Multi-exponential analysis of magnitude MR images using a quantitative multispectral edge-preserving filter

Bonny, J.-M.; Boespflug-Tanguly, Odile; Zanca, Michel; Renou, Jean-Pierre

doi:10.1016/s1090-7807(02)00107-6

Cited by 15 publications

(14 citation statements)

References 39 publications

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“…Within each set, decay curves were generated with SNR values logarithmically spaced between 100 and 3200, where SNR is defined as the magnitude of the first echo divided by the standard deviation of the Gaussian noise. For a given SNR, 2000 decay curves (each with a unique noise realization) were generated and the bias introduced by the magnitude operation, which converts the underlying Gaussian noise to Rician, was removed (see Data Analysis for more details) (13). MET 2 analysis was then performed to determine the distribution of T 2 s (i.e., the T 2 spectrum) in the simulated data.…”

Section: Methodsmentioning

confidence: 99%

Evidence of multiexponential T₂ in rat glioblastoma

et al. 2009

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The aim of this study was to characterize multiexponential T2 (MET2) relaxation in a rat C6 glioblastoma tumor model. To do this, rats (n = 11) were inoculated with the C6 cells via stereotaxic injection into the brain. Ten days later, MET2 measurements were performed in vivo using a single-slice, multi-echo spin-echo sequence at 7.0 T. Tumor signal was biexponential in eight animals with a short-lived T2 component (T2 = 20.7 ± 5.4 ms across samples) representing 6.8 ± 6.2 % of the total signal and a long-lived T2 component (T2 = 76.4 ± 9.3 ms) representing the remaining signal fraction. In contrast, signal from contralateral grey matter was consistently monoexponential (T2 = 48.8 ± 2.3 ms). Additional ex vivo studies (n = 3) and Monte Carlo simulations showed that the in vivo results were not significantly corrupted by partial volume averaging or noise. The underlying physiological origin of the observed MET2 components is unknown; however, MET2 analysis may hold promise as a non-invasive tool for characterizing tumor microenvironment in vivo on a sub-voxel scale.

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Section: Methodsmentioning

confidence: 99%

Evidence of multiexponential T₂ in rat glioblastoma

et al. 2009

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“…The linear filter only takes into consideration the relative position in kernel, and remains constant throughout the whole image filtering. Nonlinear filters are relative to the target pixel and the coefficients are calculated as a function of local variations of the signal (Bonnya et al, 2003). For example, in the linear filter class, average and Gaussian filers are often used.…”

Section: Image Filtermentioning

confidence: 99%

Adipose Measurement Using Micro MRI

Tang¹,

Moats²

2012

Advanced Image Acquisition, Processing Techniques and Applications I

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“…In this case errors up to 30% were also added in the initial guess, resulting a T 2 time distribution retrieval with two peaks, although, in some case, negative values can also be presented. To avoid these negative values, an appropriated filter can be used while treating with experimental data [26,27].…”

Section: Inversion Of Experimental Datamentioning

confidence: 99%