High-Dimensional Confidence Regions in Sparse MRI
IEEE International Conference on Acoustics Speech and Signal Processing, pp. 1–5
Abstract
One of the most promising solutions for uncertainty quantification in high-dimensional statistics is the debiased LASSO that relies on unconstrained ℓ1-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 $n \gtrsim \max \left\{ {{s_0}{{\log }^2}{s_0}\log p,{s_0}{{\log }^2}p} \right\}$.
Authors 5
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Affiliation as printed
RWTH Aachen University,Department of Mathematics,Aachen,Germany
Department of Mathematics, RWTH Aachen University, Aachen, Germany
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Technical University of Munich
Affiliation as printed
Technical University of Munich,Department of Mathematics,Munich,Germany
Department of Mathematics, Technical University of Munich, Munich, Germany
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Technical University of Munich
Affiliation as printed
Technical University of Munich,Department of Mathematics,Munich,Germany
Chair of Theoretical Information Technology, Technical University of Munich, Munich, Germany
Department of Mathematics, Technical University of Munich, Munich, Germany
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Technische Hochschule Ingolstadt · Technical University of Munich
Affiliation as printed
Technische Hochschule Ingolstadt,AImotion Bavaria,Ingolstadt,Germany
AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
Department of Physics, Technical University of Munich, Garching, Germany
GE Healthcare, Munich, Germany
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Affiliation as printed
RWTH Aachen University,Department of Mathematics,Aachen,Germany
Department of Mathematics, RWTH Aachen University, Aachen, Germany
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