A Large-Scale Optimization Method Using a Sparse Approximation of the Hessian for Magnetic Resonance Fingerprinting

DOI10.1137/16M1095032OpenAlexW2737548198MaRDI QIDQ4602176

Gerd Wübbeler, Clemens Elster

Publication date: 26 January 2018

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/16m1095032

zbMATH Keywords

large-scale optimizationtrust region methodMRImagnetic resonance fingerprinting

Mathematics Subject Classification ID

General nonlinear regression (62J02) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Inverse problems in linear algebra (15A29)

Related Items (1)

Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nonmonotonic trust region algorithm
• Non-monotone trust-region algorithms for nonlinear optimization subject to convex constraints
• Global Convergence of a Class of Trust Region Algorithms for Optimization with Simple Bounds
• Separable nonlinear least squares: the variable projection method and its applications
• The Variable Selection Problem
• Accelerating the Nonuniform Fast Fourier Transform
• 10.1162/153244303322753616
• A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
• A Statistical Model for Positron Emission Tomography
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