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Spatially Regularized Multi-Exponential Transverse Relaxation Times Estimation From Magnitude Magnetic Resonance Images Under Rician Noise

Abstract : The extraction of multi-exponential decay parameters from multi-temporal images corrupted with Rician noise and with limited time samples proves to be a challenging problem frequently encountered in clinical and food MRI studies. This work aims at proposing a method for the estimation of multi-exponential transverse relaxation times from noisy magnitude MRI images. A spatially regularized Maximum-Likelihood estimator accounting for the Rician distribution of the noise is introduced. To deal with the large-scale optimization problem , a Majoration-Minimization approach coupled with an adapted non-linear least squares algorithm is implemented. The proposed algorithm is numerically fast, stable and leads to accurate results. Its effectiveness is illustrated by an application to a simulated phantom and to magnitude multi spin echo MRI images acquired from a tomato sample.
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https://hal.archives-ouvertes.fr/hal-02317863
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Submitted on : Wednesday, October 16, 2019 - 2:05:10 PM
Last modification on : Wednesday, September 28, 2022 - 3:13:42 PM
Long-term archiving on: : Friday, January 17, 2020 - 3:59:50 PM

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  • HAL Id : hal-02317863, version 1
  • IRSTEA : PUB00063456
  • WOS : 000521828601054

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Christian El Hajj, Saïd Moussaoui, Guylaine Collewet, Maja Musse. Spatially Regularized Multi-Exponential Transverse Relaxation Times Estimation From Magnitude Magnetic Resonance Images Under Rician Noise. 26th IEEE International Conference on Image Processing, Sep 2019, Tapei, Taiwan. ⟨hal-02317863⟩

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