches and develop algorithms with higher analysis reliability. As a pre-processing method, subspace denoising and artifact removal approaches are taken into consideration, to provide a method that automates and improves the estimation of the Number of Component (NoC) for artifacts such as Eye Blinking (EB). By using synthetic EEG-like simulation and real MEG data, it is shown that the proposed method is more reliable over the conventional manual method in estimating the NoC. For Independent Component Analysis (ICA)-based approaches, the proposed method in this thesis provides an estimation for the number of components with an accuracy of 98.7%. The thesis is also devoted to improving source localization techniques, which aims to estimate the location of the source within the brain, which elicit time-series measurements. In this context, after obtaining a practical insight into the performance of the popular L2-Regularization based approaches, a post-processing thresholding method is introduced. The proposed method improves the spatial resolution of the L2-Regularization inverse solutions, especially for Standard Low-Resolution Electromagnetic Tomography (sLORETA), which is a well-known and widely used inverse solution. As a part of the proposed method, a novel noise variance estimation is introduced, which combines the kurtosis statistical parameter and data (noise) entropy. This new noise variance estimation technique allows for a superior performance of the proposed method compared to the existing ones. The algorithm is validated on the synthetic EEG data using well-established validation metrics. It is shown that the proposed solution improves the resolution of conventional methods in the process of thresholding/denoising automatically and without loss of any critical information.

scholarly journals Fast and Accurate Light Field View Synthesis by Optimizing Input View Selection

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 557
Author(s):  
Xingzheng Wang ◽  
Yongqiang Zan ◽  
Senlin You ◽  
Yuanlong Deng ◽  
Lihua Li
Keyword(s):  
Spatial Resolution ◽  
Light Field ◽  
Angular Resolution ◽  
Super Resolution ◽  
Depth Estimation ◽  
View Synthesis ◽  
The Novel ◽  
Arbitrary Location ◽  
Field View

There is a trade-off between spatial resolution and angular resolution limits in light field applications; various targeted algorithms have been proposed to enhance angular resolution while ensuring high spatial resolution simultaneously, which is also called view synthesis. Among them, depth estimation-based methods can use only four corner views to reconstruct a novel view at an arbitrary location. However, depth estimation is a time-consuming process, and the quality of the reconstructed novel view is not only related to the number of the input views, but also the location of the input views. In this paper, we explore the relationship between different input view selections with the angular super-resolution reconstruction results. Different numbers and positions of input views are selected to compare the speed of super-resolution reconstruction and the quality of novel views. Experimental results show that the speed of the algorithm decreases with the increase of the input views for each novel view, and the quality of the novel view decreases with the increase of the distance from the input views. After comparison using two input views in the same line to reconstruct the novel views between them, fast and accurate light field view synthesis is achieved.

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 Fast and High-Resolution Neonatal Brain MRI Through Super-Resolution Reconstruction From Acquisitions With Variable Slice Selection Direction

2021 ◽  
Vol 15 ◽  
Author(s):  
Yao Sui ◽  
Onur Afacan ◽  
Ali Gholipour ◽  
Simon K. Warfield
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Super Resolution ◽  
Neonatal Brain ◽  
Signal To Noise ◽  
High Quality ◽  
Noise Ratio

The brain of neonates is small in comparison to adults. Imaging at typical resolutions such as one cubic mm incurs more partial voluming artifacts in a neonate than in an adult. The interpretation and analysis of MRI of the neonatal brain benefit from a reduction in partial volume averaging that can be achieved with high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is slow, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio. The purpose of this study is thus that using super-resolution reconstruction in conjunction with fast imaging protocols to construct neonatal brain MRI images at a suitable signal-to-noise ratio and with higher spatial resolution than can be practically obtained by direct Fourier encoding. We achieved high quality brain MRI at a spatial resolution of isotropic 0.4 mm with 6 min of imaging time, using super-resolution reconstruction from three short duration scans with variable directions of slice selection. Motion compensation was achieved by aligning the three short duration scans together. We applied this technique to 20 newborns and assessed the quality of the images we reconstructed. Experiments show that our approach to super-resolution reconstruction achieved considerable improvement in spatial resolution and signal-to-noise ratio, while, in parallel, substantially reduced scan times, as compared to direct high-resolution acquisitions. The experimental results demonstrate that our approach allowed for fast and high-quality neonatal brain MRI for both scientific research and clinical studies.

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