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.

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging

2017 ◽  
Vol 39 (4) ◽  
pp. 240-259 ◽  
Author(s):  
Tiexiang Wen ◽  
Ling Li ◽  
Qingsong Zhu ◽  
Wenjian Qin ◽  
Jia Gu ◽  
...  
Keyword(s):  
Image Quality ◽  
Ultrasound Imaging ◽  
Kernel Regression ◽  
3D Ultrasound ◽  
Speckle Reduction ◽  
Reconstruction Method ◽  
Processing Unit ◽  
Hole Filling ◽  
Volume Reconstruction ◽  
Computational Performance

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor–based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

Towards 3D registration of fetal brain MRI and ultrasound

2012 ◽  
Author(s):  
Maria Kuklisova-Murgasova ◽  
Gerardine Quaghebeur ◽  
Joseph V. Hajnal ◽  
J. Alison Noble ◽  
Julia A. Schnabel
Keyword(s):  
Brain Mri ◽  
Fetal Brain ◽  
3D Registration

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

3D RECONSTRUCTION OF BREAST CANCER FROM MAMMOGRAMS USING VOLUME RENDERING TECHNIQUES

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Ho Wei Yong ◽  
Abdullah Bade ◽  
Rajesh Kumar Muniandy
Keyword(s):  
Breast Cancer ◽  
3D Reconstruction ◽  
Computational Cost ◽  
Reconstruction Method ◽  
Cancer Model ◽  
Volume Reconstruction ◽  
The Past ◽  
Breast Cancer Model ◽  
Reconstruction Software ◽  
To Come

Over the past thirty years, a number of researchers have investigated on 3D organ reconstruction from medical images and there are a few 3D reconstruction software available on the market. However, not many researcheshave focused on3D reconstruction of breast cancer’s tumours. Due to the method complexity, most 3D breast cancer’s tumours reconstruction were done based on MRI slices dataeven though mammogram is the current clinical practice for breast cancer screening. Therefore, this research will investigate the process of creating a method that will be able to reconstruct 3D breast cancer’s tumours from mammograms effectively.  Several steps were proposed for this research which includes data acquisition, volume reconstruction, andvolume rendering. The expected output from this research is the 3D breast cancer’s tumours model that is generated from correctly registered mammograms. The main purpose of this research is to come up with a 3D reconstruction method that can produce good breast cancer model from mammograms while using minimal computational cost.

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 Dictionary learning sparse-sampling reconstruction method for in-vivo 3D photoacoustic computed tomography

2019 ◽  
Vol 10 (4) ◽  
pp. 1660 ◽  
Author(s):  
Fangyan Liu ◽  
Xiaojing Gong ◽  
Lihong V. Wang ◽  
Jingjing Guan ◽  
Liang Song ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dictionary Learning ◽  
Sparse Sampling ◽  
Reconstruction Method

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.

scholarly journals Automatic quantification of normal cortical folding patterns from fetal brain MRI

NeuroImage ◽  
2014 ◽  
Vol 91 ◽  
pp. 21-32 ◽  
Author(s):  
R. Wright ◽  
V. Kyriakopoulou ◽  
C. Ledig ◽  
M.A. Rutherford ◽  
J.V. Hajnal ◽  
...  
Keyword(s):  
Brain Mri ◽  
Fetal Brain ◽  
Cortical Folding ◽  
Automatic Quantification
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