Infimal convolution of total generalized variation functionals for dynamic MRI

Matthias Schlögl, Martin Holler, Andreas Schwarzl, Kristian Bredies, Rudolf Stollberger

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)142–155
JournalMagnetic Resonance in Medicine
Volume78
Issue number1
DOIs
Publication statusPublished - 2017

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Computer-Assisted Image Processing
Perfusion
Magnetic Resonance Imaging
Datasets

Keywords

  • CMR
  • Dynamic magnetic resonance imaging
  • Infimal convolution
  • Perfusion imaging
  • Total generalized variation
  • Variational models

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fields of Expertise

  • Human- & Biotechnology

Cooperations

  • BioTechMed-Graz

Cite this

Infimal convolution of total generalized variation functionals for dynamic MRI. / Schlögl, Matthias; Holler, Martin; Schwarzl, Andreas; Bredies, Kristian; Stollberger, Rudolf.

In: Magnetic Resonance in Medicine, Vol. 78, No. 1, 2017, p. 142–155 .

Research output: Contribution to journalArticleResearchpeer-review

Schlögl, Matthias ; Holler, Martin ; Schwarzl, Andreas ; Bredies, Kristian ; Stollberger, Rudolf. / Infimal convolution of total generalized variation functionals for dynamic MRI. In: Magnetic Resonance in Medicine. 2017 ; Vol. 78, No. 1. pp. 142–155 .
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