scholarly journals The effects of changing water content, relaxation times, and tissue contrast on tissue segmentation and measures of cortical anatomy in MR images

2013 ◽  
Vol 31 (10) ◽  
pp. 1709-1730 ◽  
Author(s):  
Ravi Bansal ◽  
Xuejun Hao ◽  
Feng Liu ◽  
Dongrong Xu ◽  
Jun Liu ◽  
...  
Keyword(s):  
Water Content ◽  
Relaxation Times ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Tissue Contrast

scholarly journals High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation

2020 ◽  
Vol 34 (04) ◽  
pp. 4067-4074
Author(s):  
Mohammad Hamghalam ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Stage Structure ◽  
Target Area ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Generative Adversarial Network ◽  
Low Contrast ◽  
Adversarial Network ◽  
Multi Stage ◽  
Translation Techniques ◽  
Tissue Contrast

Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block, as well as training on HTC images, guides our model to converge on certain tissues. To increase the resolution of HTC images, we employ multi-stage architecture to focus on one particular tissue as a foreground and filter out the irrelevant background in each stage. This multi-stage structure also alleviates the common artifacts of the synthetic images by decreasing the gap between source and target domains. We show the application of our method for synthesizing HTC images on brain MR scans, including glioma tumor. We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images. The experiments over three competitive segmentation baselines on BraTS 2018 dataset indicate that incorporating the synthetic HTC images in the multi-modal segmentation framework improves the average Dice scores 0.8%, 0.6%, and 0.5% on the whole tumor, tumor core, and enhancing tumor, respectively, while eliminating one real MRI sequence from the segmentation procedure.

scholarly journals A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Fabio Baselice ◽  
Giampaolo Ferraioli ◽  
Vito Pascazio
Keyword(s):  
Brain Tissue ◽  
Relaxation Times ◽  
Optimal Decision ◽  
Proton Density ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Classification Rate ◽  
Novel Approach ◽  
Wide Range ◽  
Novel Method

Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented. Starting from the estimation of proton density and relaxation times, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to globally improve the classification rate. The effectiveness of the approach is evaluated on both simulated and real datasets.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Optimization of T2-weighted imaging for shoulder magnetic resonance arthrography by synthetic magnetic resonance imaging

2017 ◽  
Vol 59 (8) ◽  
pp. 959-965
Author(s):  
Seung Hyun Lee ◽  
Young Han Lee ◽  
Seok Hahn ◽  
Jaemoon Yang ◽  
Ho-Taek Song ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Supraspinatus Tendon ◽  
Joint Fluid ◽  
Resonance Imaging ◽  
Mr Images ◽  
Diagnostic Agreement ◽  
Tissue Contrast ◽  
Synthetic Images ◽  
Synthetic Mri

Background Synthetic magnetic resonance imaging (MRI) allows reformatting of various synthetic images by adjustment of scanning parameters such as repetition time (TR) and echo time (TE). Optimized MR images can be reformatted from T1, T2, and proton density (PD) values to achieve maximum tissue contrast between joint fluid and adjacent soft tissue. Purpose To demonstrate the method for optimization of TR and TE by synthetic MRI and to validate the optimized images by comparison with conventional shoulder MR arthrography (MRA) images. Material and Methods Thirty-seven shoulder MRA images acquired by synthetic MRI were retrospectively evaluated for PD, T1, and T2 values at the joint fluid and glenoid labrum. Differences in signal intensity between the fluid and labrum were observed between TR of 500–6000 ms and TE of 80–300 ms in T2-weighted (T2W) images. Conventional T2W and synthetic images were analyzed for diagnostic agreement of supraspinatus tendon abnormalities (kappa statistics) and image quality scores (one-way analysis of variance with post-hoc analysis). Results Optimized mean values of TR and TE were 2724.7 ± 1634.7 and 80.1 ± 0.4, respectively. Diagnostic agreement for supraspinatus tendon abnormalities between conventional and synthetic MR images was excellent (κ = 0.882). The mean image quality score of the joint space in optimized synthetic images was significantly higher compared with those in conventional and synthetic images (2.861 ± 0.351 vs. 2.556 ± 0.607 vs. 2.750 ± 0.439; P < 0.05). Conclusion Synthetic MRI with optimized TR and TE for shoulder MRA enables optimization of soft-tissue contrast.

scholarly journals Tuning Water Networks via Ionic Liquid/Water Mixtures

2020 ◽  
Vol 21 (2) ◽  
pp. 403 ◽  
Author(s):  
Archana Verma ◽  
John P. Stoppelman ◽  
Jesse G. McDaniel
Keyword(s):  
Ionic Liquid ◽  
Water Content ◽  
Relaxation Times ◽  
Water Structure ◽  
Lyotropic Liquid Crystals ◽  
Rotational Relaxation ◽  
Membrane Materials ◽  
Water Fraction ◽  
Pairwise Correlation ◽  
Dynamics Simulations

Water in nanoconfinement is ubiquitous in biological systems and membrane materials, with altered properties that significantly influence the surrounding system. In this work, we show how ionic liquid (IL)/water mixtures can be tuned to create water environments that resemble nanoconfined systems. We utilize molecular dynamics simulations employing ab initio force fields to extensively characterize the water structure within five different IL/water mixtures: [BMIM + ][BF 4 − ], [BMIM + ][PF 6 − ], [BMIM + ][OTf − ], [BMIM + ][NO 3 − ] and [BMIM + ][TFSI − ] ILs at varying water fraction. We characterize water clustering, hydrogen bonding, water orientation, pairwise correlation functions and percolation networks as a function of water content and IL type. The nature of the water nanostructure is significantly tuned by changing the hydrophobicity of the IL and sensitively depends on water content. In hydrophobic ILs such as [BMIM + ][PF 6 − ], significant water clustering leads to dynamic formation of water pockets that can appear similar to those formed within reverse micelles. Furthermore, rotational relaxation times of water molecules in supersaturated hydrophobic IL/water mixtures indicate the close-connection with nanoconfined systems, as they are quantitatively similar to water relaxation in previously characterized lyotropic liquid crystals. We expect that this physical insight will lead to better design principles for incorporation of ILs into membrane materials to tune water nanostructure.

scholarly journals Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

2015 ◽  
Vol 60 (16) ◽  
pp. 6547-6562 ◽  
Author(s):  
Valerio Fortunati ◽  
René F Verhaart ◽  
Wiro J Niessen ◽  
Jifke F Veenland ◽  
Margarethus M Paulides ◽  
...  
Keyword(s):  
Head And Neck ◽  
Treatment Planning ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Hyperthermia Treatment
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