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Infimal convolution of total generalized variation functionals for dynamic MRI
Author(s) -
Schloegl Matthias,
Holler Martin,
Schwarzl Andreas,
Bredies Kristian,
Stollberger Rudolf
Publication year - 2017
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26352
Subject(s) - regularization (linguistics) , undersampling , dynamic contrast , dynamic contrast enhanced mri , computer science , iterative reconstruction , convolution (computer science) , algorithm , dynamic imaging , compressed sensing , magnetic resonance imaging , mathematical optimization , temporal resolution , total variation denoising , mathematics , artificial intelligence , image (mathematics) , image processing , physics , artificial neural network , radiology , medicine , digital image processing , quantum mechanics
Purpose To accelerate dynamic MR applications using infimal convolution of total generalized variation functionals (ICTGV) as spatio‐temporal regularization for image reconstruction. Theory and Methods ICTGV comprises a new image prior tailored to dynamic data that achieves regularization via optimal local balancing between spatial and temporal regularity. Here it is applied for the first time to the reconstruction of dynamic MRI data. CINE and perfusion scans were investigated to study the influence of time dependent morphology and temporal contrast changes. ICTGV regularized reconstruction from subsampled MR data is formulated as a convex optimization problem. Global solutions are obtained by employing a duality based non‐smooth optimization algorithm. Results The reconstruction error remains on a low level with acceleration factors up to 16 for both CINE and dynamic contrast‐enhanced MRI data. The GPU implementation of the algorithm suites clinical demands by reducing reconstruction times of one dataset to less than 4 min. Conclusion ICTGV based dynamic magnetic resonance imaging reconstruction allows for vast undersampling and therefore enables for very high spatial and temporal resolutions, spatial coverage and reduced scan time. With the proposed distinction of model and regularization parameters it offers a new and robust method of flexible decomposition into components with different degrees of temporal regularity. Magn Reson Med 78:142–155, 2017. © 2016 International Society for Magnetic Resonance in Medicine