Estimation of Signal and Noise Parameters from MR Data

Dekker, Arnold J. den; Sijbers, J.

doi:10.1201/9781420028669.pt2

Cited by 8 publications

(10 citation statements)

References 37 publications

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“…When A n =0 as in the nulled myocardium, the Rician distribution collapses in a Rayleigh distribution, defined by the number of coils n and the noise variance 2 . The value can be estimated from the image background as = 1.44 BK [3,4]. However, measurements on DE-CMR images of the 15 healthy subjects involved in the study revealed that a simple Rician distribution could not fully explain the detected signal distribution.…”

Section: Image Modelmentioning

confidence: 99%

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Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Positano

Brotini

Aquaro

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Cardiovascular magnetic resonance is able to detect myocardial fibrosis by delayed enhancement of a contrast media. However, detection and quantification of fibrosis is difficult due to the complex pattern of the fibrotic tissue signal. In this study a software model of the signal distribution in normal and fibrotic myocardium was inferred from MR images of healthy subjects and patients with hypertrophic cardiomyopathy. The developed model allowed to define a methodology for the discrimination of fibrotic areas. The method was based on the fitting of the signal histogram with a modified gamma function. The scale parameter characterizing the gamma function was used as discriminating factor in MR image analysis, reaching a sensitivity of 85% and a specificity of 86%. The proposed approach outperformed the standard approach used in the clinical practice.

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Section: Image Modelmentioning

confidence: 99%

“…Hence, the only source of signal in normal myocardium should be Rician distributed noise [3]. In particular, if phased array coils are used, the noise distribution is described by [4]:…”

Section: Image Modelmentioning

confidence: 99%

Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Positano

Brotini

Aquaro

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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“…Effectively, the CRLB is a function for the curvature of the cost function, given by . The CRLB-inequality yields [19] …”

Section: Cramér-rao Analysis For Model Selection and Optimizingmentioning

confidence: 99%

“…An incorrect representation of the noise properties, particularly assuming a Gaussian instead of a Rician noise distribution in the DWIs, may also render an inappropriate (i.e., biased) signal model [19]. A single tensor model was extended by estimating the Rician noise level in a maximum likelihood framework [20], [21].…”

Section: A Diffusion Modelingmentioning

confidence: 99%

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Estimation of Diffusion Properties in Crossing Fiber Bundles

Caan

Khedoe

Poot

et al. 2010

IEEE Trans. Med. Imaging

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Abstract-There is an ongoing debate on how to model diffusivity in fiber crossings. We propose an optimization framework for the selection of a dual tensor model and the set of diffusion weighting parameters b, such that both the diffusion shape and orientation parameters can be precisely as well as accurately estimated. For that, we have adopted the Cramér-Rao lower bound (CRLB) on the variance of the model parameters, and performed Monte Carlo simulations. We have found that the axial diffusion needs to be constrained, while an isotropic fraction can be modeled by a single parameter iso . Under these circumstances, the Fractional Anisotropy (FA) of both tensors can theoretically be independently estimated with a precision of 9% (at SNR = 25).

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Standardized T₂* map of normal human heart in vivo to correct T₂* segmental artefacts

et al. 2007

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A segmental, multislice, multi-echo T 2 Ã MRI approach could be useful in heart iron-overloaded patients to account for heterogeneous iron distribution, demonstrated by histological studies. However, segmental T 2 Ã assessment in heart can be affected by the presence of geometrical and susceptibility artefacts, which can act on different segments in different ways. The aim of this study was to assess T 2 Ã value distribution in the left ventricle and to develop a correction procedure to compensate for artefactual variations in segmental analysis. MRI was performed in four groups of 22 subjects each: healthy subjects (I), controls (II) (thalassemia intermedia patients without iron overload), thalassemia major patients with mild (III) and heavy (IV) iron overload. Three short-axis views (basal, median, and apical) of the left ventricle were obtained and analyzed using custom-written, previously validated software. The myocardium was automatically segmented into a 16-segment standardized heart model, and the mean T 2 Ã value for each segment was calculated. Punctual distribution of T 2 Ã over the myocardium was assessed, and T 2 Ã inhomogeneity maps for the three slices were obtained. In group I, no significant variation in the mean T 2 Ã among slices was found. T 2 Ã showed a characteristic circumferential variation in all three slices. The effect of susceptibility differences induced by cardiac veins was evident, together with low-scale variations induced by geometrical artefacts. Using the mean segmental deviations as correction factors, an artefact correction map was developed and used to normalize segmental data. The correction procedure was validated on group II. Group IV showed no significant presence of segmental artefacts, confirming the hypothesis that susceptibility artefacts are additive in nature and become negligible for high levels of iron overload. Group III showed a greater variability with respect to normal subjects. The correction map failed to compensate for these variations if both additive and percentage-based corrections were applied. This may reinforce the hypothesis that true inhomogeneity in iron deposition exists.

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Estimation of Signal and Noise Parameters from MR Data

Cited by 8 publications

References 37 publications

Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Estimation of Diffusion Properties in Crossing Fiber Bundles

Standardized T₂* map of normal human heart in vivo to correct T₂* segmental artefacts

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Estimation of Signal and Noise Parameters from MR Data

Cited by 8 publications

References 37 publications

Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Estimation of Diffusion Properties in Crossing Fiber Bundles

Standardized T2* map of normal human heart in vivo to correct T2* segmental artefacts

Contact Info

Product

Resources

About

Standardized T₂* map of normal human heart in vivo to correct T₂* segmental artefacts