Memory Network-Based Quality Normalization of Magnetic Resonance Images for Brain Segmentation

2019 ◽  
pp. 58-67
Author(s):  
Yang Su ◽  
Jie Wei ◽  
Benteng Ma ◽  
Yong Xia ◽  
Yanning Zhang
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Brain Segmentation ◽  
Memory Network

scholarly journals Brain Segmentation from Super-Resolved Magnetic Resonance Images

2019 ◽  
Author(s):  
Farah Bazzi ◽  
Juan D. Dios Rodriguez-Callejas ◽  
Caroline Fonta ◽  
Ahmad Diab ◽  
Hassan Amoud ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Brain Segmentation

scholarly journals Phantom-based performance evaluation: Application to brain segmentation from magnetic resonance images

2000 ◽  
Vol 4 (4) ◽  
pp. 303-316 ◽  
Author(s):  
Bruno Moretti ◽  
Jalal Mohamed Fadili ◽  
Su Ruan ◽  
Daniel Bloyet ◽  
Bernard Mazoyer
Keyword(s):  
Performance Evaluation ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Brain Segmentation

scholarly journals Factorisation-Based Image Labelling

2022 ◽  
Vol 15 ◽  
Author(s):  
Yu Yan ◽  
Yaël Balbastre ◽  
Mikael Brudfors ◽  
John Ashburner
Keyword(s):  
Magnetic Resonance ◽  
Latent Variables ◽  
State Of The Art ◽  
General Purpose ◽  
Magnetic Resonance Images ◽  
Grand Challenge ◽  
Manual Annotation ◽  
Brain Segmentation ◽  
Proposed Model ◽  
Using Data

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Marmoset brain segmentation from deconvolved magnetic resonance images and estimated label maps

2021 ◽  
Author(s):  
Farah Bazzi ◽  
Muriel Mescam ◽  
Ahmad Diab ◽  
Omar Falou ◽  
Hassan Amoud ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Brain Segmentation

An emerging technology: Nuclear magnetic resonance microscopy

1988 ◽  
Vol 46 ◽  
pp. 1000-1001
Author(s):  
M.J. Hennessy ◽  
E. Kwok
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Relaxation Times ◽  
Basic Research ◽  
Local Environment ◽  
Magnetic Resonance Images ◽  
Nmr Microscopy ◽  
Mri Scans ◽  
Temperature Relaxation ◽  
Nuclear Magnetic

Much progress in nuclear magnetic resonance microscope has been made in the last few years as a result of improved instrumentation and techniques being made available through basic research in magnetic resonance imaging (MRI) technologies for medicine. Nuclear magnetic resonance (NMR) was first observed in the hydrogen nucleus in water by Bloch, Purcell and Pound over 40 years ago. Today, in medicine, virtually all commercial MRI scans are made of water bound in tissue. This is also true for NMR microscopy, which has focussed mainly on biological applications. The reason water is the favored molecule for NMR is because water is,the most abundant molecule in biology. It is also the most NMR sensitive having the largest nuclear magnetic moment and having reasonable room temperature relaxation times (from 10 ms to 3 sec). The contrast seen in magnetic resonance images is due mostly to distribution of water relaxation times in sample which are extremely sensitive to the local environment.

Functional information from magnetic resonance imaging

1995 ◽  
Vol 53 ◽  
pp. 84-85
Author(s):  
Alan P. Koretsky ◽  
Afonso Costa e Silva ◽  
Yi-Jen Lin
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Cancer Biology ◽  
Imaging Modality ◽  
Magnetic Resonance Images ◽  
Great Success ◽  
Functional Information ◽  
Resonance Imaging ◽  
High Resolution Mri

Magnetic resonance imaging (MRI) has become established as an important imaging modality for the clinical management of disease. This is primarily due to the great tissue contrast inherent in magnetic resonance images of normal and diseased organs. Due to the wide availability of high field magnets and the ability to generate large and rapidly switched magnetic field gradients there is growing interest in applying high resolution MRI to obtain microscopic information. This symposium on MRI microscopy highlights new developments that are leading to increased resolution. The application of high resolution MRI to significant problems in developmental biology and cancer biology will illustrate the potential of these techniques.In combination with a growing interest in obtaining high resolution MRI there is also a growing interest in obtaining functional information from MRI. The great success of MRI in clinical applications is due to the inherent contrast obtained from different tissues leading to anatomical information.

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