scholarly journals A scalable method to improve gray matter segmentation at ultra high field MRI

2018 ◽  
Author(s):  
Omer Faruk Gulban ◽  
Marian Schneider ◽  
Ingo Marquardt ◽  
Roy A.M. Haast ◽  
Federico De Martino
Keyword(s):  
Gray Matter ◽  
Compositional Data ◽  
Three Dimensional ◽  
Ground Truth ◽  
7 Tesla ◽  
Compositional Data Analysis ◽  
Two Dimensional ◽  
Anatomical Images ◽  
7 Tesla Mri ◽  
Magnetic Resonance Imaging Mri

AbstractHigh-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

scholarly journals Microinfarcts in the deep gray matter on 7 Tesla MRI: Risk factors, MRI correlates and relation to cognitive functioning — the SMART‐MR study

2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Rashid Ghaznawi ◽  
Maarten H. T. Zwartbol ◽  
Jeroen de Bresser ◽  
Hugo J. Kuijf ◽  
Jeroen Hendrikse ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Functioning ◽  
Gray Matter ◽  
7 Tesla ◽  
7 Tesla Mri ◽  
Deep Gray Matter ◽  
Mr Study

scholarly journals Noninvasive 7 tesla MRI of fatal craniocerebral gunshots – a glance into the future of radiologic wound ballistics

2020 ◽  
Vol 16 (4) ◽  
pp. 595-604
Author(s):  
Dominic Gascho ◽  
Eva Deininger-Czermak ◽  
Niklaus Zoelch ◽  
Carlo Tappero ◽  
Stefan Sommer ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Gunshot Wounds ◽  
7 Tesla ◽  
Clinical Use ◽  
Advantages And Disadvantages ◽  
Radiologic Findings ◽  
Diagnostic Potential ◽  
The Future ◽  
7 Tesla Mri ◽  
Magnetic Resonance Imaging Mri

AbstractCompared to computed tomography (CT), magnetic resonance imaging (MRI) provides superior visualization of the soft tissue. Recently, the first 7 Tesla (7 T) MRI scanner was approved for clinical use, which will facilitate access to these ultra-high-field MRI scanners for noninvasive examinations and scientific studies on decedents. 7 T MRI has the potential to provide a higher signal-to-noise ratio (SNR), a characteristic that can be directly exploited to improve image quality and invest in attempts to increase resolution. Therefore, evaluating the diagnostic potential of 7 T MRI for forensic purposes, such as assessments of fatal gunshot wounds, was deemed essential. In this article, we present radiologic findings obtained for craniocerebral gunshot wounds in three decedents. The decedents were submitted to MRI examinations using a 7 T MRI scanner that has been approved for clinical use and a clinical 3 T MRI scanner for comparison. We focused on detecting tiny injuries beyond the wound tract caused by temporary cavitation, such as microbleeds. Additionally, 7 T T2-weighted MRI highlighted a dark (hypo intense) zone beyond the permanent wound tract, which was attributed to increased amounts of paramagnetic blood components in damaged tissue. Microbleeds were also detected adjacent to the wound tract in the white matter on 7 T MRI. Based on the findings of radiologic assessments, the advantages and disadvantages of postmortem 7 T MRI compared to 3 T MRI are discussed with regard to investigations of craniocerebral gunshot wounds as well as the potential role of 7 T MRI in the future of forensic science.

scholarly journals 7 Tesla MRI of the ex vivo human brain at 100 micron resolution

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Brian L. Edlow ◽  
Azma Mareyam ◽  
Andreas Horn ◽  
Jonathan R. Polimeni ◽  
Thomas Witzel ◽  
...  
Keyword(s):  
Human Brain ◽  
Ex Vivo ◽  
Three Dimensional ◽  
Academic Community ◽  
7 Tesla ◽  
Brain Anatomy ◽  
Isotropic Resolution ◽  
Scan Time ◽  
Brain Specimen ◽  
7 Tesla Mri

Abstract We present an ultra-high resolution MRI dataset of an ex vivo human brain specimen. The brain specimen was donated by a 58-year-old woman who had no history of neurological disease and died of non-neurological causes. After fixation in 10% formalin, the specimen was imaged on a 7 Tesla MRI scanner at 100 µm isotropic resolution using a custom-built 31-channel receive array coil. Single-echo multi-flip Fast Low-Angle SHot (FLASH) data were acquired over 100 hours of scan time (25 hours per flip angle), allowing derivation of synthesized FLASH volumes. This dataset provides an unprecedented view of the three-dimensional neuroanatomy of the human brain. To optimize the utility of this resource, we warped the dataset into standard stereotactic space. We now distribute the dataset in both native space and stereotactic space to the academic community via multiple platforms. We envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance understanding of human brain anatomy in health and disease.

scholarly journals Analyzing Femorotibial Cartilage Thickness Using Anatomically Standardized Maps: Reproducibility and Reference Data

2021 ◽  
Vol 10 (3) ◽  
pp. 461
Author(s):  
Julien Favre ◽  
Hugo Babel ◽  
Alessandro Cavinato ◽  
Katerina Blazek ◽  
Brigitte M. Jolles ◽  
...  
Keyword(s):  
Reference Data ◽  
Three Dimensional ◽  
Cartilage Thickness ◽  
Future Research ◽  
Two Dimensional ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Operator Reproducibility ◽  
Mesh Models ◽  
And Cartilage

Alterations in cartilage thickness (CTh) are a hallmark of knee osteoarthritis, which remain difficult to characterize at high resolution, even with modern magnetic resonance imaging (MRI), due to a paucity of standardization tools. This study aimed to assess a computational anatomy method producing standardized two-dimensional femorotibial CTh maps. The method was assessed with twenty knees, processed following three common experimental scenarios. Cartilage thickness maps were obtained for the femorotibial cartilages by reconstructing bone and cartilage mesh models in tree-dimension, calculating three-dimensional CTh maps, and anatomically standardizing the maps. The intra-operator accuracy (median (interquartile range, IQR) of −0.006 (0.045) mm), precision (0.152 (0.070) mm), entropy (7.02 (0.71) and agreement (0.975 (0.020))) results suggested that the method is adequate to capture the spatial variations in CTh and compare knees at varying osteoarthritis stages. The lower inter-operator precision (0.496 (0.132) mm) and agreement (0.808 (0.108)) indicate a possible loss of sensitivity to detect differences in a setting with multiple operators. The results confirmed the promising potential of anatomically standardized maps, with the lower inter-operator reproducibility stressing the need to coordinate operators. This study also provided essential reference data and indications for future research using CTh maps.

Improvement of generalization capability of two-dimensional salt segmentation via iterative semi-supervised learning

2022 ◽  
pp. 1-43
Author(s):  
Lingxiao Jia ◽  
Satyakee Sen ◽  
Subhashis Mallick
Keyword(s):  
Oil And Gas ◽  
Three Dimensional ◽  
Ground Truth ◽  
Unlabeled Data ◽  
New Method ◽  
Two Dimensional ◽  
Learning Tools ◽  
Generalization Capability ◽  
Oil And Gas Exploration ◽  
Depth Images

Accurate interpretations of subsurface salts are vital to oil and gas exploration. Manually interpreting them from seismic depth images, however, is labor-intensive. Consequently, use of deep learning tools such as a convolutional neural network for automatic salt interpretation recently became popular. Because of poor generalization capabilities, interpreting salt boundaries using these tools is difficult when labeled data are available from one geological region and we like to make predictions for other nearby regions with varied geological features. At the same time, due to vast amount of the data involved and the associated computational complexities needed for training, such generalization is necessary for solving practical salt interpretation problems. In this work, we propose a semi-supervised training, which allows the predicted model to iteratively improve as more and more information is distilled from the unlabeled data into the model. In addition, by performing mixup between labeled and unlabeled data during training, we encourage the predicted models to linearly behave across training samples; thereby improving the generalization capability of the method. For each iteration, we use the model obtained from previous iteration to generate pseudo labels for the unlabeled data. This automated consecutive data distillation allows our model prediction to improve with iteration, without any need for human intervention. To demonstrate the effectiveness and efficiency, we apply the method on two-dimensional images extracted from a real three-dimensional seismic data volume. By comparing our predictions and fully supervised baseline predictions with those that were manually interpreted and we consider as “ground truth”, we find than the prediction quality our new method surpasses the baseline prediction. We therefore conclude that our new method is a viable tool for automated salt delineation from seismic depth images.

A METHOD FOR COMPARISON OF RADIOCARPAL CARTILAGE T2 RELAXATION TIME MAPS AND CONTACT PRESSURE DISTRIBUTIONS IN NORMAL AND INJURED WRISTS

2015 ◽  
Vol 18 (03) ◽  
pp. 1550012
Author(s):  
Isaac Chappell ◽  
Phil Lee ◽  
Terence E. McIff ◽  
E. Bruce Toby ◽  
Kenneth J. Fischer
Keyword(s):  
Contact Pressure ◽  
Dimensional Space ◽  
Relaxation Times ◽  
Three Dimensional ◽  
T2 Relaxation ◽  
Anatomical Images ◽  
Joint Contact ◽  
Magnetic Resonance Imaging Mri ◽  
Joint Contact Pressure ◽  
Contact Pressures

Objective: The goal of this study was to demonstrate a methodology to observe the relationship between joint contact pressure and cartilage T2 relaxation times in three-dimensional space. Methods: One subject diagnosed with unilateral scapholunate dissociation had both injured and uninjured wrists scanned using a Siemens 3T Skyra magnetic resonance imaging (MRI) scanner. Four time echo scans were performed with TE ranging 15–61[Formula: see text]ms with the hand relaxed. T2 maps were constructed using a custom Matlab code, and these maps were registered to anatomical images for the same subject. The anatomical images were used to construct surface contact models and calculate contact pressures for a simple grasp activity in a prior study. Contact pressures and T2 relaxation times were analyzed using regression analysis. Results and Conclusion: This study demonstrates the feasibility of comparing T2 relaxation times and contact pressure data. For this single demonstration subject, it is not surprising that no relationship was found between T2 relaxation times for the articular cartilage and contact pressures in the normal wrist, contact pressures in the wrist with injury, nor contact pressure changes due to injury. However, the method has been demonstrated and may be useful to evaluate the influence of joint injuries or other pathologies on T2 relaxation times in the context of changes in joint contact pressures with larger cohorts of subjects.

scholarly journals 7 Tesla MRI of the ex vivo human brain at 100 micron resolution

2019 ◽  
Author(s):  
Brian L. Edlow ◽  
Azma Mareyam ◽  
Andreas Horn ◽  
Jonathan R. Polimeni ◽  
Thomas Witzel ◽  
...  
Keyword(s):  
Human Brain ◽  
Ex Vivo ◽  
Three Dimensional ◽  
Academic Community ◽  
7 Tesla ◽  
Brain Anatomy ◽  
Isotropic Resolution ◽  
Scan Time ◽  
Brain Specimen ◽  
7 Tesla Mri

AbstractWe present an ultra-high resolution MRI dataset of an ex vivo human brain specimen. The brain specimen was donated by a 58-year-old woman who had no history of neurological disease and died of non-neurological causes. After fixation in 10% formalin, the specimen was imaged on a 7 Tesla MRI scanner at 100 μm isotropic resolution using a custom-built 31-channel receive array coil. Single-echo multi-flip Fast Low-Angle SHot (FLASH) data were acquired over 100 hours of scan time (25 hours per flip angle), allowing derivation of a T1 parameter map and synthesized FLASH volumes. This dataset provides an unprecedented view of the three-dimensional neuroanatomy of the human brain. To optimize the utility of this resource, we warped the dataset into standard stereotactic space. We now distribute the dataset in both native space and stereotactic space to the academic community via multiple platforms. We envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance understanding of human brain anatomy in health and disease.

scholarly journals Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Zeju Li ◽  
Yuanyuan Wang ◽  
Jinhua Yu ◽  
Zhifeng Shi ◽  
Yi Guo ◽  
...  
Keyword(s):  
Conditional Random Field ◽  
Three Dimensional ◽  
Ground Truth ◽  
Low Grade Glioma ◽  
Dice Similarity Coefficient ◽  
Low Grade ◽  
Low Contrast ◽  
Magnetic Resonance Imaging Mri ◽  
3D Information ◽  
Fully Connected

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.

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