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Multi-Exponential Relaxation Times Maps Reconstruction and Unsupervised Classification in Magnitude Magnetic Resonance Imaging

Abstract : In clinical and biological applications of T2 relaxom-etry, a multi-exponential decay model proved to be representative of the relaxation signal inside each voxel of the MRI images. However, estimating and exploiting the model parameters for magnitude data is a large-scale ill-posed inverse problem. This paper presents a parameter estimation method that combines a spatial regularization with a Maximum-Likelihood criterion based on the Rician distribution of the noise. In order to properly carry out the estimation on the image level, a Majorization-Minimization approach is implemented alongside an adapted non-linear least-squares algorithm. We propose a method for exploiting the reconstructed maps by clustering the parameters using a K-means classification algorithm applied to the extracted relaxation time and amplitude maps. The method is illustrated on real MRI data of food sample analysis.
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https://hal.archives-ouvertes.fr/hal-02318133
Contributor : Guylaine Collewet Connect in order to contact the contributor
Submitted on : Wednesday, October 16, 2019 - 4:22:10 PM
Last modification on : Thursday, November 17, 2022 - 4:48:10 PM
Long-term archiving on: : Friday, January 17, 2020 - 4:47:48 PM

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  • HAL Id : hal-02318133, version 1
  • IRSTEA : PUB00063451

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Christian El Hajj, Saïd Moussaoui, Guylaine Collewet, Maja Musse. Multi-Exponential Relaxation Times Maps Reconstruction and Unsupervised Classification in Magnitude Magnetic Resonance Imaging. 27th European Signal Processing Conference, Sep 2019, La Corogne, Spain. ⟨hal-02318133⟩

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