ALMA: a mathematics-driven approach for determining tuning parameters in generalized LASSO problems, with applications to MRI

arXiv:2406.19239v1 Announce Type: cross
Abstract: Magnetic Resonance Imaging (MRI) is a powerful technique employed for non-invasive in vivo visualization of internal structures. Sparsity is often deployed to accelerate the signal acquisition or overcome the presence of motion artifacts, improving the quality of image reconstruction. Image reconstruction algorithms use TV-regularized LASSO (Total Variation-regularized LASSO) to retrieve the missing information of undersampled signals, by cleaning the data of noise and while optimizing sparsity. A tuning parameter moderates the balance between these two …

abstract acquisition applications artifacts arxiv cs.cv eess.iv eess.sp generalized image imaging improving lasso mathematics mri parameters physics.med-ph quality signal sparsity tuning type visualization