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- W4372270070 abstract "One of the most promising solutions for uncertainty quantification in high-dimensional statistics is the debiased LASSO that relies on unconstrained ℓ <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</inf> -minimization. The initial works focused on real Gaussian designs as a toy model for this problem. However, in medical imaging applications, such as compressive sensing for MRI, the measurement system is represented by a (subsampled) complex Fourier matrix. The purpose of this work is to extend the method to the MRI case in order to construct confidence intervals for each pixel of an MR image. We show that a sufficient amount of data is $n gtrsim max left{ {{s_0}{{log }^2}{s_0}log p,{s_0}{{log }^2}p} right}$." @default.
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- W4372270070 date "2023-06-04" @default.
- W4372270070 modified "2023-10-14" @default.
- W4372270070 title "High-Dimensional Confidence Regions in Sparse MRI" @default.
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- W4372270070 doi "https://doi.org/10.1109/icassp49357.2023.10096320" @default.
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