Validating Nonlinear Registration to Improve Subtraction Images for Lesion Detection and Quantification in Multiple Sclerosis

2017 ◽  
Vol 28 (1) ◽  
pp. 70-78 ◽  
Author(s):  
Vikas Kotari ◽  
Racha Salha ◽  
Dana Wang ◽  
Emily Wood ◽  
Marco Salvetti ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Lesion Detection ◽  
Detection And Quantification ◽  
Nonlinear Registration

scholarly journals Ultra-high-field 7-T MRI in multiple sclerosis and other demyelinating diseases: from pathology to clinical practice

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicolo’ Bruschi ◽  
Giacomo Boffa ◽  
Matilde Inglese
Keyword(s):  
Multiple Sclerosis ◽  
High Field ◽  
Neurological Diseases ◽  
Lesion Detection ◽  
Demyelinating Diseases ◽  
Metabolic Imaging ◽  
Central Vein ◽  
Ultra High Field ◽  
Functional Features

Abstract Magnetic resonance imaging (MRI) is essential for the early diagnosis of multiple sclerosis (MS), for investigating the disease pathophysiology, and for discriminating MS from other neurological diseases. Ultra-high-field strength (7-T) MRI provides a new tool for studying MS and other demyelinating diseases both in research and in clinical settings. We present an overview of 7-T MRI application in MS focusing on increased sensitivity and specificity for lesion detection and characterisation in the brain and spinal cord, central vein sign identification, and leptomeningeal enhancement detection. We also discuss the role of 7-T MRI in improving our understanding of MS pathophysiology with the aid of metabolic imaging. In addition, we present 7-T MRI applications in other demyelinating diseases. 7-T MRI allows better detection of the anatomical, pathological, and functional features of MS, thus improving our understanding of MS pathology in vivo. 7-T MRI also represents a potential tool for earlier and more accurate diagnosis.

Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

2014 ◽  
Vol 115 (3) ◽  
pp. 147-161 ◽  
Author(s):  
Mariano Cabezas ◽  
Arnau Oliver ◽  
Eloy Roura ◽  
Jordi Freixenet ◽  
Joan C. Vilanova ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Mri ◽  
Lesion Detection ◽  
Multiple Sclerosis Lesion

scholarly journals Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors

2006 ◽  
Vol 2006 ◽  
pp. 1-10 ◽  
Author(s):  
Jinyi Qi
Keyword(s):  
Region Of Interest ◽  
Lesion Detection ◽  
Emission Tomography ◽  
Reconstruction Methods ◽  
Positron Emission ◽  
Hotelling Observer ◽  
Sharp Edges ◽  
Gaussian Prior ◽  
Non Gaussian ◽  
Detection And Quantification

Statistical image reconstruction methods based on maximum a posteriori (MAP) principle have been developed for emission tomography. The prior distribution of the unknown image plays an important role in MAP reconstruction. The most commonly used prior are Gaussian priors, whose logarithm has a quadratic form. Gaussian priors are relatively easy to analyze. It has been shown that the effect of a Gaussian prior can be approximated by linear filtering a maximum likelihood (ML) reconstruction. As a result, sharp edges in reconstructed images are not preserved. To preserve sharp transitions, non-Gaussian priors have been proposed. However, their effect on clinical tasks is less obvious. In this paper, we compare MAP reconstruction with Gaussian and non-Gaussian priors for lesion detection and region of interest quantification using computer simulation. We evaluate three representative priors: Gaussian prior, Huber prior, and Geman-McClure prior. We simulate imaging a prostate tumor using positron emission tomography (PET). The detectability of a known tumor in either a fixed background or a random background is measured using a channelized Hotelling observer. The bias-variance tradeoff curves are calculated for quantification of the total tumor activity. The results show that for the detection and quantification tasks, the Gaussian prior is as effective as non-Gaussian priors.

Clinical application of multi-contrast 7-T MR imaging in multiple sclerosis: increased lesion detection compared to 3 T confined to grey matter

2012 ◽  
Vol 23 (2) ◽  
pp. 528-540 ◽  
Author(s):  
Wolter L. de Graaf ◽  
Iris D. Kilsdonk ◽  
Alexandra Lopez-Soriano ◽  
Jaco J. M. Zwanenburg ◽  
Fredy Visser ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Mr Imaging ◽  
Clinical Application ◽  
Grey Matter ◽  
Lesion Detection

scholarly journals Komparacija detektabilnosti mozdanih lezija u multipleks sklerozi pri pregledu magnetnom rezonancijom jacine magnetnog polja 1.0 i 3.0 Tesla

2007 ◽  
Vol 54 (3) ◽  
pp. 115-117 ◽  
Author(s):  
T.L. Stosic-Opincal ◽  
M. Gavrilov ◽  
S. Lavrnic ◽  
R. Milenkovic ◽  
V. Peric ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Spin Echo ◽  
Relative Sensitivity ◽  
Brain Lesions ◽  
Lesion Detection ◽  
Focal Lesion ◽  
Gadolinium Contrast ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery ◽  
3.0 T

To estimate the relative sensitivity of MR examination for brain lesions in multiple sclerosis at 1.0 Tesla (T) and 3.0 T using identical acquisition conditions. 54 patients with multiple sclerosis were examined both at 1.0T (Siemens Impact Expert) and 3.0T (Philips Intera) using T1-weighted spin echo (T1W-SE) with and without gadolinium contrast injections, T2W SE and fluid attenuated inversion recovery (FLAIR) imaging. Images were examined independently by three experienced neuroradiologists using focal lesion counting. 3.0T scans compared with 1.0T scans demonstrate a 27.3%, increase in the number of detected contrast enhanced lesions and an 22.7% increase in the number of detected lesions on FLAIR MR tomograms. High field 3.0T MR imaging demonstrates better sensitivity in the detection of focal brain lesions in multiple sclerosis. This improvement is more apparent in contrast enhanced lesion detection and less noticeable in FLAIR detected lesions.

scholarly journals A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis

2020 ◽  
Vol 25 ◽  
pp. 102149 ◽  
Author(s):  
Mostafa Salem ◽  
Sergi Valverde ◽  
Mariano Cabezas ◽  
Deborah Pareto ◽  
Arnau Oliver ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Convolutional Neural Network ◽  
Lesion Detection

scholarly journals Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Oren Freifeld ◽  
Hayit Greenspan ◽  
Jacob Goldberger
Keyword(s):  
Multiple Sclerosis ◽  
Brain Mri ◽  
Gaussian Mixture ◽  
Lesion Detection ◽  
Multiple Sclerosis Lesion ◽  
Curve Evolution ◽  
Brain Images ◽  
Global Parameter ◽  
Mri Brain ◽  
Mri Brain Images

This paper focuses on the detection and segmentation of Multiple Sclerosis (MS) lesions in magnetic resonance (MRI) brain images. To capture the complex tissue spatial layout, a probabilistic model termed Constrained Gaussian Mixture Model (CGMM) is proposed based on a mixture of multiple spatially oriented Gaussians per tissue. The intensity of a tissue is considered a global parameter and is constrained, by a parameter-tying scheme, to be the same value for the entire set of Gaussians that are related to the same tissue. MS lesions are identified as outlier Gaussian components and are grouped to form a new class in addition to the healthy tissue classes. A probability-based curve evolution technique is used to refine the delineation of lesion boundaries. The proposed CGMM-CE algorithm is used to segment 3D MRI brain images with an arbitrary number of channels. The CGMM-CE algorithm is automated and does not require an atlas for initialization or parameter learning. Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data.

Sign in / Sign up

Export Citation Format

Share Document