Spatio-Temporal Image Reconstruction for Accelerating Dynamic MRIApplications using Variational Priors

Matthias Schlögl

Research output: ThesisDoctoral Thesis


Magnetic Resonance Imaging (MRI) emerged during the second half of the
last century to one of the most important clinical device for non-invasive
imaging of the human body. By now it is not only possible to achieve in-
credible image quality with very high resolutions and different soft-tissue
contrast but also to extract a wide range of more complex information such
as diffusion, perfusion, flow, brain activation, or even metabolic information.
MRI became the leading technology for diagnostic imaging of many diseases
such as tumor, stroke, or cardiovascular diseases with the advantage of not
using ionizing radiation as in computer tomography.
For many of the more complex information made accessible with MRI, it
is necessary to encode dynamic processes, such as imaging of the beating
heart, capturing the temporal course of an injected contrast agent with
high temporal resolution or inferring on quantitative information from a
characteristic signal evolution. In order to reach and improve necessary
conditions of spatio-temporal resolution and spatial coverage the encoding
process needs to be very fast.
Today, sequence design for rapid imaging already reached limitations defined by hardware and energy deposition constrains, such that further progress is only achievable by leaving out acquisitions steps. This, however, comes at the cost of increasing the ill-posedness of the corresponding reconstruction problem that would conventionally lead to severe artifacts and noise corruption. From a mathematical view-point it is therefore necessary to employ the concept of regularisation, where strong improvements require the regularisation to be tailored specifically to the dynamic MRI reconstruction problem.
The core of this thesis is the analysis and application of modern dynamic
regularization strategies to different dynamic MRI (dMRI) modalities. Covered examples include functional cardiac imaging and cardiac perfusion imaging, dynamic contrast enhanced MRI, time-resolved angiography, and accelerated MR parameter mapping.
Original languageEnglish
QualificationDoctor of Technology
Awarding Institution
  • Graz University of Technology (90000)
  • Stollberger, Rudolf, Supervisor
  • Ücker, Martin, Supervisor, External person
Publication statusPublished - 18 Sept 2018


  • dynamic MRI
  • cardiac imaging
  • qMRI
  • time-resolved angiography
  • inverse problems
  • variational modeling
  • spatio-temporal regularization


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