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Enhancing fMRI Reconstruction by Means of the ICBTV-Regularisation Combined with Suitable Subsampling Strategies and Temporal Smoothing

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  • UserEva-Maria Brinkmann (Westfalische Wilhelms-Universitat Munster; Westfalische Wilhelms-Universitat Munster)
  • ClockMonday 30 October 2017, 15:40-16:00
  • HouseSeminar Room 1, Newton Institute.

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VMVW02 - Generative models, parameter learning and sparsity

Based on the magnetic resonance imaging (MRI) technology, fMRI is a noninvasive functional neuroimaging method, which provides maps of the brain at different time steps, thus depicting brain activity by detecting changes in the blood flow and hence constituting an important tool in brain research.
An fMRI screening typically consists of three stages: At first, there is a short low-resolution prescan to ensure the proper positioning of the proband or patient. Secondly, an anatomical high resolution MRI scan is executed and finally the actual fMRI scan is taking place, where a series of data is acquired via fast MRI scans at consecutive time steps thus illustrating the brain activity after a stimulus. In order to achieve an adequate temporal resolution in the fMRI data series, usually only a specific portion of the entire k-space is sampled.
Based on the assumption that the full high-resolution MR image and the fast acquired actual fMRI frames share a similar edge set (and hence the sparsity pattern with respect to the gradient), we propose to use the Infimal Convolution of Bregman Distances of the TV functional (ICBTV), first introduced in \cite{Moeller_et_al}, to enhance the quality of the reconstructed fMRI data by using the full high-resolution MRI scan as a prior. Since in fMRI the hemodynamic response is commonly modelled by a smooth function, we moreover discuss the effect of suitable subsampling strategies in combination with temporal regularisation.

This is joint work with Julian Rasch, Martin Burger (both WWU M ünster) and with Ville Kolehmainen (University of Eastern Finland).

[1] {Moeller_et_al} M. Moeller, E.-M. Brinkmann, M. Burger, and T. Seybold: Color Bregman TV. SIAM J . Imag. Sci. 7(4) (2014), pp. 2771-2806.

This talk is part of the Isaac Newton Institute Seminar Series series.

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