Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients

Commowick, Olivier; Fillard, Pierre; Clatz, Olivier; Warfield, Simon K.

doi:10.1007/978-3-540-85988-8_116

Cited by 28 publications

(36 citation statements)

References 14 publications

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“…The smaller the sizes, the closer the method gets to [5]. On the contrary, large sizes tend to increase the number of false negatives.…”

Section: Resultsmentioning

confidence: 99%

“…Fig. 1 shows the average Dice score results of our method (M 2 ) and the one proposed in [5] (M 1 ).…”

Section: Experiments On Simulated Datamentioning

confidence: 99%

“…Then, a DTI atlas is computed from the control subjects DTI using Guimond's et al atlas construction method [7], combined to a non linear tensor-based registration algorithm. Each DTI patient image is then registered onto the atlas and compared voxel by voxel to the database of controls either with the method proposed in [5] M 1 or the proposed method M 2 . We present in Fig.…”

Section: Experiments On Multiple Sclerosis Datamentioning

confidence: 99%

“…However, utilizing only a scalar value may discard a large part of the tensor information and decrease the precision of the comparison. To overcome this problem, several groups have proposed methods to utilize the full tensor either for population comparison (Lepore et al utilized the Hotelling's T 2 test on tensors for HIV patients [3], Whitcher et al [4] the Cramers test on tensors), or for patient to group comparison (Commowick et al [5]). These works have demonstrated that a test based on the full tensor information yields more precise comparisons.…”

Section: Introductionmentioning

confidence: 99%

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Non-local Robust Detection of DTI White Matter Differences with Small Databases

Commowick

Stamm

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Diffusion imaging, through the study of water diffusion, allows for the characterization of brain white matter, both at the population and individual level. In recent years, it has been employed to detect brain abnormalities in patients suffering from a disease, e.g. from multiple sclerosis (MS). State-of-the-art methods usually utilize a database of matched (age, sex, ...) controls, registered onto a template, to test for differences in the patient white matter. Such approaches however suffer from two main drawbacks. First, registration algorithms are prone to local errors, thereby degrading the comparison results. Second, the database needs to be large enough to obtain reliable results. However, in medical imaging, such large databases are hardly available. In this paper, we propose a new method that addresses these two issues. It relies on the search for samples in a local neighborhood of each pixel to increase the size of the database. Then, we propose a new test based on these samples to perform a voxelwise comparison of a patient image with respect to a population of controls. We demonstrate on simulated and real MS patient data how such a framework allows for an improved detection power and a better robustness and reproducibility, even with a small database.

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“…The smaller the sizes, the closer the method gets to [5]. On the contrary, large sizes tend to increase the number of false negatives.…”

Section: Resultsmentioning

confidence: 99%

“…Fig. 1 shows the average Dice score results of our method (M 2 ) and the one proposed in [5] (M 1 ).…”

Section: Experiments On Simulated Datamentioning

confidence: 99%

Section: Experiments On Multiple Sclerosis Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-local Robust Detection of DTI White Matter Differences with Small Databases

Commowick

Stamm

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

Self Cite

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“…tensor orientation) is thus lost in the process. Recently, several groups performed full-tensor voxelwise analysis of tensor fields with LE metrics, both in the DTI context [6,17] and in the TBM context [10,5]. However, to the best of our knowledge the LE framework has not yet been applied to voxelwise regression nor to brain-wide machine learning.…”

Section: Introductionmentioning

confidence: 99%

Voxelwise Multivariate Statistics and Brain-Wide Machine Learning Using the Full Diffusion Tensor

Fouque

Fillard

Bargiacchi

et al. 2011

Lecture Notes in Computer Science

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Abstract. In this paper, we propose to use the full diffusion tensor to perform brain-wide score prediction on diffusion tensor imaging (DTI) using the log-Euclidean framework., rather than the commonly used fractional anisotropy (FA). Indeed, scalar values such as the FA do not capture all the information contained in the diffusion tensor. Additionally, full tensor information is included in every step of the pre-processing pipeline: registration, smoothing and feature selection using voxelwise multivariate regression analysis. This approach was tested on data obtained from 30 children and adolescents with autism spectrum disorder and showed some improvement over the FA-only analysis.

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Spatial mapping of translational diffusion coefficients using diffusion tensor imaging: A mathematical description

Shetty

Chiang

Maletić-Savatić

et al. 2014

Concepts Magnetic Resonance

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In this article, we discuss the theoretical background for diffusion weighted imaging and diffusion tensor imaging. Molecular diffusion is a random process involving thermal Brownian motion. In biological tissues, the underlying microstructures restrict the diffusion of water molecules, making diffusion directionally dependent. Water diffusion in tissue is mathematically characterized by the diffusion tensor, the elements of which contain information about the magnitude and direction of diffusion and is a function of the coordinate system. Thus, it is possible to generate contrast in tissue based primarily on diffusion effects. Expressing diffusion in terms of the measured diffusion coefficient (eigenvalue) in any one direction can lead to errors. Nowhere is this more evident than in white matter, due to the preferential orientation of myelin fibers. The directional dependency is removed by diagonalization of the diffusion tensor, which then yields a set of three eigenvalues and eigenvectors, representing the magnitude and direction of the three orthogonal axes of the diffusion ellipsoid, respectively. For example, the eigenvalue corresponding to the eigenvector along the long axis of the fiber corresponds qualitatively to diffusion with least restriction. Determination of the principal values of the diffusion tensor and various anisotropic indices provides structural information. We review the use of diffusion measurements using the modified Stejskal–Tanner diffusion equation. The anisotropy is analyzed by decomposing the diffusion tensor based on symmetrical properties describing the geometry of diffusion tensor. We further describe diffusion tensor properties in visualizing fiber tract organization of the human brain.

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Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients

Cited by 28 publications

References 14 publications

Non-local Robust Detection of DTI White Matter Differences with Small Databases

Non-local Robust Detection of DTI White Matter Differences with Small Databases

Voxelwise Multivariate Statistics and Brain-Wide Machine Learning Using the Full Diffusion Tensor

Spatial mapping of translational diffusion coefficients using diffusion tensor imaging: A mathematical description

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